Elekta, Inc.

Impac Medical Systems, Inc.

Computerized Medical Systems, Inc.

Lyndon Stanley Hibbard

Xiao Han

Virgil Matthew Willcut

Yan Zhou

Ryan Scott Brame

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.

Predicting radiotherapy control points using projection images

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.

Machine learning optimization of fluence maps for radiotherapy treatment

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.

Adversarial prediction of radiotherapy treatment plans

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.

System and method for learning models of radiotherapy treatment plans to predict radiotherapy dose distributions

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.

Radiotherapy treatment plan modeling using generative adversarial networks

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.

Systems and methods for determining radiation therapy machine parameter settings

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 and methods for segmentation of intra-patient medical images

Methods and systems are disclosed for correcting segmentation errors in pre-existing contours and surfaces. Techniques are disclosed for receiving one or more edit contours from a user, identifying pre-existing data points that should be eliminated, and generating a new corrected surface. Embodiments disclosed herein relate to using received edit contours to generate a set of points on a pre-existing surface, and applying a proximity test to eliminate pre-existing constraint points that are undesirable.

Method and apparatus for correction of errors in surfaces

Disclosed herein are techniques for performing automated structure delineation on image data using trained landmark detectors and a shape refinement tool. The landmark detectors can be trained to detect a landmark in the image data based on image features that are indicative of intensity variations over a plurality of windows of the image data points. A machine-learning algorithm can be used to train the landmark detectors. The landmarks in the image data that are detected by the trained landmark detects can be used to initialize an iterative shape refinement to thereby compute a refined shape estimate for a structure of interest such as a prostate.

Method and apparatus for automated delineation of structure shape for image guided treatment planning

A technique is disclosed for generating a new contour and/or a 3D surface such as a variational implicit surface from contour data. In one embodiment, B-spline interpolation is used to efficiently generate a new contour (preferably a transverse contour), from a plurality of input contours (preferably, sagittal and/or coronal contours). In another embodiment, a point reduction operation is performed on data sets corresponding to any combination of transverse, sagittal, or coronal contour data prior to processing those data sets to generate a 3D surface such as a variational implicit surface. A new contour can also be generated by the intersection of this surface with an appropriately placed and oriented plane. In this manner, the computation of the variational implicit surface becomes sufficiently efficient to make its use for new contour generation practical.

Method and apparatus for efficient three-dimensional contouring of medical images

A technique is disclosed for generating a new contour and/or a 3D surface such as a variational implicit surface from contour data. In one embodiment, a point reduction operation is performed on data sets corresponding to any combination of transverse, sagittal, or coronal contour data prior to processing those data sets to generate a 3D surface such as a variational implicit surface. A new contour can also be generated by the intersection of this surface with an appropriately placed and oriented plane. In this manner, the computation of the variational implicit surface becomes sufficiently efficient to make its use for new contour generation practical.

Growing community of inventors

St. Louis, MO, United States of America

Lyndon Stanley Hibbard