AIMC Topic: Radiographic Image Enhancement
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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...
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.
Aged
Male
Middle Aged
Deep Learning
Retrospective Studies
Tomography, X-Ray Computed
Signal-To-Noise Ratio
Female
Humans
Computed Tomography Angiography
Iodine
Myocardial Perfusion Imaging
Contrast Media
Radiographic Image Enhancement
Radiographic Image Interpretation, Computer-Assisted
Radiation Dosage
Deep Learning for Contrast Enhanced Mammography - A Systematic Review.
Academic radiology
Dec 5, 2024
BACKGROUND/AIM: Contrast-enhanced mammography (CEM) is a relatively novel imaging technique that enables both anatomical and functional breast imaging, with improved diagnostic performance compared to standard 2D mammography. The aim of this study is...
Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.
Japanese journal of radiology
Nov 14, 2024
PURPOSE: To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hy...
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