Elekta, Inc.

Resonant Medical, Inc.

Eleckta Ltd.

Martin Emile Lachaine

Tony Falco

Silvain Bériault

Fabienne Lathuiliere

Sebastien Tremblay

Michel Moreau

Silvain Beriault

Daniel Lodu

Sergei Koptenko

Véronique Audet

Xing Huang

Jonathan Mason

David Roberts

Joshua Freedman

Joseph Stancanello

Francois Paul George Rene Hebert

Roushanak Rahmat

Fabienne Lathuilière

Jean-Francois Gauthier

Michael Donovan

Ulrich Scheipers

Martin Bonneville

Chantal Dussault

Jason Gorman

Rui Lopes

Marc-André Lalonde

Pierre-Olivier Beaudoin

Jean-François Gauthier

Philippe Fortier

Silvain BÈRIAULT

There are provided systems, methods and radiotherapy devices for monitoring the position of a subject during radiotherapy, which comprise obtaining an MR image slice of a subject that exhibits through-plane distortion, obtaining a pre-treatment image slice of the subject that corresponds to the MR image slice and exhibits through-plane distortion that corresponds to the through-plane distortion of the MR image slice, and comparing the MR image slice with the corresponding pre-treatment image slice.

MRI-based motion monitoring and gating for radiotherapy

Systems and methods are disclosed for processing of cone beam computed tomography (CBCT) image data, in connection with radiotherapy planning and treatments. Example operations for a radiotherapy workflow include: obtaining a plurality of newly captured CBCT projections that provide imaging of an anatomical area corresponding to an area for radiotherapy treatment; reconstructing a quantitative CBCT image from the plurality of newly captured CBCT projections; identifying anatomical changes of the human patient based on comparing a representation of the anatomical area in the quantitative CBCT image with a representation of the anatomical area in a planning image; and modifying a treatment plan for a radiotherapy workflow based on the anatomical changes, with the treatment plan providing dose calculations for the anatomical area. Further operations for training and use of a regression model to correct CBCT projections and perform additional imaging and radiotherapy data processing operations are disclosed.

Techniques for adaptive radiotherapy based on CBCT projection correction and reconstruction

Systems and methods are provided for performing operations comprising: generating a radiotherapy treatment plan for treating an ablation volume comprising a myocardial scar based on a first image of a patient that is generated using contrast-enhanced magnetic resonance (MR) imaging information; identifying, based on the first image of the patient, the ablation volume comprising the myocardial scar in a second image of the patient that is generated using MR imaging information without contrast; adjusting the radiotherapy treatment plan based on the ablation volume identified in the second image of the patient; acquiring a cine image of the patient during delivery of a radiotherapy treatment fraction by a radiotherapy treatment device based on the adjusted radiotherapy treatment plan; and comparing the cine image to the second image of the patient to control delivery of a radiotherapy beam by the radiotherapy treatment device.

Cardiac ablation using an MR linac

Systems and methods are provided for registering images. The systems and methods perform operations comprising: receiving, at a first time point in a given radiation session, a first imaging slice corresponding to a first plane; encoding the first imaging slice to a lower dimensional representation; applying a trained machine learning model to the encoded first imaging slice to estimate an encoded version of a second imaging slice corresponding to a second plane at the first time point to provide a pair of imaging slices for the first time point; simultaneously spatially registering the pair of imaging slices to a volumetric image, received prior to the given radiation session, comprising a time-varying object to calculate displacement of the object; and generating an updated therapy protocol to control delivery of a therapy beam based on the calculated displacement of the object.

Automatic gating with an MR linac

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.

Machine learning approach to real-time patient motion monitoring

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.

Real-time patient motion monitoring using a magnetic resonance linear accelerator (MRLINAC)

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.

Real-time motion monitoring using deep neural network

Systems and methods for managing motions of an anatomical region of interest of a patient during image-guided radiotherapy are disclosed. An exemplary system may include an image acquisition device, a radiotherapy device, and a processor device. The processor device may be configured to control the image acquisition device to acquire at least one 2D image. Each 2D image may include a cross-sectional image of the anatomical region of interest. The processor device may also be configured to perform automatic contouring in each 2D image to extract a set of contour elements segmenting the cross-sectional image of the anatomical region of interest in that 2D image. The processor device may be further configured to match the set of contour elements to a 3D surface image of the anatomical region of interest to determine a motion of the anatomical region of interest and to control radiation delivery based on the determined motion.

Motion management in image-guided radiotherapy using contour elements

Apparatus and techniques are described herein for nuclear magnetic resonance (MR) projection imaging. Such projection imaging may be used for generating four-dimensional (4D) imaging information representative of a physiologic cycle of a subject, such as including generating two or more two-dimensional (2D) images, the 2D images comprising projection images representative of different projection angles, and the 2D images generated using imaging information obtained via nuclear magnetic resonance (MR) imaging, assigning the particular 2D images to bins at least in part using information indicative of temporal positions within the physiologic cycle corresponding to the particular 2D images, constructing three-dimensional (3D) images using the binned 2D images, and constructing the 4D imaging information, comprising aggregating the 3D images.

Magnetic resonance projection for constructing four-dimensional image information

Systems and methods are provided for registering images. The systems and methods perform operations comprising: receiving, at a first time point during a given radiation session, a first imaging slice comprising an object, the first imaging slice corresponding to a first plane; accessing, at the first time point during the given radiation session, a composite imaging slice corresponding to the first plane, the composite imaging slice being generated using a plurality of imaging slices obtained prior to the first time point; spatially registering the first imaging slice and the composite imaging slice; determining movement of the object using the spatially registered first imaging slice and the composite imaging slice; and generating an updated therapy protocol to control delivery of a therapy beam based on the determined movement.

Adaptive radiation therapy using composite imaging slices

Real-time patient motion monitoring using a magnetic resonance linear accelerator (MR-LINAC)

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.

Magnetic resonance projection imaging

Growing community of inventors

Montreal, Canada

Martin Emile Lachaine