Reflexion Medical, Inc.

Leland Stanford Junior University

Ge Precision Healthcare LLC

Debashish Pal

Peter D Olcott

Yevgen Voronenko

Michael Kirk Owens

Rostem Bassalow

Jayakrishnan Janardhanan

Adam S Wang

Sen Wang

Abdullah-Al-Zubaer Imran

Evan Zucker

Bhavik Natvar Patel

David Q Larkin

Christopher Eric Brown

Ayan Mitra

George Andrew Zdasiuk

Peter Demetri Olcott

Systems and methods for shuttle mode radiation delivery are described herein. One method for radiation delivery comprises moving the patient platform through the patient treatment region multiple times during a treatment session. This may be referred to as patient platform or couch shuttling (i.e., couch shuttle mode). Another method for radiation delivery comprises moving the therapeutic radiation source jaw across a range of positions during a treatment session. The jaw may move across the same range of positions multiple times during a treatment session. This may be referred to as jaw shuttling (i.e., jaw shuttle mode). Some methods combine couch shuttle mode and jaw shuttle mode. Methods of dynamic or pipelined normalization are also described.

Systems and methods for shuttle mode radiation delivery

Disclosed herein are methods for radiotherapy treatment plan optimization for irradiating one or more target regions using both an internal therapeutic radiation source (ITRS) and an external therapeutic radiation source (ETRS). One variation of a method comprises iterating through ITRS radiation dose values and ETRS radiation dose values to attain a cumulative dose that meets prescribed dose requirements. In some variations, an ITRS is an injectable compound that has a targeting backbone and a radionuclide, and images acquired using an imaging compound that has the same targeting backbone as the injectable compound can be used to calculate the radiation dose deliverable using the injectable ITRS, and also to calculate firing filters for delivering radiation using a biologically-guided radiation therapy (BGRT) system. Image data acquired from a previous treatment session may be used to adapt the dose provided by an ITRS and/or ETRS for a future treatment session.

Joint optimization of radionuclide and external beam radiotherapy

Described herein are methods for beam station delivery of radiation treatment, where the patient platform is moved to a series of discrete patient platform locations or beam stations that are determined during treatment planning, stopped at each of these locations while the radiation source rotates about the patient delivering radiation to the target regions that intersect the radiation beam path, and then moving to the next location after the prescribed dose of radiation (e.g., in accordance with a calculated fluence map) for that location has been delivered to the patient.

Beam station treatment planning and radiation delivery methods

Disclosed herein are methods for patient setup and patient target region localization for the irradiation of multiple patient target regions in a single treatment session. Virtual localization is a method that can be used to register a patient target region without requiring that the patient is physically moved using the patient platform. Instead, the planned fluence is updated to reflect the current location of the patient target region by selecting a localization reference in the localization image, calculating a localization function based on the localization reference point, and calculating the delivery fluence by convolving the localization function with a shift-invariant firing filter. Mosaic multi-target localization partitions a planned fluence map for multiple patient target regions into sub-regions that can be individually localized. De-coupled multi-target localization involves generating a separate planned fluence map for each target but constraining a cumulative fluence map to ensure dosimetric goals are met.

Multi-target treatment planning and delivery and virtual localization for radiation therapy

Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Patient anatomy and task specific automatic exposure control in computed tomography

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Sunnyvale, CA, United States of America

Debashish Pal