AIMC Topic: Radiation Dosage
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Low-dose PET image noise reduction using deep learning: application to cardiac viability FDG imaging in patients with ischemic heart disease.
Physics in medicine and biology
Feb 25, 2021
INTRODUCTION: Cardiac [F]FDG-PET is widely used for viability testing in patients with chronic ischemic heart disease. Guidelines recommend injection of 200-350 MBq [F]FDG, however, a reduction of radiation exposure has become increasingly important,...
Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography.
The British journal of radiology
Feb 24, 2021
OBJECTIVES: To compare the image quality of low-dose CT urography (LD-CTU) using deep learning image reconstruction (DLIR) with conventional CTU (C-CTU) using adaptive statistical iterative reconstruction (ASIR-V).
A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks.
Physics in medicine and biology
Feb 24, 2021
Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinic...
Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection.
The British journal of radiology
Feb 22, 2021
OBJECTIVES: To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR).
Postoperative Period
Whole Body Imaging
Middle Aged
Humans
Neoplasms
Algorithms
Deep Learning
Contrast Media
Aged
Radiographic Image Interpretation, Computer-Assisted
Aged, 80 and over
Quality Control
Adult
Radiation Dosage
Male
Signal-To-Noise Ratio
Prospective Studies
Tomography, X-Ray Computed
Female
Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction.
European radiology
Feb 8, 2021
OBJECTIVE: To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen.
Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T-weighted Contrast-enhanced Imaging.
BMC medical imaging
Feb 3, 2021
BACKGROUND: Based on conventional MRI images, it is difficult to differentiatepseudoprogression from true progressionin GBM patients after standard treatment, which isa critical issue associated with survival. The aim of this study was to evaluate th...
A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effects of high absorbed doses are relatively well known, the effects of low absorbed doses are still deb...
Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction.
Japanese journal of radiology
Jan 15, 2021
PURPOSE: To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique.
DaNet: dose-aware network embedded with dose-level estimation for low-dose CT imaging.
Physics in medicine and biology
Jan 13, 2021
Many deep learning (DL)-based image restoration methods for low-dose CT (LDCT) problems directly employ the end-to-end networks on low-dose training data without considering dose differences. However, the radiation dose difference has a great impact ...
Deep learning-based reconstruction in ultra-high-resolution computed tomography: Can image noise caused by high definition detector and the miniaturization of matrix element size be improved?
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)
Jan 13, 2021
PURPOSE: This study aimed to assess the noise characteristics of ultra-high-resolution computed tomography (UHRCT) with deep learning-based reconstruction (DLR).
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