Radiology

Utility of breath-hold half-Fourier single-shot turbo spin-echo with deep learning-based reconstruction for acquiring fat-suppressed T2-weighted images of the pancreas.

PURPOSE: To determine the best technique for acquiring fat-suppressed T2-weighted images (FS-T2WI) of the pancreas, in terms of acquisition time and image quality, among conventional respiratory-gated turbo spin echo (RG-TSE), research breath-hold TSE with deep learning-based reconstruction (BH-DL-TSE), and research breath-hold half-Fourier single-shot turbo spin-echo with deep learning-based reco...

A Deep Learning Model for Artery of Adamkiewicz Detection in Bronchial Artery Embolization: A Multicenter Retrospective Study.

PURPOSE: To evaluate the effectiveness of a deep learning model for recognizing the artery of Adamkiewicz and anterior spinal artery (ASA) to prevent spinal artery non-target embolization during bronchial artery embolization (BAE). MATERIALS AND METHODS: This multicenter, retrospective study included 2036 patients from January 2019 to December 2023 with hemoptysis who underwent de novo BAE. A deep...

Poincaré feature-based classification of electroencephalography signals for multiple sclerosis diagnosis.

BACKGROUND: As one of the most widespread neurodegenerative disorders, Multiple Sclerosis (MS) is a progressive neuroinflammatory disorder affecting m...

Financial impact of deep learning reconstruction in magnetic resonance imaging: experiences after widespread deployment.

PURPOSE: To evaluate the impact of deep learning reconstruction (DLR) on MRI productivity at a tertiary care academic hospital, and to validate a prev...

Connectome-based prediction of problematic use of social media in adolescents: Findings from the ABCD study.

Problematic use of social media (PUSM) is a major public health concern estimated to affect 35% of adolescents. However, data-driven research to ident...

Correlation between the high-density lipoprotein-to-low-density lipoprotein ratio and contrast-induced acute kidney injury: an analysis based on the in-hospital AKI electronic monitoring system.

BACKGROUND: Contrast-induced acute kidney injury (CI-AKI) is a common and serious complication that can occur following angiography procedures. Consid...

Lean mass loss in the intensive care unit and its restoration: A narrative review.

The preservation of lean mass (LM) and its restoration following catabolic loss represents a primary challenge for clinical nutrition in critically il...

Machine Learning Integration of MRI Intratumoral and Peritumoral Radiomics Features for Predicting PNSTs Postoperative Complications.

RATIONALE AND OBJECTIVES: Postoperative complications (15-76%) substantially affect patients with peripheral nerve sheath tumors (PNSTs), yet objectiv...

Artificial intelligence as a diagnostic support tool in hysteroscopy: current evidence and clinical implications.

BACKGROUND: Hysteroscopy allows direct inspection of the uterine cavity for many conditions. Despite being widely adopted, its diagnostic accuracy lar...

Diagnostic Accuracy of Artificial Intelligence Models in Computed Tomography Interpretation of Chronic Rhinosinusitis: A Systematic Review.

BACKGROUND: Chronic rhinosinusitis (CRS) is a prevalent condition frequently evaluated using computed tomography (CT). The application of artificial i...

Advancing Coronary Risk Assessment Through Combined Radiomic, Mechanical, and Hemodynamic Analysis.

PURPOSE: Detecting vulnerable coronary plaques through coronary computed tomography angiography (CCTA) is a crucial, yet challenging task. To date, mo...

Integrating B-mode ultrasound radiomics and clinical variables to predict 26-week progression-free survival in advanced hepatocellular carcinoma under targeted immunotherapy.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality globally, with over 905,700 new cases and 830,200 de...

Can AI write reports like a radiologist? A blinded evaluation of large language model-generated lumbar spine MRI reports.

BACKGROUND: To compare the quality and clinical usefulness of large language model (LLM)-generated lumbar spine magnetic resonance imaging (MRI) repor...

Comparison of respiratory-gated and breath‑hold accelerated T2-weighted sequences for liver MRI with deep learning reconstruction.

BACKGROUND: T2-weighted imaging (T2WI) of the liver suffers from prolonged scan times and respiratory motion artifacts. Deep learning (DL)-based recon...

Navigated hepatic tumor resection using intraoperative ultrasound imaging.

PURPOSE: This proof-of-concept study evaluates the feasibility and accuracy of an ultrasound-based navigation system for open liver surgery. Unlike mo...

Acute deep neck infection MRI: deep learning segmentation and clinical relevance of retropharyngeal edema volume.

OBJECTIVE: Retropharyngeal edema (RPE) on MRI in patients with acute neck infection is associated with disease severity. We explored the potential rol...

Evaluation of GPT-5 for Esophageal Cancer Staging Using Fluorodeoxyglucose Positron Emission Tomography Maximum-Intensity Projection Images: Comparative Pilot Study.

BACKGROUND: Accurate esophageal cancer staging relies on 18F fluorodeoxyglucose positron emission tomography (18F FDG-PET), but its interpretation is ...

Physics-Informed Deep Learning for Shear Wave Speed Estimation in MR Elastography.

OBJECTIVE: Magnetic Resonance Elastography (MRE) is a non-invasive imaging technique for mapping biomechanical properties of in vivo tissue, including...

Biomechanics-informed Non-rigid Medical Image Registration with Elasticity Theories.

Biomechanical modelling of soft tissue provides a method for constraining medical image registration, such that the estimated spatial transformation i...

Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks.

Representing signals using coordinate networks dominates the area of inverse problems recently, and is widely applied in various scientific computing ...