Radiology

New Techniques in Musculoskeletal MRI: State of the Art.

ABSTRACT: Musculoskeletal magnetic resonance imaging has evolved substantially, driven by advances in hardware, image acquisition, and reconstruction techniques. Improvements in gradient performance and dedicated radiofrequency coils have enhanced spatial resolution and scan efficiency across field strengths. Image acceleration strategies, including parallel imaging, simultaneous multislice acquis...

Exploring the utility of artificial intelligence in identifying progression of prostate cancer during active surveillance: A systematic review.

BACKGROUND: There is a recent surge in interest in the application of artificial intelligence (AI) in prostate cancer (PCa). When evaluating PSMA PET scans, AI has shown promise in detecting intraprostatic cancer and metastatic disease. This systematic review aims to assess the ability of AI to detect or predict the progression of PCa during AS. METHODS: This systematic review was registered on PR...

Robot-assisted lumbar facet joint infiltration improves accuracy and reduces radiation exposure compared to the manual technique in a comparative phantom study.

Compact robotic systems offer new opportunities for spinal procedures outside the operating room, but their potential for small-scale interventions su...

Design and validation of renal stone detection using multi-architecture feature extraction with deep sequential learning model on axial computed tomography images.

Kidney stone disease is a significant public health threat, with its prevalence escalating due to evolving dietary habits, rising rates of obesity, ot...

Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers.

Chronic kidney disease (CKD) is a complex condition where the kidneys are damaged and progressively lose their ability to filter blood, 10% of the wor...

Diagnostic Performance of a Large Language Model (ChatGPT-4o) in Chronic Rhinosinusitis CT Scan Interpretation.

BACKGROUND: Large language models (LLMs), such as ChatGPT, are increasingly utilized by physicians for clinical decision support due to their ease of ...

Deep-learning endomicroscope with large field-of-view and depth-of-field for real-time in vivo imaging of epithelial cancer hallmarks.

In vivo microscopy (IVM) has shown great promise to improve early detection of epithelial precancer, but it suffers from fundamental trade-offs that l...

Multimodal deep learning model integrating electronic medical records and CT images for gallbladder cancer diagnosis: a retrospective multicenter study in China.

OBJECTIVE: Gallbladder cancer (GBC) is a rare gastrointestinal malignancy with a global 5-year survival rate of less than 5%. Early diagnosis is chall...

Improving confidence in MRI-based auto-segmentation via uncertainty assessment.

BACKGROUND AND PURPOSE: Accurate delineation of organs of interest (OOIs, also commonly referred to as organs at risk, OARs) is crucial for safe radio...

Predicting drainage success of peritonsillar abscesses: a radiomics-based machine learning approach using contrast-enhanced computed tomography.

PURPOSE: To develop and validate radiomics-based machine learning models combined with clinical parameters derived from venous phase contrast-enhanced...

Low-Field Musculoskeletal MRI: Recent Advances, Challenges, and Practice Considerations.

Magnetic resonance imaging (MRI) is well established as a cornerstone of musculoskeletal imaging. Although most clinical musculoskeletal MRI examinati...

Assessing the Reliability of MRI and CT Scans for Liver Volume Estimation in Patients Undergoing Liver Transplantation.

OBJECTIVE: Magnetic resonance imaging (MRI) and computed tomography (CT) are commonly used to measure organ volumes, but accuracy has not been methodi...

Glass-box agentic-style workflow for multiclass cine cardiac magnetic resonance imaging classification with a large language model.

PURPOSE: To develop and evaluate a glass-box, agentic-style radiology pipeline that separates perception from reasoning for auditable multiclass diagn...

AI-driven Abdominal Aortic Calcification Extracted From Contrast-enhanced CT Is Predictive of All-cause Mortality and Cardiovascular Events in a Large Adult Population.

OBJECTIVE: To assess the utility of AI-driven quantification of abdominal aortic calcification (AAC) extracted from contrast-enhanced CT (CECT) scans ...

Brain stroke detection and classification using deep learning-based transCBAMSegNet and hybrid transformer models.

Stroke is a life-threatening neurological condition that requires rapid assessment to reduce mortality and long-term disability. Accurate lesion segme...

Evaluation of the time‑of‑flight-enhanced deep learning image reconstruction method in 18F‑FDG PET/CT for breast cancer imaging.

BACKGROUND: Breast cancer is the most frequently diagnosed cancer among women. Accurate diagnosis and effective management rely heavily on high-qualit...

Dual-Domain Multipath Self-Supervised Diffusion Model for Accelerated MRI Reconstruction.

Magnetic resonance imaging (MRI) is a vital diagnostic tool, but its inherently long acquisition times reduce clinical efficiency and patient comfort....

DynaTOF: Time-Resolved Noncontrast Cerebral MR Angiography Using Spatially Modulated RF Saturation.

PURPOSE: To develop a time-resolved extension of three-dimensional time-of-flight MR angiography, termed Dynamic TOF (DynaTOF), that exploits intrinsi...

The potential role of artificial intelligence in the diagnosis of Acanthamoeba keratitis-a scoping review.

BACKGROUND: Acanthamoeba keratitis (AK) is a severe, sight-threatening infectious disease with a rising global incidence, linked to the increasing use...

State of the Art: Scapholunate Ligament Injuries.

ABSTRACT: Scapholunate ligament injuries are the most common ligamentous injuries of the wrist and typically result from high-energy trauma, most ofte...