- 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.
Dosevolume Histogram And Dose Distribution Based Autoplanning
- Austin TX, US Mingli Chen - Coppell TX, US Steve Jiang - Southlake TX, US Weiguo Lu - Coppell TX, US
Assignee:
The Board of Regents of the University of Texas System - Austin TX
International Classification:
G16H 20/40 A61N 5/10
Abstract:
A method and system for generating a voxel-based quadratic penalty model for automatic intensity modulated radiation therapy (IMRT) treatment planning are disclosed herein. A computing system generates an initial assignment of threshold values to a penalty function for IMRT treatment planning The computing system receives an update to a dose value associated with the IMRT treatment planning The computing system dynamically updates the threshold values based on the updated dose value. The computing system continues to iterate the threshold values based on further updated dose values.
Methods, Apparatuses, And Systems For Creating A Patient-Specific Soft Bolus For Radiotherapy Treatment
- Austin TX, US Xuejun GU - Dallas TX, US Jun TAN - Dallas TX, US Bo ZHAO - Dallas TX, US Troy LONG - Dallas TX, US Weiguo LU - Dallas TX, US Tobin STROM - Dallas TX, US Kenneth WESTOVER - Dallas TX, US Steve B. JIANG - Dallas TX, US
International Classification:
A61N 5/10
Abstract:
Methods, apparatuses, systems, and implementations for creating a patient-specific soft bolus for radiotherapy treatment are disclosed. 2D and/or 3D images of a desired radiotherapy treatment site may be acquired, such as the head, neck, skin, breast, anus, and/or vulva. A user may interact with one or more representations of the images via an interactive user interface such as a graphical user interface (GUI). The images may include target/avoidance structures and radiation beam arrangement. The user may interact with the images to create a visualization of a patient-specific bolus. The visualization and properties of the bolus may be modified as the user manipulates aspects of the image. Data corresponding to the bolus models may be used to create 3D printed negative molds of the bolus model using 3D printing technology. A soft patient-specific bolus may be cast using the 3D printed model.
Radiation Therapy Systems That Include Primary Radiation Shielding, And Modular Secondary Radiation Shields
- Austin TX, US Steve Jiang - Dallas TX, US Robert Timmerman - Westlake TX, US Arnold Pompos - Irving TX, US
Assignee:
The Board of Regents of the University of Texas System - Austin TX
International Classification:
A61B 6/10 A61B 6/04 G21F 3/00
Abstract:
Radiation therapy systems and their components, including secondary radiation shields. At least some versions of the disclosed systems combine a radiation delivery device, a primary radiation shielding device, and a secondary shielding layer into an integrated, modular unit. This is accomplished by using a small direct beam shield capable of blocking a primary beam from a radiation delivery device. In turn, a thinner shielding layer can be used to surround the radiation delivery device and primary shielding device, enabling a single modular unit to be delivered to an installation site. In some embodiments, a bed may be disposed within the secondary shielding layer. In some embodiments, the system is configured to provide up to 4-pi (4π) steradians of radiation coverage to the bed from the radiation delivery device.