AIMC Topic: Radiographic Image Enhancement
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Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area.
Radiological physics and technology
Apr 12, 2023
This study aimed to determine the optimal radiographic conditions for detecting lesions on digital chest radiographs using an indirect conversion flat-panel detector with a copper (Cu) filter. First, we calculated the effective detective quantum effi...
[Relationship between Image Quality and Reconstruction FOV in Deep Learning Reconstructed Images of CT].
Nihon Hoshasen Gijutsu Gakkai zasshi
Sep 6, 2022
In this study, we compared the image quality of deep learning reconstruction (DLR) with that of conventional image reconstruction methods under the same conditions of reconstruction FOV and acquisition dose assuming abdomen computed tomography (CT) i...
DR-only Carbon-ion radiotherapy treatment planning via deep learning.
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)
Jul 4, 2022
PURPOSE: To evaluate the feasibility of patient-specific digital radiography (DR)-only treatment planning for carbon ion radiotherapy in anthropomorphic thorax-and-abdomen phantom and head-and-neck patients.
Two-phase learning-based 3D deblurring method for digital breast tomosynthesis images.
PloS one
Jan 24, 2022
In digital breast tomosynthesis (DBT) systems, projection data are acquired from a limited number of angles. Consequently, the reconstructed images contain severe blurring artifacts that might heavily degrade the DBT image quality and cause difficult...
Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network.
Computational and mathematical methods in medicine
Jan 4, 2022
Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening tech...
Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.
The British journal of radiology
Dec 15, 2021
OBJECTIVES: The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric a...
Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results.
Clinical radiology
Nov 12, 2021
AIM: To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (...
Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm.
Computational and mathematical methods in medicine
Nov 8, 2021
A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammog...
A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.
European radiology
Sep 22, 2021
OBJECTIVES: To develop a deep-learning (DL) model for identifying fresh VCFs from digital radiography (DR), with magnetic resonance imaging (MRI) as the reference standard.
A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis.
Scientific reports
May 13, 2021
Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in ...
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