PURPOSE: To evaluate the impact of deep learning-based post-hoc noise reduction (DLNR) on image quality, coronary artery disease reporting and data system (CAD-RADS) assessment, and diagnostic performance in quarter-dose versus full-dose coronary CT ...
RATIONALE AND OBJECTIVES: Timely and accurate classification of bacterial pneumonia (BP) is essential for guiding antibiotic therapy. However, distinguishing BP from non-bacterial pneumonia (NBP) using computed tomography (CT) is challenging due to o...
OBJECTIVES: To evaluate the diagnostic performance of lumbar spine CT using deep learning denoising (DLD CT) for detecting disc herniation and spinal stenosis.
OBJECTIVE: Clinical staging on CT has several biases, and a radiogenomics approach could be proposed. The study aimed to test the performance of a radiogenomics approach in identifying high-risk colon cancer.
Accurate segmentation of mammographic mass is very important as shape characteristics of these masses play a significant role for radiologist to diagnose benign and malignant cases. Recently, various deep learning segmentation algorithms have become ...
OBJECTIVE: To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans.
AJNR. American journal of neuroradiology
Jun 3, 2025
BACKGROUND AND PURPOSE: Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating the use of smoother reconstruction kernels that reduce resolution below the 0.125-mm maximum spatial re...
RATIONALE AND OBJECTIVES: This prospective study evaluated the performance of AI in a diagnostic clinic setting, comparing its effectiveness with radiologists of varying experience.
OBJECTIVE: This study aimed to construct a novel model, Multi-Spatial Attention U-Net (MSAU-Net) by incorporating our proposed Multi-Spatial Attention (MSA) block into the U-Net for the automated segmentation of the gallbladder on CT images.
RATIONALE AND OBJECTIVES: This study aims to analyze the intratumoral and peritumoral characteristics of lung adenocarcinoma patients on the basis of chest CT images via radiomic and deep learning methods and to develop and validate a multimodel fusi...
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