AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Preliminary phantom study of four-dimensional computed tomographic angiography for renal artery mapping: Low-tube voltage and low-contrast volume imaging with deep learning-based reconstruction.
Radiography (London, England : 1995)
Mar 26, 2025
INTRODUCTION: A hybrid angio-CT system with 320-row detectors and deep learning-based reconstruction (DLR), provides additional imaging via 4D-CT angiography (CTA), potentially shortening procedure time and reducing DSA acquisitions, contrast media, ...
Foundation Model and Radiomics-Based Quantitative Characterization of Perirenal Fat in Renal Cell Carcinoma Surgery.
Academic radiology
Mar 24, 2025
RATIONALE AND OBJECTIVES: To quantitatively characterize the degree of perirenal fat adhesion using artificial intelligence in renal cell carcinoma.
Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.
BMC medical imaging
Mar 24, 2025
Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diagnosis from chest X-rays is critical yet challenging due to subtle manifestations of TB, particularly...
Technical implications of a novel deep learning system in the segmentation and evaluation of computed tomography angiography before transcatheter aortic valve replacement.
Therapeutic advances in cardiovascular disease
Mar 24, 2025
OBJECTIVE: The goal of this study was to compare the computed tomography angiography scans of the segmentation results from the Cvpilot, 3mensio, and Volume Viewer systems to explore the practicability of the Cvpilot system in the automatic segmentat...
Transcatheter Aortic Valve Replacement
Aortography
Deep Learning
Reproducibility of Results
Aortic Valve Stenosis
Predictive Value of Tests
Humans
Aged
Aortic Valve
Radiographic Image Interpretation, Computer-Assisted
Retrospective Studies
Aged, 80 and over
Multidetector Computed Tomography
Male
Female
Computed Tomography Angiography
Deep Guess acceleration for explainable image reconstruction in sparse-view CT.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 21, 2025
Sparse-view Computed Tomography (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging. Reconstructions based on the traditional Filtered Back Projection algorithm suffer from severe artifacts due to sparse data. In c...
Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model.
La Radiologia medica
Mar 18, 2025
PURPOSE: Low-dose CT protocols are widely used for emergency imaging, follow-ups, and attenuation correction in hybrid PET/CT and SPECT/CT imaging. However, low-dose CT images often suffer from reduced quality depending on acquisition and patient att...
Segment Like A Doctor: Learning reliable clinical thinking and experience for pancreas and pancreatic cancer segmentation.
Medical image analysis
Mar 13, 2025
Pancreatic cancer is a lethal invasive tumor with one of the worst prognosis. Accurate and reliable segmentation for pancreas and pancreatic cancer on computerized tomography (CT) images is vital in clinical diagnosis and treatment. Although certain ...
Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography.
Academic radiology
Mar 12, 2025
BACKGROUND: Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessment of volumetric bone mineral density (vBMD) and the diagnosis of osteoporosis (OP).
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...
Non-invasive derivation of instantaneous free-wave ratio from invasive coronary angiography using a new deep learning artificial intelligence model and comparison with human operators' performance.
The international journal of cardiovascular imaging
Mar 10, 2025
Invasive coronary physiology is underused and carries risks/costs. Artificial Intelligence (AI) might enable non-invasive physiology from invasive coronary angiography (CAG), possibly outperforming humans, but has seldom been explored, especially for...
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