AIMC Topic: Radiation Dosage
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Feasibility of two-dimensional dose distribution deconvolution using convolution neural networks.
Medical physics
Nov 6, 2019
PURPOSE: The purpose of this study was to investigate the feasibility of two-dimensional (2D) dose distribution deconvolution using convolutional neural networks (CNNs) instead of an analytical approach for an in-house scintillation detector that has...
A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation.
Scientific reports
Oct 16, 2019
In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus making active motion management an essential step in thoracic and abdominal tumor treatment. In curr...
Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.
Medical physics
Oct 10, 2019
PURPOSE/OBJECTIVE(S): Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution ...
Evaluation of an Artificial Intelligence-Based 3D-Angiography for Visualization of Cerebral Vasculature.
Clinical neuroradiology
Oct 9, 2019
PURPOSE: The three-dimensional digital subtraction angiography (3D DSA) technique is the current standard and is based on both mask and fill runs to enable the subtraction technique. Artificial intelligence (AI)-based 3D angiography (3DA) was develop...
Two stage residual CNN for texture denoising and structure enhancement on low dose CT image.
Computer methods and programs in biomedicine
Oct 8, 2019
BACKGROUND AND OBJECTIVE: X-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiation have attracted the attention of the academic community widely. Reducing radiation dose results in...
Computationally efficient deep neural network for computed tomography image reconstruction.
Medical physics
Sep 21, 2019
PURPOSE: Deep neural network-based image reconstruction has demonstrated promising performance in medical imaging for undersampled and low-dose scenarios. However, it requires large amount of memory and extensive time for the training. It is especial...
Artificial intelligence and breast screening: French Radiology Community position paper.
Diagnostic and interventional imaging
Sep 12, 2019
The objective of this article was to evaluate the evidence currently available about the clinical value of artificial intelligence (AI) in breast imaging. Nine experts from the disciplines involved in breast disease management - including physicists ...
3D cone-beam CT of the ankle using a novel twin robotic X-ray system: Assessment of image quality and radiation dose.
European journal of radiology
Sep 7, 2019
PURPOSE: To evaluate image quality (IQ) and radiation dose in cone-beam computed tomography (CBCT) of the ankle using a novel twin robotic X-ray system.
Physics-driven learning of x-ray skin dose distribution in interventional procedures.
Medical physics
Sep 6, 2019
PURPOSE: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to...
The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction.
Medical physics
Aug 27, 2019
PURPOSE: An important challenge for deep learning models is generalizing to new datasets that may be acquired with acquisition protocols different from the training set. It is not always feasible to expand training data to the range encountered in cl...
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