Ruijiang Li - Sunnyvale CA, US Steve B. Jiang - San Diego CA, US John Lewis - La Jolla CA, US Laura Cervino - La Jolla CA, US
Assignee:
THE REGENTS OF THE UNIVERSITY OF CALIFORNIA - Oakland CA
International Classification:
A61B 6/00
US Classification:
600425
Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for computer tomography (CT) sorting based on internal anatomy of patients. CT scans of anatomical features of a human are obtained as pixels. From the scans, multiple respiratory features are determined. An optimal respiratory feature is selected and a respiratory signal is generated based on the multiple CT scans.
Graphics Processing Unit-Based Fast Cone Beam Computed Tomography Reconstruction
Steve B. Jiang - San Diego CA, US Xun Jia - San Diego CA, US
Assignee:
The Regents of the University of California - Oakland CA
International Classification:
G06F 15/16 G06T 15/00 G06K 9/00 G06F 15/00
US Classification:
345419, 345501, 382131, 345502
Abstract:
Techniques, apparatus and systems are disclosed for performing graphics processor unit (GPU)-based fast cone beam computed tomography (CBCT) reconstruction algorithm based on a small number of x-ray projections. In one aspect a graphics processor unit (GPU) implemented method of reconstructing a cone beam computed tomography (CBCT) image includes receiving, at the GPU, image data for CBCT reconstruction. The GPU uses an iterative process to minimize an energy functional component of the received image data. The energy functional component includes a data fidelity term and a data regularization term. The reconstructed CBCT image is generated based on the minimized energy functional component.
Optimization Process For Volumetric Modulated Arc Therapy
Steve B. Jiang - San Diego CA, US Chunhua Men - Chesterfield MO, US Xun Jia - San Diego CA, US
Assignee:
THE REGENTS OF THE UNIVERSITY OF CALIFORNIA - Oakland CA
International Classification:
A61N 5/10
US Classification:
600 1
Abstract:
Systems, techniques, and processes are disclosed for implementing aperture-based optimization techniques. In one aspect, a method performed by an aperture-based radiation treatment system includes implementing a volumetric modulated arc therapy (VMAT) treatment plan by generating apertures at beam angles. Beam intensities of the generated apertures are determined, which can be used to control generation of a radiation dose in a radiation therapy.
- Austin TX, US Debabrata Saha - Carrollton TX, US Michael D. Story - Dallas TX, US Hak Choy - Dallas TX, US Steve Bin Jiang - Southlake TX, US
Assignee:
The Board of Regents of The University of Texas System - Austin TX
International Classification:
A61N 5/10
Abstract:
In one aspect, the present disclosure relates to a method of adaptive treatment of a subject with a tumor. The method may include administering a first pulse dose of radiation to a tumor within a subject; administering a second pulse dose of radiation to the tumor, wherein the second pulse dose is administered after an observation period, the observation period having a duration of at least 7 days; and concurrently treating the subject with an immunotherapy.
Independent Stereotactic Radiotherapy Dose Calculation And Treatment Plan Verification
- Austin TX, US Xuejun GU - Dallas TX, US Mingli CHEN - Coppell TX, US Xun JIA - Dallas TX, US Steve Bin JIANG - Southlake TX, US
Assignee:
THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM - Austin TX
International Classification:
A61N 5/10
Abstract:
The present disclosure is directed towards a treatment planning system for use in a stereotactic radiotherapy system. In particular, the disclosed systems and methods may be used for generating a treatment plan and/or verifying an existing treatment plan. Moreover, the disclosed systems and methods may be suitable for use in a clinical setting. A method for verifying a treatment plan of a stereotactic radiotherapy device may include the steps of receiving a treatment plan, generating a second treatment plan by applying a modified monte-carlo method to regions of interest in the treatment plan, and identifying discrepancies between the received treatment plan and the generated second treatment plan.
The Board of Regents of the University of Texas System - Austin TX
International Classification:
A61N 5/10 G16H 20/40 G16H 30/20
Abstract:
A method for determining a radiotherapy treatment plan can include: receiving anatomical data for a patient; generating, via a neural network analyzing the anatomical data, a plurality of fitness values for a plurality of candidate beam orientations; determining a selected beam orientation based on the plurality of fitness values; performing a fluence map optimization (FMO) process on the selected beam orientation; and determining a dose distribution for the patient based on the FMO process.
A system for MRI-guided radiotherapy is disclosed herein. The system includes a radiotherapy apparatus in the form of a linear accelerator or heavy ion system, an MRI portion, and a patient platform. The linear accelerator portion includes a stand, a gantry coupled to the stand, and a treatment head. The gantry is configured to rotate about the stand. The treatment head is coupled to the gantry. The treatment head is configured to deliver a radiotherapy beam. A system for MRI-guided radiotherapy is disclosed herein. The system includes a radiotherapy portion and an MRI portion adjacent to the radiotherapy portion. The MRI portion includes a magnet configured to generate an inhomogeneous magnetic field.
Deep Learning Based Dosed Prediction For Treatment Planning And Quality Assurance In Radiation Therapy
The Board of Regents of the University of Texas System - Austin TX
International Classification:
G16H 20/10 G06N 3/04 G06N 3/08 G06N 5/04
Abstract:
A method and system for generating a treatment plan are disclosed herein. A computing system receives a plurality of dose volume histograms for a plurality of patients and a plurality of volumetric dose distributions corresponding to the plurality of dose volume histograms. The computing system generates a volumetric dose prediction model using a neural network by learning, by the neural network, a relationship between a plurality of dose volume histograms for the plurality of patients and the corresponding plurality of volumetric dose distributions. The computing system receives a candidate dose volume histogram for a target patient. The computing system infers, via the volumetric dose prediction module, a volumetric dose prediction distribution matching the candidate dose volume histogram. The computing system generates a recommendation based on the inferred volumetric dose prediction distribution.