Elekta, Inc.

Elekta Ab (publ)

Impac Medical Systems, Inc.

Virgil Matthew Willcut

David Anthony Roberts

Martin Broad

Michel Moreau

Xiao Han

Lyndon Stanley Hibbard

Yan Zhou

Richard Henry Stark

Spencer Marshall

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.

Deformable image registration using deep learning

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.

Adaptive radiotherapy system

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.

Systems and methods of accounting for shape change during radiotherapy

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.

Radiotherapy treatment plan optimization workflow

A collimator for a radiotherapy apparatus, comprising a block of isolation-attenuating material having a front face forming the leading edge of tie block and at least one main rear face defining the trailing edge of the block, in which the or each rear face is substantially planar in the direction of the depth of the block and non-parallel to the front face. The collimator may form part of a radiotherapy apparatus, and methods of operation of such apparatus are described.

Collimator for radiotherapy apparatus

A collimator for a radiotherapy apparatus comprising a block of radiation-attenuating material having a front face forming the leading edge of the block and at least one main rear face defining the trailing edge of the block, in which the least one main rear face has a material disposed thereon and is non-parallel to the front face. The collimator may form part of a radiotherapy apparatus, and methods of operation of such apparatus are described.

Systems and methods can include obtaining computerized physician intent data representing an initial patient care plan; creating a computerized workflow to include a course of multiple radiation therapy sessions; performing instructions on the oncology computer system to generate control parameters for a radiation therapy apparatus to provide the radiation treatment in accordance with the workflow during the course of sessions; obtaining computerized treatment data after initiating the course of sessions; processing the computerized treatment data, using the processor circuit, to determine an indication of delivery or effect of the radiation treatment during the course of sessions based on the initial patient care plan relative to the workflow; using the indication of delivery or effect of the radiation treatment to adapt the patient care plan; and managing the workflow for the patient using the adapted patient care plan as the patient proceeds through a course of sessions.

Automated, data-driven treatment management system for adaptive radiotherapy workflows

A collimator for a radiotherapy apparatus, comprising a block of radiation-attenuating material having a front face forming the leading edge of the block and at least one main rear face defining the trailing edge of the block, in which the or each rear face is substantially planar in the direction of the depth of the block and non-parallel to the front face. The collimator may form part of a radiotherapy apparatus, and methods of operation of such apparatus are described.

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.

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Kirkwood, MO, United States of America

Virgil Matthew Willcut