AIMC Topic: Proton Therapy
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Toward automatic beam angle selection for pencil-beam scanning proton liver treatments: A deep learning-based approach.
Medical physics
May 11, 2022
BACKGROUND: Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning, however, poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam a...
Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy.
Physics in medicine and biology
May 9, 2022
Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present ...
Towards machine learning aided real-time range imaging in proton therapy.
Scientific reports
Feb 17, 2022
Compton imaging represents a promising technique for range verification in proton therapy treatments. In this work, we report on the advantageous aspects of the i-TED detector for proton-range monitoring, based on the results of the first Monte Carlo...
Factors associated with long-term survival in gemcitabine-concurrent proton radiotherapy for non-metastatic locally advanced pancreatic cancer: a single-center retrospective study.
Radiation oncology (London, England)
Feb 10, 2022
BACKGROUND: Factors associated with long-term survival in gemcitabine-concurrent proton radiotherapy (GPT) for non-metastatic, locally advanced pancreatic cancer (LAPC) remain unclear. This study aimed to determine the factors associated with long-te...
Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.
Medical physics
Nov 16, 2021
PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation...
Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy.
The British journal of radiology
Oct 28, 2021
OBJECTIVE: Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. This work presents a learning-based...
Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning.
Physics in medicine and biology
Mar 16, 2021
Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been propos...
Comparison of the suitability of CBCT- and MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients.
Physics in medicine and biology
Dec 5, 2020
Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep c...
Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Nov 29, 2020
OBJECTIVE: Dose prediction using deep learning networks prior to radiotherapy might lead tomore efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess thei...
Feasibility study of range verification based on proton-induced acoustic signals and recurrent neural network.
Physics in medicine and biology
Nov 5, 2020
Range verification in proton therapy is a critical quality assurance task. We studied the feasibility of online range verification based on proton-induced acoustic signals, using a bidirectional long-short-term-memory recurrent neural network and var...
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