AIMC Topic: Radiotherapy Dosage
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Deep-learning-assisted algorithm for catheter reconstruction during MR-only gynecological interstitial brachytherapy.
Journal of applied clinical medical physics
Dec 10, 2021
Magnetic resonance imaging (MRI) offers excellent soft-tissue contrast enabling the contouring of targets and organs at risk during gynecological interstitial brachytherapy procedure. Despite its advantage, one of the main obstacles preventing a tran...
Prospective machine learning CT quantitative evaluation of idiopathic pulmonary fibrosis in patients undergoing anti-fibrotic treatment using low- and ultra-low-dose CT.
Clinical radiology
Dec 7, 2021
AIM: To compare the machine learning computed tomography (CT) quantification tool, Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) to pulmonary function testing (PFT) in assessing idiopathic pulmonary fibrosis (IPF) for...
Deep learning-based 3Ddose reconstruction with an electronic portal imaging device for magnetic resonance-linear accelerators: a proof of concept study.
Physics in medicine and biology
Dec 2, 2021
To develop a novel deep learning-based 3Ddose reconstruction framework with an electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs).The proposed method directly back-projected 2D portal dose into 3D patient c...
Intentional deep overfit learning (IDOL): A novel deep learning strategy for adaptive radiation therapy.
Medical physics
Nov 30, 2021
PURPOSE: Applications of deep learning (DL) are essential to realizing an effective adaptive radiotherapy (ART) workflow. Despite the promise demonstrated by DL approaches in several critical ART tasks, there remain unsolved challenges to achieve sat...
A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer.
Scientific reports
Nov 26, 2021
Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large ...
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...
Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy.
Radiation oncology (London, England)
Oct 14, 2021
OBJECTIVE: To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.
Phantoms, Imaging
Deep Learning
Humans
Organs at Risk
Neural Networks, Computer
Lung Neoplasms
Cone-Beam Computed Tomography
Radiotherapy Planning, Computer-Assisted
Prognosis
Bone Neoplasms
Image Processing, Computer-Assisted
Tomography, X-Ray Computed
Radiotherapy Dosage
Soft Tissue Neoplasms
Radiotherapy, Intensity-Modulated
Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy.
Radiation oncology (London, England)
Oct 14, 2021
PURPOSE: To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts.
Deep learning-augmented radioluminescence imaging for radiotherapy dose verification.
Medical physics
Oct 5, 2021
PURPOSE: We developed a novel dose verification method using a camera-based radioluminescence imaging system (CRIS) combined with a deep learning-based signal processing technique.
Outcome-based multiobjective optimization of lymphoma radiation therapy plans.
The British journal of radiology
Sep 30, 2021
At its core, radiation therapy (RT) requires balancing therapeutic effects against risk of adverse events in cancer survivors. The radiation oncologist weighs numerous disease and patient-level factors when considering the expected risk-benefit ratio...
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