Radiology

Development of a Real-time Radiation Detection System that Uses IoT to Provide Real-time Monitoring in Custom-built Website.

Effective radiation monitoring is crucial for ensuring public security and safety, particularly in the event of nuclear (e.g., nuclear accident, fallout, etc.) or radiological (e.g., radiation source spill, dirty bombs, etc.) emergencies. Traditional monitoring methods often lack real-time capabilities and comprehensive data visualization and analysis, which can delay response time and increase fu...

Clinical feasibility test of 60 kVp double-low-dose coronary CT angiography with a deep learning reconstruction algorithm.

OBJECTIVES: To test the feasibility of 60 kVp double-low-dose coronary CT angiography (CCTA) with a deep learning reconstruction (DLR) algorithm. MATERIALS AND METHODS: Eighty-nine patients (44 females, 59.9 ± 13.2 years, 23.1 ± 3.3 kg/m2) with known or suspected coronary artery disease were prospectively enrolled. Each patient underwent the double-low-dose CCTA (60-kVp, 28 mL contrast at 2.5 mL/s...

Artificial intelligence-derived transition zone PSA density as a triage tool to reduce unnecessary prostate systematic biopsies in MRI-negative men.

OBJECTIVES: The study aimed to assess the predictive performance of transition zone PSA density (TZ-PSAD) compared to conventional PSA density (PSAD) ...

Single-Stage Lesion Identification in $^{68}$Ga-DOTATATE PET Images.

OBJECTIVE: Positron emission tomography (PET) is a commonly used imaging modality for assessment of neuroendocrine tumors (NETs), and lesion identific...

Sharper than human eyes? A systematic review and meta-analysis of machine learning for retinal detachment detection.

Retinal detachment (RD) is a sight-threatening condition requiring rapid diagnosis to prevent vision loss. Artificial intelligence (AI), particularly ...

A Comparative Evaluation of 7T MRI for Epilepsy with Deep-Learning-Based Image Reconstruction and Dynamic Parallel Transmission.

OBJECTIVES: 7T MRI enhances lesion detection in epilepsy but is limited by radiofrequency transmission field (B1+) inhomogeneity and long scan times. ...

Comprehensive 3D Optical Coherence Tomography Dataset for AMD and DME: Facilitating Deep-Learning-Based 3D Segmentation.

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are vision-threatening pathologies for which optical coherence tomography (OCT...

Hemodynamics-driven magnetoelastic vascular grafts for stenosis diagnosis.

Conventional approaches for vascular graft stenosis diagnostics, including X-ray angiography, magnetic resonance imaging and Doppler ultrasound, altho...

Predicting homologous recombination deficiency and treatment responses using a computed tomography-based foundation model: a preclinical study.

Homologous recombination deficiency (HRD) can lead to genomic instability, increased cancer susceptibility, and enhanced sensitivity to DNA-targeting ...

Automated deep learning pipeline for measuring lumbar thecal sac AP diameter on mid-sagittal MR images.

OBJECTIVE: To develop and validate an automated, disc-level deep learning pipeline for quantitative measurement of anteroposterior (AP) thecal sac dia...

Health-oriented sleep states: making sleep states reflect health conditions.

STUDY OBJECTIVES: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by th...

MS-STFNN: A multi-scale spatio-temporal fusion neural network for fMRI-based depression diagnosis.

Major depressive disorder (MDD) is a common mental disorder. Current clinical diagnosis primarily relies on subjective scales, underscoring an urgent ...

Magnetic resonance imaging (MRI) of the myometrium - benign and malignant disease.

Myometrial lesions are a common indication for pelvic imaging, with accurate characterisation crucial for guiding management. While ultrasonography re...

Artificial intelligence-powered automatic coronary computed tomography angiography plaque quantification: comparison against optical coherence tomography.

AIMS: Coronary computed tomography angiography (CCTA) enables a non-invasive, comprehensive assessment of coronary artery disease, and artificial inte...

Lesion network mapping in pediatric epilepsy.

OBJECTIVE: To describe the current use, limitations, and future directions of lesion network mapping in pediatric epilepsy. METHODS: Narrative review ...

Development of a hybrid deep learning-based framework for liver fibrosis classification using ultrasound images.

BACKGROUND AND AIMS: Liver fibrosis is a progressive accumulation of extracellular matrix proteins with distortion of hepatic architecture and can pro...

Renal graft imaging: An update and overview.

Imaging of renal graft function and pathology, including delayed graft function (DGF), often requires a multimodal approach. The existing literature d...

Deep Learning Reconstruction Combined with Contrast-Enhancement Boost Technique in "Quadruple-low" CCTA Protocol: Evaluation of Image Quality and Diagnostic Accuracy.

RATIONALE AND OBJECTIVES: To evaluate the impact of a deep learning reconstruction (DLR) algorithm combined with contrast-enhancement boost (CE-boost)...

Coronary revascularization: a long-term perspective.

This paper will forecast advancements in coronary revascularization by 2040, drawing on historical trends and recent breakthroughs. Having forecasted ...

Current and future use of artificial intelligence in valvular heart disease imaging.

Valvular heart disease (VHD) remains significantly underdiagnosed and undertreated. This review examines an artificial intelligence (AI)-enhanced 'spo...