AIMC Topic: Proton Therapy

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Deep learning proton beam range estimation model for quality assurance based on two-dimensional scintillated light distributions in simulations.

Medical physics
BACKGROUND: Many studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive th...

Automated treatment planning for proton pencil beam scanning using deep learning dose prediction and dose-mimicking optimization.

Journal of applied clinical medical physics
PURPOSE: The purpose of this study is to investigate the use of a deep learning architecture for automated treatment planning for proton pencil beam scanning (PBS).

Deep learning-based protoacoustic signal denoising for proton range verification.

Biomedical physics & engineering express
Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at the end of range, which is called the Bragg peak (BP). The protoacoustic technique was developed to...

Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer.

Medical physics
BACKGROUND: In cancer care, determining the most beneficial treatment technique is a key decision affecting the patient's survival and quality of life. Patient selection for proton therapy (PT) over conventional radiotherapy (XT) currently entails co...

A deep learning-based approach for statistical robustness evaluation in proton therapy treatment planning: a feasibility study.

Physics in medicine and biology
. Robustness evaluation is critical in particle radiotherapy due to its susceptibility to uncertainties. However, the customary method for robustness evaluation only considers a few uncertainty scenarios, which are insufficient to provide a consisten...

A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.

Physics in medicine and biology
. Range uncertainty is a major concern affecting the delivery precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging is a promising technique to provide 3Drange verification. However, the conventional back-projected PG i...

SWFT-Net: a deep learning framework for efficient fine-tuning spot weights towards adaptive proton therapy.

Physics in medicine and biology
. One critical task for adaptive proton therapy is how to perform spot weight re-tuning and reoptimize plan, both of which are time-consuming and labor intensive. We proposed a deep learning framework (SWFT-Net) to speed up such a task, a starting po...

3Ddose verification in prostate proton therapy with deep learning-based proton-acoustic imaging.

Physics in medicine and biology
Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising technique, proton-acoustic (PA) imaging, has been developed to provide real-time3D dose verification. Howev...

Validation of a deep learning-based material estimation model for Monte Carlo dose calculation in proton therapy.

Physics in medicine and biology
. Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DEC...

A plan verification platform for online adaptive proton therapy using deep learning-based Monte-Carlo denoising.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND PURPOSE: This study focused on developing a fast Monte Carlo (MC) plan verification platform via a deep learning (DL)-based denoising approach. It can maintain the MC dose calculation accuracy while significantly reducing the computation t...