AIMC Topic: Radiographic Image Enhancement
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Feasibility study of "double-low" scanning protocol combined with artificial intelligence iterative reconstruction algorithm for abdominal computed tomography enhancement in patients with obesity.
BMC medical imaging
Jul 9, 2025
OBJECTIVE: To evaluate the efficacy of the "double-low" scanning protocol combined with the artificial intelligence iterative reconstruction (AIIR) algorithm for abdominal computed tomography (CT) enhancement in obese patients and to identify the opt...
An artificial intelligence model for predicting an appropriate mAs with target exposure indicator for chest digital radiography.
Scientific reports
Apr 8, 2025
In digital radiography, image quality is synergistically affected by anatomy-specific examinations, exposure factors, body parameters, detector types, and vendors/systems. However, estimating appropriate exposure factors before radiography with optim...
Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation.
Japanese journal of radiology
Apr 7, 2025
PURPOSE: To evaluate the impact of an AI-based, noise reduction technique for compensation of image degradation on pediatric and neonatal chest and abdomen radiography using a visual grading analysis.
Semi-supervised spatial-frequency transformer for metal artifact reduction in maxillofacial CT and evaluation with intraoral scan.
European journal of radiology
Mar 31, 2025
PURPOSE: To develop a semi-supervised domain adaptation technique for metal artifact reduction with a spatial-frequency transformer (SFTrans) model (Semi-SFTrans), and to quantitatively compare its performance with supervised models (Sup-SFTrans and ...
Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement.
Japanese journal of radiology
Mar 12, 2025
PURPOSE: Myocardial computed tomography (CT) late enhancement (LE) allows assessment of myocardial scarring. Super-resolution deep learning image reconstruction (SR-DLR) trained on data acquired from ultra-high-resolution CT may improve image quality...
Adaptive enhancement of shoulder x-ray images using tissue attenuation and type-II fuzzy sets.
PloS one
Feb 6, 2025
Shoulder X-ray images typically have low contrast and high noise levels, making it challenging to distinguish and identify subtle anatomical structures. While existing image enhancement techniques are effective in improving contrast, they often overl...
Feature-targeted deep learning framework for pulmonary tumorous Cone-beam CT (CBCT) enhancement with multi-task customized perceptual loss and feature-guided CycleGAN.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 26, 2025
Thoracic Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for lung cancer treatments. However, CBCT images often suffer from streaking artifacts an...
Automatic Vertical Root Fracture Detection on Intraoral Periapical Radiographs With Artificial Intelligence-Based Image Enhancement.
Dental traumatology : official publication of International Association for Dental Traumatology
Jan 19, 2025
BACKGROUND/AIM: To explore transfer learning (TL) techniques for enhancing vertical root fracture (VRF) diagnosis accuracy and to assess the impact of artificial intelligence (AI) on image enhancement for VRF detection on both extracted teeth images ...
A Radiomic-Clinical Model of Contrast-Enhanced Mammography for Breast Cancer Biopsy Outcome Prediction.
Academic radiology
Jan 10, 2025
RATIONALE AND OBJECTIVES: In the USA over 1 million breast biopsies are performed annually. Approximately 9.6% diagnostic exams were given Breast Imaging Reporting and Data System (BI-RADS) ≥4A, most of which are 4A/4B. Contrast-enhanced mammography ...
Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.
European radiology
Dec 20, 2024
OBJECTIVES: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.
Tomography, X-Ray Computed
Female
Aged
Male
Humans
Retrospective Studies
Radiographic Image Interpretation, Computer-Assisted
Radiation Dosage
Signal-To-Noise Ratio
Computed Tomography Angiography
Middle Aged
Deep Learning
Iodine
Contrast Media
Myocardial Perfusion Imaging
Radiographic Image Enhancement
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