Radiology

Enhancing mesenteric vascular imaging with dual-energy CTA: a comparison of DLIR and ASIR-V using low contrast agent and low radiation dose protocols.

BACKGROUND: Timely diagnosis of mesenteric vascular diseases, especially acute mesenteric ischemia (AMI) due to embolism in the superior mesenteric artery (SMA), is crucial for effective intervention. Dual-energy computed tomography angiography (DE-CTA) is a key diagnostic tool; however, concerns about contrast-induced nephropathy and radiation exposure persist. OBJECTIVES: This study assesses the...

Predicting amyotrophic lateral sclerosis stage based on multi-parameter ultrasound: development and validation of an interpretable machine learning model.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) lacks sensitive, objective staging tools to guide clinical management and trials. Existing methods have limited granularity and rely on subjective assessment, while biomarker and imaging approaches can be invasive or impractical for serial use. Ultrasound is a safe, portable imaging modality that can detect neuromuscular changes, but it has not yet b...

A Simulation-Free Radiation Therapy Workflow Using Synthetic Computed Tomography Generated from Diagnostic Magnetic Resonance Imaging for Personalized Hippocampal-Sparing Whole-Brain Treatment.

PURPOSE: The conventional computed tomography (CT)-based consultation to simulation process for hippocampal-sparing whole-brain radiation therapy (HS-...

Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer.

Integrating genotype (e.g., transcriptomics), phenotype (e.g., imaging), and tumor microenvironment (e.g., metabolomics) is crucial to elucidating the...

Bolstering the Performance of Breast Radiologists with AI-CAD in Mammography: A Multireader Study.

RATIONALE AND OBJECTIVES: Breast cancer is the most common malignancy among females globally and across most Asian countries. In 2022, Asia's age-stan...

Development and Validation of a Deep Learning-Based Segmentation Method for Fenestration Marker and Graft Body Identification in Fenestrated Endovascular Aortic Repair.

OBJECTIVE: To develop and validate a deep learning-based segmentation method for accurate identification of fenestration markers and graft body contou...

The ISLES'24 Dataset: A Multimodal Stroke Imaging Dataset with Hyperacute CT, Acute Postinterventional MRI, and 3-month Clinical Outcomes.

Stroke remains a major global health burden (1,2), although outcomes have improved substantially through imaging-guided therapy and endovascular reper...

Comparative evaluation of large language models for generating CAD-RADS 2.0-compliant diagnostic conclusions in cardiac CT reports.

OBJECTIVES: Coronary computed tomography angiography (CCTA) has become a cornerstone in non-invasive CAD diagnosis and risk stratification. To standar...

Deep-learning computer-aided detection and classification of prostate lesions on biparametric MRI: comparison with expert readers.

OBJECTIVE: To assess the performance of a deep learning-based computer-aided detection (DL-CAD) algorithm for prostate lesion detection and classifica...

Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction.

Deep learning (DL) methods can reconstruct highly accelerated magnetic resonance imaging (MRI) scans, but they rely on application-specific large trai...

Nonperiodic dynamic CT reconstruction using backward-warping implicit neural representation with diffeomorphism regularization.

\textit{Objective.} Motion artifacts remain a major obstacle in dynamic computed tomography (CT) reconstruction, particularly for nonperiodic rapid mo...

A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising.

Achieving high image quality for temporal frames in dynamic positron emission tomography (PET) is challenging due to the limited statistic especially ...

The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.

Gliomas are the most common type of primary brain tumors. Their management options and outcomes depend significantly on the underlying molecular-marke...

DeepFAN, a transformer-based model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multireader, multicase trial.

The widespread adoption of computed tomography has increased the detection of lung nodules. However, deep learning methods for classification of benig...

3D foundation model for generalizable disease detection in head computed tomography.

Head computed tomography (CT) imaging is a widely used imaging modality with multitudes of medical indications, particularly in assessing pathology of...

Plaque-Level Machine Learning Prediction of Intraplaque Hemorrhage in Carotid Arteries Using Computed Tomography Angiography.

BACKGROUND: Carotid artery plaques, especially those with intraplaque hemorrhage (IPH), are significant contributors to ischemic stroke. Although high...

ThyroFusion: A Multi-modal Deep Learning Framework Integrating Vision and Language for Thyroid Nodule Malignancy Risk Assessment.

Accurate differentiation between benign and malignant thyroid nodules remains challenging in clinical practice. Current deep learning approaches predo...

Using Radiomic Features to Detect Anatomical Errors and Assess Deep Learning-Based Left Ventricle Segmentation in Cardiac MRI.

Segmentation of the left ventricle in cardiac magnetic resonance exams is critical for accurate diagnosis and plays a central role in computer-aided d...

An Interpretable Machine-Learning Model for Predicting Occult Central Lymph Node Metastasis in Papillary Thyroid Cancer.

CONTEXT: Accurate preoperative prediction of occult lymph node metastasis (OLNM) in clinically lymph node negative (cN0) papillary thyroid carcinoma (...

Multiclass lung cancer detection using a hybrid capsule inspired deep neural network.

Convolutional Neural Networks are widely used in lung cancer detection for more than a decade. However, it suffers from preserving spatial relationshi...