Varian Medical Systems International Ag

Siemens Healthineers International Ag

Varian Medical Systems, Inc.

Varian Medical Systems Canada Inc.

Janne Iimari Nord

Jarkko Yriana Peltola

Esa Kuusela

Juha Kauppinen

Maria Cordero Marcos

Joakim Pyyry

Hannu Laaksonen

Santtu Ollila

Mikko Vainio

Sami Petri Perttu

Lasse Heikki Toimela

Joona Hartman

Corey Zankowski

Marko Rusanen

Benjamin M Haas

Viljo Petäjä

Perttu Niemela

Lauri Halko

Pascal Paysan

Steven W Prince

John Van Heteren

Dieter Marc Seghers

Ramin Baghaie

Tuomas Tallinen

Martin Sabel

Perttu Niemelä

Emmi Ruokokoski

Jan Schreier

Adam Scott Harrington

Sylvie Spiessens

Dominique Gasser

Yves Archambault

Anri Maarita Friman

Reto W Filiberti

Ross Bernald Hannibal

HsinLu Hsu

Sami P Siljamäki

Hannu Helminen

Roland Meier

Joseph Schumm

Ronan MacLaverty

Kari Jyrkkälä

Antti Viitala

Daniel Valenzuela

Tomi Ruokola

Sean Herries

Santtu Tuomo Tapani Ollila

Stanley Mansfield

Thomas Coradi

Elena Czeizler

Michelle Marie Svatos

Christopher Boylan

Sami P Siljamaki

Viljo Petaja

María Cordero Marcos

Armel C Rosselet

Tuomas Erik Torsti

Agam Sharda

Xinhui Yang

Anssi Kesti-Helia

Francesca Attanasi

Lasse Toimela

Steven Prince

Tao Sun

Santtu Tt Ollila

William Kearns

Anne Razavi

Tor-Erik Malen

Santtu T T Ollila

Helen Phillips

Olavi Pesonen

Perttu Niemelae

Maria Isabel Cordero Marcos

Santtu TT Ollila

Joakim Olavi Pyyry

Tori-Erik Malen

Cordero Marcos Maria

A control circuit accesses information regarding a plurality of pre-existing vetted radiation treatment plans for a variety of patients and uses that information to train at least one model (such as a dose volume histogram estimation model). The control circuit then uses that model to develop estimates for a radiation treatment plan for a particular patient. The control circuit can then use those estimates to develop a candidate radiation treatment plan.

Apparatus comprising a memory, a control circuit, a user interface, and a radiation treatment platform to facilitate an administration of a knowledge-based radiation treatment plan

Example methods for radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining first image data associated with a patient; generating first feature data by processing the first image data associated with a first resolution level using a first processing pathway; generating second feature data by processing second image data associated with a second resolution level using a second processing pathway; and generating third feature data by processing third image data associated with a third resolution level using a third processing pathway. The example method may also comprise generating a first combined set of feature data associated with the second resolution level, and a second combined set of feature data associated with the first resolution level based on the first feature data and the first combined set. Further, the example method may comprise generating output data associated with radiotherapy treatment of the patient.

Methods and systems for radiotherapy treatment planning using deep learning engines

Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.

Methods and systems for adaptive radiotherapy treatment planning using deep learning engines

Systems and methods for radiation treatment planning can include a computing system determining an estimate of a radiation dose distribution within an anatomical region of a patient, and determining a cost matrix representing an objective function, using the estimate of the radiation dose distribution. The computing system can project the cost matrix on each of a plurality of fluence planes. Each of the plurality of fluence planes can be associated with a corresponding gantry-couch orientation of a plurality of gantry-couch orientations of a medical linear accelerator. The computing system can determine, using projections of the cost matrix on each of the plurality of fluence planes, a sequence of gantry-couch orientations among the plurality of gantry-couch orientations representing a treatment path.

Trajectory optimization using dose estimation and conflict detection

In a radiation treatment plan that includes a plurality of treatment fields of multiple treatment modalities, such as IMRT modality and dynamic treatment path modality (e.g., VMAT and conformal arc therapy), an optimized spatial point sequence may be determined that optimizes the total treatment time, which includes both the beam-on time (i.e., during the delivery of radiation dose) and the beam-off time (i.e., during transitions between consecutive treatment fields). The result is a time-ordered field trajectory that intermixes and interleaves different treatment fields. In one embodiment, a dynamic treatment path may be cut into a plurality of sections, and one or more IMRT fields may be inserted between the plurality of sections.

Generating time-efficient treatment field trajectories for external-beam radiation treatments

Systems and methods for anatomical structure segmentation in medical images using multiple anatomical structures, instructions and segmentation models.

Systems and methods for automatic segmentation in medical imaging with multiple anatomical structure segmentation models

Systems, devices, and methods for dosimetric verification of radiation therapy treatments by selective evaluation of measurement points. Systems, methods, and computer program-products for providing dosimetric verification of radiation therapy treatments by evaluating measurement points using different evaluation criteria.

Portal dosimetry systems, devices, and methods

A method of generating a treatment plan for treating a patient with radiotherapy, the method includes obtaining a plurality of sample plans, which are generated by use of a knowledge base comprising historical treatment plans and patient data. The method also includes performing a multi-criteria optimization based on the plurality of sample plans to construct a Pareto frontier, where the plurality of sample plans are evaluated with at least two objectives measuring qualities of the plurality of sample plans such that treatment plans on the constructed Pareto frontier are Pareto optimal with respect to the objectives. The method further includes identifying a treatment plan by use of the constructed Pareto frontier.

Knowledge based multi-criteria optimization for radiotherapy treatment planning

A multi-layer multi-leaf collimation system includes at least a two layers of collimation leaves. The first multi-leaf collimator layer is configured to primarily perform a first function to affect a radiation beam traveling from a radiation source to a target and a second multi-leaf collimator layer is configured to primarily perform a second function, different from the first function, to affect the radiation beam for the administration of a treatment plan.

Method and apparatus for using a multi-layer multi-leaf collimation system

A search space used for multicriteria optimization in radiation treatment planning can be expanded using extrapolation. For instance, given an initial set of base plans, one or more virtual plans can be generated by assigning a weight to each base plan such that at least one of the weights is less than zero and/or such that the sum of the weights is not normalized to 1. The dose distribution for the virtual plan is computed as the weighted sum of the dose distributions of the base plans. Virtual plans criteria can be used together with the initial set of base plans to define an expanded search space within which interpolation can be performed to identify an optimal treatment plan.

Expansion of search space for multicriteria optimization in radiation treatment planning

One or more medical images of a patient are processed by a first neural network model to determine a region-of-interest (ROI) or a cut-off plane. Information from the first neural network model is used to crop the medical images, which serves as input to a second neural network model. The second neural network model processes the cropped medical images to determine contours of anatomical structures in the medical images of the patient. Each of the first and second neural network models are deep neural network models. By use of cropped images in the training and inference phases of the second neural network model, contours are produced with sharp edges or flat surfaces.

Systems and methods for image cropping and anatomical structure segmentation in medical imaging

An apparatus for treatment planning and/or treatment setup includes: a simulator configured to obtain a first model representing a first component of a medical system, and virtually move the first model to simulate a movement of the first component of the medical system; an analyzer configured to determine imaging waypoints during a treatment process based on the virtual movement of the first model; and a graphic generator configured to generate a graphic based on the determined imaging waypoints.

Imaging waypoints for radiation treatment

A system for estimating a dose from a radiation therapy plan includes a memory that stores machine-readable instructions and a processor communicatively coupled to the memory, the processor operable to execute the instructions to subdivide a representation of a volume of interest into voxels. The processor also determines distances between a planned radiation field origin and each respective voxel. The processor further computes geometry-based expected (GED) metrics based on the distances, a plan parameter, and a field strength parameter. The processor sums the metrics to yield an estimated dose received by the volume of interest from the planned radiation field.

Knowledge-based spatial dose metrics and methods to generate beam orientations in radiotherapy

A control circuit optimizes a radiation treatment plan for a patient treatment volume using an automatically iterating optimization process that optimizes as a function, at least in part, of predetermined cost functions, wherein at least one of the cost functions favors apertures for the multi-leaf collimation system having local curvature that deviates only minimally from a reference curvature. By one approach the control circuit determines the reference curvature as a function, at least in part, of at least one of setting the reference curvature to a static minimal local curvature, a shape of a projective mapping of the treatment volume onto an isocenter plane, and/or a fluence map associated with an amount of radiation to be administered to the treatment volume from a particular direction. By one approach the control circuit dynamically determines when to employ one or more such cost functions.

Treating a treatment volume with therapeutic radiation using a multi-leaf collimation system

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Espoo, Finland

Janne Iimari Nord