Radiology

SSiamese Capsule Network (SNNCap) : Cognitive Analysis for Alzheimer's Disease Classification from MRI Data.

Alzheimer's Disease (AD) detection is essential for timely treatment and better patient care. Magnetic Resonance Imaging (MRI) is a technique in which radio waves and magnetic fields are used to capture high-resolution, multi-dimensional representations of brain structures. This high-resolution imaging capability makes MRI a key tool for diagnosing neurological disorders such as Alzheimer's diseas...

Near-Real-Time Multi-Parametric Quantitative MRI using Parallel Non-Cartesian 6D Spatial-Temporal Dictionary Learning Neural Networks.

OBJECTIVE: Multi-parametric quantitative MRI (qMRI) enables precise targeting during image-guided interventions such as deep brain stimulation. To address the demand for higher temporal resolution in multi-parametric qMRI of the brain, we propose an online pipeline for near-real-time quantitative MRI. METHODS: The acquisition utilizes an alternating dual-flip-angle blipped multi-gradient-echo sequ...

Ultrafast Infant Brain Quantitative MRI Using Overlapping-Echo Acquisition with Volumetric Physical Simulation of Slice-level Non-Idealities.

OBJECTIVE: Quantitative MRI (qMRI) is sensitive to brain microstructural and metabolic changes; however, existing techniques often unsuitable for asse...

The Potency of DCLGAN for Reducing Scan time in PET/CT imaging with different radiotracers of clinical importance.

BACKGROUND AND PURPOSE: Shortening PET/CT acquisition without degrading diagnostic or quantitative performance would improve patient comfort and scann...

Detection of calcified plaques: comparison between coronary CT angiography and thin-slice non-contrast CT with deep learning-aided image registration.

OBJECTIVES: To investigate whether coronary CT angiography (CCTA) misses calcified plaques detected by thin-slice non-contrast CT (NCCT). MATERIALS AN...

Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens.

BACKGROUND: Radiologist burnout affects approximately 40% of US radiologists. Large language models (LLMs) may improve workflow efficiency, but real-w...

Cerebral Vasospasm Detection and Delayed Cerebral Ischemia Prediction after Aneurysmal Subarachnoid Hemorrhage: A Scoping Review.

BACKGROUND/OBJECTIVE: Aneurysmal subarachnoid hemorrhage (aSAH) is complicated by angiographic cerebral vasospasm and delayed cerebral ischemia (DCI),...

Interpretable predictions from whole-body FDG-PET/CT using parameters associated with clinical outcome.

BACKGROUND: Accurate prediction of clinical outcomes is challenging yet important for patient care. The aim of the study was to evaluate a deep learni...

A Graph Attention Network-Based Multimodal Auxiliary Intelligent Grading Model for Uterine Prolapse Severity.

INTRODUCTION AND HYPOTHESIS: Uterine prolapse affects women's quality of life. Traditional diagnosis relies on subjective experience with limited accu...

AI-Driven Multi-parametric MS Lesion Analysis from T2-FLAIR Imaging: a Clinical Decision Support Framework for Neuroradiology.

Artificial intelligence (AI) is transforming neuroradiological practice, yet multiple sclerosis (MS) diagnosis remains challenged by qualitative MRI a...

Prospective biopsy-controlled validation of an AI model for predicting glioblastoma infiltration: Results from the SupraGlio trial.

BACKGROUND: Glioblastoma recurrence is driven by diffuse microscopic infiltration beyond the contrast-enhancing tumour margin. GlioMap is an open-acce...

Machine learning models for predicting microvascular invasion in hepatocellular carcinoma with three-dimensional whole-lesion 18F-FDG PET radiomics.

OBJECTIVE: To estimate the performance of machine learning models based on preoperative three-dimensional whole-lesion radiomics features for predicti...

Artificial intelligence (AI)-assisted ultrasound in clinical trials: Endpoint automation, decentralized monitoring, and regulatory readiness.

Ultrasound is among the most widely used imaging modalities in clinical trials, and yet its dependence on operator skill and equipment settings has hi...

CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two dementia-related brain illnesses that are prevalent among elders in the twenty-fi...

Optimizing laboratory X-ray diffraction contrast tomography: Effects of detector binning.

In laboratory-based diffraction contrast tomography (LabDCT), pixel binning on 2D detectors is an effective strategy to reduce exposure time and impro...

GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound.

PURPOSE: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggre...

Effective elimination of respiratory misregistration-induced attenuation correction errors in PET/CT via deep learning trained on data-driven gated PET from strictly respiratory-phase-matched PET/CT cases.

Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy.

BACKGROUND: Accurate prediction of pathological complete response (pCR) after preoperative chemoradiation therapy, followed by surgery (trimodality th...

Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.

BACKGROUND: Poor cardiac MR image quality can prompt repeat examinations and hinder clinical decision-making. PURPOSE: To evaluate whether pre-imaging...

Deep learning-based segmentation of enamel, cementum, alveolar bone, and gingiva in periodontal ultrasound images.

OBJECTIVES: To develop a deep learning-based multi-class segmentation model for the simultaneous segmentation of key periodontal structures, including...