System and techniques may be adapted for use in radiotherapy treatment planning. A technique may include determining a set of optimization functions with initial optimization goals, including at least one optimization function depending on LET and at least one optimization function for selecting a dose. The technique may include generating, for example using processing circuitry, a treatment plan via automated multicriteria optimization of the set of optimization functions while preserving the initial optimization goals using the patient information. In some examples, the treatment plan may be output (e.g., stored or displayed).
Systems and techniques may be used for radiotherapy. An example system may include a fixation device arranged to receive and immobilize a patient. The example system may include a first filter arranged to extend along a first portion (e.g., a spine or cranium) of the patient, the first filter attached to the fixation device at a first location, the first filter including a plurality of beam attenuating elements. The example system may include a fixed beam proton delivery system arranged to deliver a therapeutic proton radiation dose attenuated via the first filter to the first portion of the patient.
Systems (500) and methods (700) for verifying a primary dose profile generated by a radiation machine using cloud-based services are disclosed. An exemplary system (500) can include a cloud (530,600) that provides cloud-based services, and a user interface (136) that enables multi-tenant access to the cloud-based services. A file service (610) can extract from a patient (501) DICOM file image information and information about a radiation machine. A dose engine service (620) can determine a secondary radiation dose profile by applying a dose algorithm (624) to the image and the radiation machine information. The applied dose algorithm (624) can be different from the dose algorithm used by the radiation machine to generate the primary dose profile. A dose evaluation service (630) can use the secondary radiation dose profile to verify accuracy of the primary dose profile based on a consistency indicator between the primary and secondary dose profiles.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
A computer-implemented image evaluation method for a radiotherapy device, a radiotherapy device and a computer-readable medium are provided. The computer-implemented image-evaluation method comprises obtaining a time series of images of a subject disposed in the radiotherapy device. The computer-implemented image-evaluation method further comprises determining a quality factor for an image of the time series of images. The computer-implemented image-evaluation method further comprises, in response to determining that the quality factor does not meet a condition, generating a computer-executable instruction for adjusting operation of the radiotherapy device.
Methods, systems and computer-readable media for controlling a radiotherapy apparatus are disclosed. A method for controlling a radiotherapy apparatus comprises obtaining a first treatment plan comprising positioning information of a beam shaping apparatus of the radiotherapy apparatus; receiving, during delivery of a radiation therapeutic beam to a target on a patient, information including a positional shift of the target; and generating a revised treatment plan based on the first treatment plan, the generating of the revised treatment plan comprising determining an updated configuration of the beam shaping apparatus from the positioning information of the first treatment plan based on the positional shift of the target.
The present disclosure relates to systems and methods for developing radiotherapy treatment plans though the use of machine learning approaches and neural network components. A neural network is trained using one or more three-dimensional medical images, one or more three-dimensional anatomy maps, and one or more dose distributions to predict a fluence map or a dose map. During training the neural network receives a predicted dose distribution determined by the neural network that is compared to an expected dose distribution. Iteratively the comparison is performed until a predetermined threshold is achieved. The trained neural network is then utilized to provide a three-dimensional dose distribution.
G06N 3/084 - Backpropagation, e.g. using gradient descent
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
7.
COMPOSITE FIELD SEQUENCING (CFS) FOR PROTON BEAM THERAPY
System and techniques may be adapted for use in composite field sequencing for proton therapy. A technique may include generating a proton therapy plan in a treatment planning system, the proton therapy plan including a plurality of static fields. The technique may include creating a single data file of a single dynamic field representing the plurality of static fields. The single data file may be sent to a proton therapy system for delivery of the single dynamic field. The technique may include receiving a response information related to a dose delivered to a patient by the single dynamic field.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
8.
COMPARING HEALTHCARE PROVIDER CONTOURS USING AUTOMATED TOOL
Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
9.
COMPARING HEALTHCARE PROVIDER CONTOURS USING AUTOMATED TOOL
Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.
G06T 7/174 - Segmentation; Edge detection involving the use of two or more images
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Systems and methods are disclosed for performing operations comprising: receiving dose information representing dose delivered during a first radiotherapy treatment fraction; accessing one or more previous dose information representing dose delivered during one or more previous radiotherapy treatment fractions; computing a measure of biologically effective dose (BED) based on a combination of the dose information delivered during a first radiotherapy treatment fraction and the dose delivered during the one or more previous radiotherapy treatment fractions; and performing an isotoxic planning process for delivering a second radiotherapy treatment fraction following the first radiotherapy treatment fraction based on the computed measure of BED.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
11.
DYNAMIC CONTEXTUAL INTERFACES TO ELECTRONIC MEDICAL RECORDS DATABASE
A system and method for presenting a dynamic patient whiteboard may include displaying a custom user interface including the dynamic patient whiteboard. The custom user interface may display a patient list including a plurality of patients and related disease state, cancer type, or treatment protocol, in an example, The dynamic patient whiteboard may include a plurality of oncology-related tasks, and each oncology-related task of the plurality of oncology-related tasks may include a task status. An electronic medical records (EMR) database may be accessed. A processor may be used to determine whether a task status for an oncology-related task has been updated, and in response optionally update the dynamic patient whiteboard to reflect the updated task status. Context information may be displayed for the updated task or other tasks, for example based on a particular user identity.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Systems and methods may be used for protecting healthy tissue in particle therapy. For example, a method may include defining a particle arc range for a radiotherapy treatment of a patient. The method may include generating a spot selection for an arc sequence, including a trajectory for delivering the radiotherapy treatment, for example, based on a temporal dose heterogeneity parameter or a spatial dose heterogeneity parameter. The method may include optimizing fluence of the arc sequence for the radiotherapy treatment, for example, based on an applied temporal dose heterogeneity specific cost function or an applied spatial dose heterogeneity specific cost function.
Techniques are described for contouring of a region of interest based on imaging parameters of spatial imaging data and guided by user input of locations in the spatial imaging data, which may be used for segmentation or radiation treatment planning. An approach is described of combining a new paint brush tool with an edge-detection algorithm to correct for both the jagged contours and the painting routine not being executed often enough. By using an edge-detection algorithm, the user does not need to focus as much attention on moving the mouse accurately because the system will find the true organ boundary (e.g., using the image gradient) automatically, which may also lead to more time savings.
Systems and methods are disclosed for generating radiotherapy machine parameters used in a radiotherapy treatment plan, based on machine learning prediction. The systems and methods include: obtaining three-dimensional image data which indicates target dose areas and organs-at-risk areas of a subject; generating anatomy projection images from the image data, each anatomy projection image providing a view from a respective beam angle of the radiotherapy treatment; using a trained neural network model (trained with corresponding pairs of anatomy projection images and control point images) to generate control point images, each control point image indicating an intensity and aperture(s) of a control point of the radiotherapy treatment to apply at a respective beam angle; and generating a set of final control points for use in the radiotherapy treatment to control a radiotherapy treatment machine, based on optimization of the control points indicated by the generated control point images.
Systems and methods are disclosed for performing operations comprising: receiving a plurality of training images representing different phases of a periodic motion of a target region in a patient; applying a model to the plurality of training images to generate a lower-dimensional feature space representation of the plurality of training images; clustering the lower-dimensional feature space representation of the plurality of training images into a plurality of groups corresponding to the different phases of the periodic motion; and classifying a motion phase associated with a new image of the target region in the patient based on the plurality of groups of the clustered lower-dimensional feature space representation of the plurality of training images.
Systems and methods are disclosed for performing operations comprising: receiving multi-parametric input data representing data associated with a patient; receiving an indication of a disease associated with the patient; processing the multi-parametric input data to generate one or more metrics corresponding to a plurality of different modalities for treating the disease associated with the patient; selecting, based on the one or more metrics, a given modality from the plurality of different modalities to treat the disease associated with the patient; and configuring parameters of the given modality based on a portion of the multi-parametric input data.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
Systems and methods for generating a radiotherapy treatment plan using information about gantry angle-indexed dose (GAID) variation are discussed. An exemplary system can include an interface to receive a beam model for use in the radiation machine, and a processor that can determine, for the radiation machine, a GAID variation represented by a plurality of radiation doses at different gantry angles. The processor can determine a radiation treatment plan for the patient using the beam model and the GAID variation.
Systems and techniques may be used for generating an image using one or more protons. For example, a technique may include detecting, over a time period using two orthogonal two-dimensional detector arrays, a magnetic field corresponding to a proton in motion. The technique may include determining a trajectory of the proton based on the magnetic field over the period of time, and generating a two-dimensional proton image using the trajectory. The two-dimensional proton image may be output for display.
Systems and techniques may be used to generate a radiotherapy treatment plan to execute using a particle beam from a continuously rotating gantry towards a target. A technique may include identifying a target location within a tumor of a patient, providing a particle beam configured to deliver radiotherapy treatment to the tumor along a trajectory using at least two energies including a first energy and a second energy, the first energy greater than the second energy, and determining a first location along the trajectory past the target location and a second location before the target location along the trajectory. The technique may include determining a configuration for the particle beam to deliver the first energy to the first location and the second energy to the second location. In some examples, a radiotherapy treatment plan according to the configuration may be output.
Systems and techniques may be used for determining a line segment to be delivered from a particle beam towards a target. An example technique may include continuously scanning the particle beam at a constant rate from a starting point to an ending point, and determining a plurality of spots located between the starting point and the ending point. The technique may include determining a plurality of beamlets based on the plurality of spots, and determining, using an amount of dose to be delivered via each beamlet, a total amount of dose to be delivered. The technique may include generating a line segment having the starting point and the ending point, the line segment having the total amount of dose to be delivered based on the plurality of beamlets.
Systems and methods are disclosed for monitoring anatomic position of a human subject and modifying a radiotherapy treatment based on anatomic position changes, as determined with a regression model trained to estimate movement of a region of interest. Example operations for movement monitoring and therapy control include: obtaining 3D image data for a subject, which provides a reference volume and at least one defined region of interest; obtaining real-time 2D image data corresponding to the subject, captured during the radiotherapy treatment session; extracting features from the 2D image data; producing a relative motion estimation of a region of interest with a machine learning regression model, the model trained to estimate a spatial transformation from the 2D image data based on training from the reference volume; and controlling a radiotherapy beam of a radiotherapy machine used in the radiotherapy session, based on the relative motion estimation.
Systems and methods are disclosed for monitoring anatomic position of a human subject for a radiotherapy treatment session, based on use of a regression model trained to estimate movement of a region of interest based on 2D image data input. Example operations for movement estimation include: obtaining 3D image data for a subject, which provides a reference volume and at least one defined region of interest; obtaining 2D image data corresponding to the subject, captured in real time (during the radiotherapy treatment session); extracting features from the 2D image data; analyzing the extracted features with a machine learning regression model, trained to estimate a spatial transformation in the three dimensions of the reference volume; and outputting and using a relative motion estimation of the at least one region of interest, produced from the machine learning regression model, the relative motion estimation being estimated from the extracted features.
Systems and methods are disclosed for monitoring anatomic position of a human subject for a radiotherapy treatment session, and optionally modifying a radiotherapy treatment based on anatomic position changes. Example operations for movement monitoring and therapy control include: obtaining 3D image data for a subject, which provides a reference volume and at least one defined region of interest; obtaining real-time 2D image data corresponding to the subject, captured during the radiotherapy treatment session; extracting features from the 2D image data; producing a relative motion estimation of a region of interest with a machine learning regression model, the model trained to estimate a spatial transformation from the 2D image data based on training from the reference volume; and controlling a radiotherapy beam of a. radiotherapy machine used in the radiotherapy session, based on the relative motion estimation.
Techniques for generating a synthetic computed tomography (sCT) image from a cone-beam computed tomography (CBCT) image are provided. The techniques include receiving a CBCT image of a subject; generating, using a generative model, a sCT image corresponding to the CBCT image, the generative model trained based on one or more deformable offset layers in a generative adversarial network (GAN) to process the CBCT image as an input and provide the sCT image as an output; and generating a display of the sCT image for medical analysis of the subject.
G06T 5/50 - Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
Systems and methods arc disclosed for generating radio-therapy treatment machine parameters based on projection images of a target anatomy. The systems and methods include receiving an image depicting an anatomy of a subject: generating a first projection image based on the received image that represents a view of the anatomy from a first gantry angle of tire radiotherapy treatment machine; applying a machine learning model to the first projection image to estimate a first graphical aperture image representation of multi-leaf collimator (MLC) leaf positions at the first gantry angle and the radiation intensity at that angle, the machine learning model being trained to establish a relationship between projection images representing different views of a patient anatomy and respective graphical aperture image representations of the MLC leaf positions at different gantry angles corresponding to the different views: and generating radiotherapy treatment machine parameters based on the first graphical aperture image representation.
Systems and methods are disclosed for performing operations comprising: receiving first and second images depicting an anatomy of a subject; applying a trained machine learning model to a first data set associated with the first image and a second data set associated with the second image to estimate a biomechanically accurate DVF representing a mapping of pixels or voxels from the first image to the second image, the machine learning model trained to establish a relationship between a plurality of pairs of data sets associated with images of a patient anatomy and respective biomechanically accurate DVF representations of pixel or voxel mapping between the plurality of pairs of data sets; applying the estimated biomechanically accurate DVF to deform a dose from a previous treatment session.
Systems and methods are disclosed for performing operations comprising: receiving first and second images depicting an anatomy of a subject; applying a trained machine learning model to a first data set associated with the first image and a second data set associated with the second image to estimate a biomechanically accurate DVF representing a mapping of pixels or voxels from the first image to the second image, the machine learning model trained to establish a relationship between a plurality of pairs of data sets associated with images of a patient anatomy and respective biomechanically accurate DVF representations of pixel or voxel mapping between the plurality of pairs of data sets; applying the estimated biomechanically accurate DVF to deform a dose from a previous treatment session.
System and methods may be used for arc fluence optimization without iteration to arc sequence generation. A method may include defining a particle arc range for a radiotherapy treatment of a patient, and generating an arc sequence, including a set of parameters for delivering the radiotherapy treatment, without requiring a dose calculation. The method may include optimizing fluence of the arc sequence for the radiotherapy treatment without iterating back to arc sequence generation, and outputting the fluence optimized arc sequence for use in the radiotherapy treatment.
Systems and methods may be used for fluence optimization without iteration to sequence generation. For example, arc sequence generation may occur before arc fluence optimization. A method may include generating an arc sequence, including a set of parameters for delivering a radiotherapy treatment, without requiring a dose calculation, wherein the set of parameters includes an organ at risk sparing level. The method may include optimizing fluence of the arc sequence for a radiotherapy treatment without iterating back to arc sequence generation. The fluence optimized arc sequence may be output for use in the radiotherapy treatment.
System and methods may be used for arc fluence optimization without iteration to arc sequence generation. A method may include defining a particle arc range for a radiotherapy treatment of a patient, and generating an arc sequence, including a set of parameters for delivering the radiotherapy treatment, without requiring a dose calculation. The method may include optimizing fluence of the arc sequence for the radiotherapy treatment without iterating back to arc sequence generation, and outputting the fluence optimized arc sequence for use in the radiotherapy treatment.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
31.
MACHINE LEARNING OPTIMIZATION OF FLUENCE MAPS FOR RADIOTHERAPY TREATMENT
Systems and methods are disclosed for generating fluence maps for a radiotherapy treatment plan that uses machine learning prediction. The systems and methods include identifying image data that indicates treatment constraints for target dose areas and organs at risk areas in an anatomy of the subject, generating anatomy projection images that represent a view of the subject from respective beam angles, using a trained neural network model to generate the computer-simulated fluence map representations based on the anatomy projection images, where the fluence maps indicate a fluence distribution of the radiotherapy treatment at each of the beam angles.
Systems and methods are disclosed for generating fluence maps for a radiotherapy treatment plan that uses machine learning prediction. The systems and methods include identifying image data that indicates treatment constraints for target dose areas and organs at risk areas in an anatomy of the subject, generating anatomy projection images that represent a view of the subject from respective beam angles, using a trained neural network model to generate the computer-simulated fluence map representations based on the anatomy projection images, where the fluence maps indicate a fluence distribution of the radiotherapy treatment at each of the beam angles.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
Systems and methods are provided for generating a pseudo-CT prediction model that can be used to generate pseudo-CT images. An exemplary system may include a processor configured to retrieve training data including at least one MR image and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may extract a plurality of features from each image point of the at least one MR image, create a feature vector for each image point based on the extracted features, and extract a CT value from each image point of the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
A61B 90/00 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
34.
Image synthesis using adversarial networks such as for radiation therapy
A statistical learning technique that does not rely upon paired imaging information is described herein. The technique may be computer-implemented and may be used in order to train a statistical learning model to perform image synthesis, such as in support of radiation therapy treatment planning. In an example, a trained statistical learning model may include a convolutional neural network established as a generator convolutional network, and the generator may be trained at least in part using a separate convolutional neural network established as a discriminator convolutional network. The generator convolutional network and the discriminator convolutional network may form an adversarial network architecture for use during training. After training, the generator convolutional network may be provided for use in synthesis of images, such as to receive imaging data corresponding to a first imaging modality type, and to synthesize imaging data corresponding to a different, second imaging modality type.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.
Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.
Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.
Systems and techniques may be used to estimate a patient state during a radiotherapy treatment. For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to an input image using the correspondence motion model.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
Systems and methods are disclosed for generating radiotherapy treatment machine parameters based on projection images of a target anatomy. The systems and methods include operations including receiving a set of pairs of image data for each gantry angle of a radiotherapy treatment machine, wherein each pair of the set of pairs comprises a given projection image that represents a view of an anatomy of a subject from a given gantry angle and a given graphical aperture image of multi-leaf collimator (MLC) leaf positions at the given gantry angle based on the given projection image; training a generative adversarial network (GAN) model based on the set of pairs of image data for each gantry angle; and using the trained GAN model to predict an aperture image of MLC leaf positions for a desired gantry angle based on a projection image that represents a view of an anatomical region of interest.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
Systems and methods are disclosed for generating radiotherapy treatment machine parameters based on projection images of a target anatomy. The systems and methods include operations including receiving a set of pairs of image data for each gantry angle of a radiotherapy treatment machine, wherein each pair of the set of pairs comprises a given projection image that represents a view of an anatomy of a subject from a given gantry angle and a given graphical aperture image of multi-leaf collimator (MLC) leaf positions at the given gantry angle based on the given projection image; training a generative adversarial network (GAN) model based on the set of pairs of image data for each gantry angle; and using the trained GAN model to predict an aperture image of MLC leaf positions for a desired gantry angle based on a projection image that represents a view of an anatomical region of interest.
Systems and methods are disclosed for generating radiotherapy treatment machine parameters based on projection images of a target anatomy. The systems and methods include operations including receiving a set of pairs of image data for each gantry angle of a radiotherapy treatment machine, wherein each pair of the set of pairs comprises a given projection image that represents a view of an anatomy of a subject from a given gantry angle and a given graphical aperture image of multi-leaf collimator (MLC) leaf positions at the given gantry angle based on the given projection image; training a generative adversarial network (GAN) model based on the set of pairs of image data for each gantry angle; and using the trained GAN model to predict an aperture image of MLC leaf positions for a desired gantry angle based on a projection image that represents a view of an anatomical region of interest.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
Systems and methods for generating a radiotherapy treatment plan using information about gantry angle-indexed dose (GAID) variation are discussed. An exemplary system can include an interface to receive a beam model for use in the radiation machine, and a processor that can determine, for the radiation machine, a GAID variation represented by a plurality of radiation doses at different gantry angles. The processor can determine a radiation treatment plan for the patient using the beam model and the GAID variation.
Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.
Systems and techniques may be used to estimate a relative motion of patient anatomy using a deep learning network during a radiotherapy treatment. For example, a method may include using a first deep neural network to relate input real-time partial patient measurements and a patient model including a reference volume to output patient states. The method may include using a second deep neural network to relate the patient states and the reference volume to relative motion information between the patient states and the reference volume. The deep neural networks may be used in real time to estimate a relative motion corresponding to an input image.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06N 3/04 - Architecture, e.g. interconnection topology
Embodiments disclose a method and system for segmenting medical images. In certain embodiments, the system comprises a database configured to store a plurality of medical images acquired by an image acquisition device. The plurality of images include at least one first medical image of an object, and a second medical image of the object, each first medical image associated with a first structure label map. The system further comprises a processor that is configured to register the at least one first medical image to the second medical image, determine a classifier model using the registered first medical image and the corresponding first structure label map, and determine a second structure label map associated with the second medical image using the classifier model.
Systems, computer-implemented methods, and computer readable media for generating a synthetic image of an anatomical portion based on an origin image of the anatomical portion acquired by an imaging device using a first imaging modality are disclosed. These systems may be configured to receive the origin image of the anatomical portion acquired by the imaging device using the first imaging modality, receive a convolutional neural network model trained for predicting the synthetic image based on the origin image, and convert the origin image to the synthetic image through the convolutional neural network model. The synthetic image may resemble an imaging of the anatomical portion using a second imaging modality differing from the first imaging modality.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
Systems and techniques may be used to estimate a real-time patient state during a radiotherapy treatment using a magnetic resonance linear accelerator (MR-Linac). For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to a 2D MR image using the correspondence motion model. The method may include directing radiation therapy, using the MR-Linac, to a target according to the patient state.
Systems and methods are disclosed for generating radiotherapy treatment machine parameters based on projection images of a target anatomy. The systems and methods include receiving an image depicting an anatomy of a subject; generating a first projection image based on the received image that represents a view of the anatomy from a first gantry angle of the radiotherapy treatment machine; applying a machine learning model to the first projection image to estimate a first graphical aperture image representation of multi-leaf collimator (MLC) leaf positions at the first gantry angle and the radiation intensity at that angle, the machine learning model being trained to establish a relationship between projection images representing different views of a patient anatomy and respective graphical aperture image representations of the MLC leaf positions at different gantry angles corresponding to the different views; and generating radiotherapy treatment machine parameters based on the first graphical aperture image representation.
Techniques for generating a synthetic computed tomography (sCT) image from a cone-beam computed tomography (CBCT) image are provided. The techniques include receiving a CBCT image of a subject; generating, using a generative model, a sCT image corresponding to the CBCT image, the generative model trained based on one or more deformable offset layers in a generative adversarial network (GAN) to process the CBCT image as an input and provide the sCT image as an output; and generating a display of the sCT image for medical analysis of the subject.
Embodiments of the disclosure may be directed to an image processing system configured to receive a medical image of a region of a subject's body taken at a first time and to receive a surface image of an exterior portion of the region of the subject's body taken at the first time. The image processing may also be configured to receive a medical image of the region of the subject's body taken at a second time and to register the medical image taken at the first time, the surface image taken at the first time, and the medical image taken at the second time.
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
A61B 90/00 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges
51.
A METHOD OF PROVIDING PROTON RADIATION THERAPY UTILIZING PERIODIC MOTION
Techniques are described herein for delivering a particle beam from a continuously rotating gantry towards a target according to a determined patient state. The determined patient state and an identified gantry angle of a gantry may be used to deliver a set of beamlets (e.g., a pattern of radiation dose) to the target. The particle beam may rotate through a range of gantry angles. The set of beamlets may be delivered continuously while the gantry rotates.
Techniques are described herein for delivering a particle beam from a continuously rotating gantry towards a target according to a determined patient state. The determined patient state and an identified gantry angle of a gantry may be used to deliver a set of beamlets (e.g., a pattern of radiation dose) to the target. The particle beam may rotate through a range of gantry angles. The set of beamlets may be delivered continuously while the gantry rotates.
Techniques for generating cancer registry records are provided. The techniques include obtaining a plurality of rules that define cancer registry record generation as a function of patient health records; obtaining one or more electronic health records associated with a patient that include cancer related treatment information; processing the cancer related treatment information in the one or more electronic health records to generate a cancer registry record for the patient that represents a portion of the cancer related treatment information; determining that the cancer registry record includes insufficient cancer related treatment information; and updating the cancer registry record to address the insufficient cancer related treatment information by evaluating the cancer related treatment information against the plurality of rules.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
Techniques for generating cancer registry records are provided. The techniques include obtaining a plurality of rules that define cancer registry record generation as a function of patient health records; obtaining one or more electronic health records associated with a patient that include cancer related treatment information; processing the cancer related treatment information in the one or more electronic health records to generate a cancer registry record for the patient that represents a portion of the cancer related treatment information; determining that the cancer registry record includes insufficient cancer related treatment information; and updating the cancer registry record to address the insufficient cancer related treatment information by evaluating the cancer related treatment information against the plurality of rules.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16B 50/30 - Data warehousing; Computing architectures
Techniques are described herein for delivering a particle beam composed of a plurality of beamlets from a continuously rotating gantry towards a target, by determining a plurality of predefined spots on the target and configuring them into a set of smaller spots on the outside of the target and a set of larger spots on the inside of the target, optimizing the delivery of the rotating particle beam such that the inside edge and the outside edge of the arc of the rotating beam are delivered to the spots located at the center of the target, and the central component of the arc of the beam is delivered to the spots located at the outside of the target.
Techniques are described herein for delivering a particle beam composed of a plurality of beamlets from a continuously rotating gantry towards a target, by determining a plurality of predefined spots on the target and configuring them into a set of smaller spots on the outside of the target and a set of larger spots on the inside of the target, optimizing the delivery of the rotating particle beam such that the inside edge and the outside edge of the arc of the rotating beam are delivered to the spots located at the center of the target, and the central component of the arc of the beam is delivered to the spots located at the outside of the target.
Systems and techniques may be used to estimate a relative motion of patient anatomy using a deep learning network during a radiotherapy treatment. For example, a method may include using a first deep neural network to relate input real-time partial patient measurements and a patient model including a reference volume to output patient states. The method may include using a second deep neural network to relate the patient states and the reference volume to relative motion information between the patient states and the reference volume. The deep neural networks may be used in real time to estimate a relative motion corresponding to an input image.
Systems and techniques may be used to estimate a relative motion of patient anatomy using a deep learning network during a radiotherapy treatment. For example, a method may include using a first deep neural network to relate input real-time partial patient measurements and a patient model including a reference volume to output patient states. The method may include using a second deep neural network to relate the patient states and the reference volume to relative motion information between the patient states and the reference volume. The deep neural networks may be used in real time to estimate a relative motion corresponding to an input image.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.
A61B 6/00 - Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
Systems and techniques may be used to estimate a patient state during a radiotherapy treatment. For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to an input image using the correspondence motion model.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
Systems and techniques may be used to estimate a real-time patient state during a radiotherapy treatment using a magnetic resonance linear accelerator (MR-Linac). For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to a 2D MR image using the correspondence motion model. The method may include directing radiation therapy, using the MR-Linac, to a target according to the patient state.
Systems and techniques may be used to estimate a patient state during a radiotherapy treatment. For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to an input image using the correspondence motion model.
Systems and techniques may be used to estimate a real-time patient state during a radiotherapy treatment using a magnetic resonance linear accelerator (MR-Linac). For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to a 2D MR image using the correspondence motion model. The method may include directing radiation therapy, using the MR-Linac, to a target according to the patient state.
The present disclosure relates to a method for use in adaptive radiotherapy and a treatment planning device. The method may comprise accessing a first medical image and a second medical image that represent a region of interest of a patient at different times. Each medical image is segmented into a target region and at least one non-target region. The method may further comprise accessing a deformation vector field including a plurality of vectors, wherein each vector defines a geometric transformation to map a respective voxel in the first medical image to a corresponding voxel in the second medical image. The method may further comprise generating a modified deformation vector field by: identifying a first vector in the deformation vector field that maps a voxel in the first medical image to a voxel that is in a non-target region in the second medical image; and determining whether the first vector causes a distance between the mapped voxel and the target region to increase and, if so, reducing the magnitude of the first vector. The method may further comprise post-processing the modified deformation vector field to compensate for changes in the shape or size of the target region.
Systems, computer-implemented methods, and computer readable media for generating a synthetic image of an anatomical portion based on an origin image of the anatomical portion acquired by an imaging device using a first imaging modality are disclosed. These systems may be configured to receive the origin image of the anatomical portion acquired by the imaging device using the first imaging modality, receive a convolutional neural network model trained for predicting the synthetic image based on the origin image, and convert the origin image to the synthetic image through the convolutional neural network model. The synthetic image may resemble an imaging of the anatomical portion using a second imaging modality differing from the first imaging modality.
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G01R 33/56 - Image enhancement or correction, e.g. subtraction or averaging techniques
An image segmentation method is disclosed. The method includes receiving a plurality of atlases and a subject image, each atlas including an atlas image showing a structure of interest and associated structure delineations, the subject image being acquired by an image acquisition device and showing the structure of interest. The method further includes calculating, by an image processor, mapped atlases by registering the respective atlases to the subject image, and determining, by the image processor, a first structure label map for the subject image based on the mapped atlases. The method also includes training, by the image processor, a structure classifier using a subset of the mapped atlases, and determining, by the image processor, a second structure label map for the subject image by applying the trained structure classifier to one or more subject image points in the subject image. The method additional includes combining, by the image processor, the first label map and the second label map to generate a third label map representative of the structure of interest.
Techniques for generating radiotherapy treatment plans and establishing machine learning models for the generation and optimization of radiotherapy dose data are disclosed. An example method for generating a radiotherapy dose distribution using a generative model, trained in a generative adversarial network, includes: receiving anatomical data of a human subject that indicates a mapping of an anatomical area for radiotherapy treatment; generating radiotherapy dose data corresponding to the mapping with use of the trained generative model, as the generative model processes the anatomical data as an input and provides the dose data as output; and identifying the radiotherapy dose distribution for the radiotherapy treatment of the human subject based on the dose data. Another example method for training of the generative model includes establishing values of the generative model and a discriminative model of the generative adversarial network using adversarial training, including in a conditional generative adversarial network arrangement.
A deformable radiotherapy phantom can be produced using an additive manufacturing process, based on a medical image of the patient. The deformable phantom can include dosimeters for measuring radiation dose distribution. A smart material can allow deformation in response to an applied stimulus. Among other things, the phantom can be used to validate radiation dose warping, a radiotherapy treatment plan, to determine a maximum acceptable deformation of the patient, to validate a cumulative accuracy of dose warping and deformable image registration, or the like.
Techniques for generating radiotherapy treatment plans and establishing machine learning models for the generation and optimization of radiotherapy dose data are disclosed. An example method for generating a radiotherapy dose distribution using a generative model, trained in a generative adversarial network, includes: receiving anatomical data of a human subject that indicates a mapping of an anatomical area for radiotherapy treatment; generating radiotherapy dose data corresponding to the mapping with use of the trained generative model, as the generative model processes the anatomical data as an input and provides the dose data as output; and identifying the radiotherapy dose distribution for the radiotherapy treatment of the human subject based on the dose data. Another example method for training of the generative model includes establishing values of the generative model and a discriminative model of the generative adversarial network using adversarial training, including in a conditional generative adversarial network arrangement.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
A deformable radiotherapy phantom can be produced using an additive manufacturing process, based on a medical image of the patient. The deformable phantom can include dosimeters for measuring radiation dose distribution. A smart material can allow deformation in response to an applied stimulus. Among other things, the phantom can be used to validate radiation dose warping, a radiotherapy treatment plan, to determine a maximum acceptable deformation of the patient, to validate a cumulative accuracy of dose warping and deformable image registration, or the like.
Techniques for generating an enhanced cone-beam computed tomography (CBCT) image using a trained model are provided. A CBCT image of a subject is received. a synthetic computed tomography (sCT) image corresponding to the CBCT image is generated, using a generative model. The generative model is trained in a generative adversarial network (GAN). The generative model is further trained to process the CBCT image as an input and provide the sCT image as an output. The sCT image is presented for medical analysis of the subject.
Techniques for generating an enhanced cone-beam computed tomography (CBCT) image using a trained model are provided. A CBCT image of a subject is received. a synthetic computed tomography (sCT) image corresponding to the CBCT image is generated, using a generative model. The generative model is trained in a generative adversarial network (GAN). The generative model is further trained to process the CBCT image as an input and provide the sCT image as an output. The sCT image is presented for medical analysis of the subject.
A statistical learning technique that does not rely upon paired imaging information is described herein. The technique may be computer-implemented and may be used in order to train a statistical learning model to perform image synthesis, such as in support of radiation therapy treatment planning. In an example, a trained statistical learning model may include a convolutional neural network established as a generator convolutional network, and the generator may be trained at least in part using a separate convolutional neural network established as a discriminator convolutional network. The generator convolutional network and the discriminator convolutional network may form an adversarial network architecture for use during training. After training, the generator convolutional network may be provided for use in synthesis of images, such as to receive imaging data corresponding to a first imaging modality type, and to synthesize imaging data corresponding to a different, second imaging modality type.
A statistical learning technique that does not rely upon paired imaging information is described herein. The technique may be computer-implemented and may be used in order to train a statistical learning model to perform image synthesis, such as in support of radiation therapy treatment planning. In an example, a trained statistical learning model may include a convolutional neural network established as a generator convolutional network, and the generator may be trained at least in part using a separate convolutional neural network established as a discriminator convolutional network. The generator convolutional network and the discriminator convolutional network may form an adversarial network architecture for use during training. After training, the generator convolutional network may be provided for use in synthesis of images, such as to receive imaging data corresponding to a first imaging modality type, and to synthesize imaging data corresponding to a different, second imaging modality type.
Apparatus and techniques are described herein for nuclear magnetic resonance (MR) projection imaging. Such projection imaging may be used to control radiation therapy delivery to a subject, such as including receiving reference imaging information, generating a two-dimensional (2D) projection image using imaging information obtained via nuclear magnetic resonance (MR) imaging, the 2D projection image corresponding to a specified projection direction, the specified projection direction including a path traversing at least a portion of an imaging subject, determining a change between the generated 2D projection image and the reference imaging information, and controlling delivery of the radiation therapy at least in part using the determined change between the obtained 2D projection image and the reference imaging information.
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.
Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.
Embodiments of the disclosure may be directed to an image processing system configured to receive a medical image of a region of a subject's body taken at a first time and to receive a surface image of an exterior portion of the region of the subject's body taken at the first time. The image processing may also be configured to receive a medical image of the region of the subject's body taken at a second time and to register the medical image taken at the first time, the surface image taken at the first time, and the medical image taken at the second time.
Embodiments of the disclosure may be directed to an image processing system configured to receive a medical image of a region of a subject's body taken at a first time and to receive a surface image of an exterior portion of the region of the subject's body taken at the first time. The image processing may also be configured to receive a medical image of the region of the subject's body taken at a second time and to register the medical image taken at the first time, the surface image taken at the first time, and the medical image taken at the second time.
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
G06T 7/30 - Determination of transform parameters for the alignment of images, i.e. image registration
A61B 90/00 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges
80.
Pseudo-CT generation from MR data using a feature regression model
Systems and methods are provided for generating a pseudo-CT prediction model that can be used to generate pseudo-CT images. An exemplary system may include a processor configured to retrieve training data including at least one MR image and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may extract a plurality of features from each image point of the at least one MR image, create a feature vector for each image point based on the extracted features, and extract a CT value from each image point of the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
A61B 90/00 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges
81.
System and method for learning models of radiotherapy treatment plans to predict radiotherapy dose distributions
The present disclosure relates to systems and methods for developing radiotherapy treatment plans through the use of machine learning approaches and neural network components. A neural network is trained using one or more three-dimensional medical images, one or more three-dimensional anatomy maps, and one or more dose distributions to predict a fluence map or a dose map. During training the neural network receives a predicted dose distribution determined by the neural network that is compared to an expected dose distribution. Iteratively the comparison is performed until a predetermined threshold is achieved. The trained neural network is then utilized to provide a three-dimensional dose distribution.
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06N 3/084 - Backpropagation, e.g. using gradient descent
Radiation treatment planning and administration can include a Monte Carlo computer simulation tool to simulate photo-generated electrons in tissue. In the simulation, electrons that have left tissue voxels and entered air voxels can be evaluated to identify electrons that are circling along a spiraling trajectory in the air voxels. After at least one full spiraling circumference or other specified distance has been traversed using a detailed electron transport model, a simpler linear ballistic motion model can be instituted. This speeds simulation while accurately accounting for spiraling electrons that re-enter tissue voxels.
Systems and methods can include training a deep convolutional neural network model to provide a beam model for a radiation machine, such as to deliver a radiation treatment dose to a subject. A method can include determining a range of parameter values for at least one parameter of a beam model corresponding to the radiation machine, generating a plurality of sets of beam model parameter values, wherein one or more individual sets of beam model parameter values can include a parameter value selected from the determined range of parameter values, providing a plurality of corresponding dose profiles respectively corresponding to respective individual sets beam model parameter values in the plurality of sets of beam model parameter values, and training the neural network model using the plurality of beam models and the corresponding dose profiles.
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
Systems and methods can include training a deep convolutional neural network model to provide a beam model for a radiation machine, such as to deliver a radiation treatment dose to a subject. A method can include determining a range of parameter values for at least one parameter of a beam model corresponding to the radiation machine, generating a plurality of sets of beam model parameter values, wherein one or more individual sets of beam model parameter values can include a parameter value selected from the determined range of parameter values, providing a plurality of corresponding dose profiles respectively corresponding to respective individual sets beam model parameter values in the plurality of sets of beam model parameter values, and training the neural network model using the plurality of beam models and the corresponding dose profiles.
Radiation treatment planning and administration can include a Monte Carlo computer simulation tool to simulate photo-generated electrons in tissue. In the simulation, electrons that have left tissue voxels and entered air voxels can be evaluated to identify electrons that are circling along a spiraling trajectory in the air voxels. After at least one full spiraling circumference or other specified distance has been traversed using a detailed electron transport model, a simpler linear ballistic motion model can be instituted. This speeds simulation while accurately accounting for spiraling electrons that re-enter tissue voxels.
The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical image. Embodiments of the present disclosure may locate and track a moving, three-dimensional (3D) target in a patient undergoing image-guided radiation therapy. For example, an adaptive filter model for a region of interest in the patient may be received, wherein the adaptive filter model is based on the target to be tracked. An image acquisition device may obtain a two-dimensional (2D) slice of a region of interest in the patient. A processor may then apply the adaptive filter model to the 2D slice, wherein the adaptive filter model includes an offset value. The processor may also determine a location of the target in the 2D slice based on the adaptive filter model. The processor may also estimate a potential location of the target based on the offset value. The processor may then repeat one or more of the above steps to track the moving target during image-guided radiation therapy of the patient.
Embodiments disclose a method and system for segmenting medical images. In certain embodiments, the system comprises a database configured to store a plurality of medical images acquired by an image acquisition device. The plurality of images include at least one first medical image of an object, and a second medical image of the object, each first medical image associated with a first structure label map. The system further comprises a processor that is configured to register the at least one first medical image to the second medical image, determine a classifier model using the registered first medical image and the corresponding first structure label map, and determine a second structure label map associated with the second medical image using the classifier model.
A61B 6/00 - Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
Systems and methods for performing radiation treatment planning are provided. An exemplary system may include a processor device communicatively coupled to a memory device and configured to perform operations when executing instruction stored in the memory device. The operations may include receiving a reference treatment plan including one or more dose constraints and determining, based on the reference treatment plan, segment information of a plurality of radiation beams. The operations may also include determining a fluence map for each of the plurality of radiation beams based on the one or more dose constraints using a fluence map optimization algorithm. The operations may also include determining a dose distribution based on the fluence maps of the plurality of radiation beams. The operations may also include determining at least one beam modulation property of a new treatment plan using a warm-start optimization algorithm based on the segment information and the dose distribution.
Systems and methods for performing radiation treatment planning are provided. An exemplary system may include a processor device communicatively coupled to a memory device and configured to perform operations when executing instruction stored in the memory device. The operations may include receiving a reference treatment plan including one or more dose constraints and determining, based on the reference treatment plan, segment information of a plurality of radiation beams. The operations may also include determining a fluence map for each of the plurality of radiation beams based on the one or more dose constraints using a fluence map optimization algorithm. The operations may also include determining a dose distribution based on the fluence maps of the plurality of radiation beams. The operations may also include determining at least one beam modulation property of a new treatment plan using a warm-start optimization algorithm based on the segment information and the dose distribution.
Techniques for use in adaptive radiotherapy and a treatment planning device are described. A method may comprise accessing two medical images representing a region of interest of a patient at different times. Each medical image can be segmented into a target region and at least one non-target region. The method may comprise accessing a deformation vector field including a plurality of vectors to map a respective voxel in a first medical image to a corresponding voxel in a second medical image. The method may comprise generating a modified deformation vector field and post-processing the modified deformation vector field to compensate for changes in the shape or size of the target region.
The present disclosure relates to a method for use in adaptive radiotherapy and a treatment planning device. The method may comprise accessing a first medical image and a second medical image that represent a region of interest of a patient at different times. Each medical image is segmented into a target region and at least one non-target region. The method may further comprise accessing a deformation vector field including a plurality of vectors, wherein each vector defines a geometric transformation to map a respective voxel in the first medical image to a corresponding voxel in the second medical image. The method may further comprise generating a modified deformation vector field by: identifying a first vector in the deformation vector field that maps a voxel in the first medical image to a voxel that is in a non-target region in the second medical image; and determining whether the first vector causes a distance between the mapped voxel and the target region to increase and, if so, reducing the magnitude of the first vector. The method may further comprise post-processing the modified deformation vector field to compensate for changes in the shape or size of the target region.
The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack.
Systems and methods are provided for generating a pseudo-CT prediction model using multi-channel MR images. An exemplary system may include a processor configured to retrieve training data including multiple MR images and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may determine at least one tissue parameter map based on the multiple MR images and obtain CT values based on the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the tissue parameter maps and the CT values of the plurality of training subjects.
G06K 9/46 - Extraction of features or characteristics of the image
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
A61B 5/055 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
Systems and methods can include a method for training a deep convolutional neural network to provide a patient radiation treatment plan, the method comprising collecting patient data from a group of patients, the patient data including at least one image of patient anatomy and a prior treatment plan, wherein the treatment plan includes predetermined machine parameters, and training a deep convolution neural network for regression by using the prior treatment plans and the corresponding collected patient data to determine a new treatment plan. Systems and methods can also include a method of using a deep convolutional neural network to provide a radiation treatment plan, the method comprising retrieving a trained deep convolution neural network previously trained on patient data from a group of patients, collecting new patient data, wherein the new patient data includes at least one image of patient anatomy, and determining a new treatment plan for the new patient using the trained deep convolutional neural network for regression, wherein the new treatment plan has a new set of machine parameters.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06N 3/04 - Architecture, e.g. interconnection topology
Features, such as anatomical features, may be automatically segmented from medical imaging information, using a computer-implemented method. In an example, three-dimensional (3D) medical imaging information may be received, such as defining a first volume. A first trained convolutional neural network (CNN) may be applied to the three-dimensional medical imaging information. An output from the first trained CNN may be used to determine a region-of-interest within the first volume, the region-of-interest defining a lesser, second volume. A different, second trained CNN may be applied to the region-of-interest, a segmented representation of the 3D medical imaging information may be provided using the outputs from the first and second CNNs, where the second CNN provides enhanced segmentation detail in the region-of-interest without requiring application of the second CNN to an entirety of the first volume. Techniques are also described from training one or more of the first and second CNNs.
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06K 9/32 - Aligning or centering of the image pick-up or image-field
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06N 3/04 - Architecture, e.g. interconnection topology
A61B 90/00 - Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups , e.g. for luxation treatment or for protecting wound edges
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
96.
RADIATION THERAPY PLANNING USING DEEP CONVOLUTIONAL NETWORK
A deep convolutional neural network can be trained to provide a patient radiation treatment plan. Training can include collecting patient data based on at least one image of patient anatomy from patients, determining a treatment plan including a set of control points from the collected patient data, and using the determined treatment plans and the corresponding collected patient data to train a deep convolutional neural network for regression to determine a treatment plan including a set of control points from collected patient data. The trained model can be used to provide a radiation treatment plan, such as in real-time.
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
97.
METHOD FOR IMPROVING CONE-BEAM CT IMAGE QUALITY USING A DEEP CONVOLUTIONAL NEURAL NETWORK
Systems and methods can include training a DCNN to reduce one or more artifacts using a projection space approach or an image space approach. A projection space approach can include collecting an artifact contaminated CBCT projection space image, and a corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated CBCT projection space image and the corresponding artifact reduced, CBCT projection space image collected from each patient in the group of patients to train a DCNN to reduce one or more artifacts in a projection space image. An image space approach can include collecting a plurality of CBCT patient anatomical images and corresponding registered CT anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced CT anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.
Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.
A61B 6/00 - Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
Embodiments of the disclosure may be directed to a system for generating a motion target volume representative of shape changes of a target region in a patient. The system may comprise at least one computer system configured to receive a plurality of electronic medical images that include the target region, and each of the plurality of images may have been taken at a different time point. The computer system may be configured to define a three-dimensional volume containing the target region in each of the plurality of images, and the three-dimensional volume may be different in at least two of the plurality of images due to differences in shape of the target region in the at least two images. The computer system may also be configured to co-register the three-dimensional volumes and generate the motion target volume, wherein the motion target volume encompasses each of the three-dimensional volumes.
Embodiments of the disclosure may be directed to a system for generating a motion target volume representative of shape changes of a target region in a patient. The system may comprise at least one computer system configured to receive a plurality of electronic medical images that include the target region, and each of the plurality of images may have been taken at a different time point. The computer system may be configured to define a three-dimensional volume containing the target region in each of the plurality of images, and the three-dimensional volume may be different in at least two of the plurality of images due to differences in shape of the target region in the at least two images. The computer system may also be configured to co-register the three-dimensional volumes and generate the motion target volume, wherein the motion target volume encompasses each of the three-dimensional volumes.
