AIMC Topic: Radiation Dosage
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Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301.
Radiation protection dosimetry
Nov 18, 2024
This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear ...
Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.
Korean journal of radiology
Sep 1, 2024
OBJECTIVE: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone.
EDRAM-Net: Encoder-Decoder with Residual Attention Module Network for Low-dose Computed Tomography Reconstruction.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
The medical application of Computed Tomography (CT) is to provide detailed anatomical structures of patients without the need for invasive procedures like surgery, which is very useful for clinicians in disease diagnosis. Excessive radiation exposure...
Rapid assessment of cosmic radiation exposure in aviation based on BP neural network method.
Radiation protection dosimetry
Jun 18, 2024
Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The mu...
Dark-Blood Computed Tomography Angiography Combined With Deep Learning Reconstruction for Cervical Artery Wall Imaging in Takayasu Arteritis.
Korean journal of radiology
Apr 1, 2024
OBJECTIVE: To evaluate the image quality of novel dark-blood computed tomography angiography (CTA) imaging combined with deep learning reconstruction (DLR) compared to delayed-phase CTA images with hybrid iterative reconstruction (HIR), to visualize ...
Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT.
Radiology. Artificial intelligence
Mar 1, 2024
Purpose To compare the image quality and diagnostic capability in detecting malignant liver tumors of low-dose CT (LDCT, 33% dose) with deep learning-based denoising (DLD) and standard-dose CT (SDCT, 100% dose) with model-based iterative reconstructi...
Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm.
Journal of X-ray science and technology
Jan 1, 2024
PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources.
Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.
Journal of X-ray science and technology
Jan 1, 2024
OBJECTIVES: To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive s...
An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction.
Current medical imaging
Jan 1, 2024
BACKGROUND: Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically.
Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction.
Current medical imaging
Jan 1, 2024
BACKGROUND: Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial.
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