Radiology

Automated Gross Tumor Volume (GTV) Contouring in High-Grade Gliomas Using a Deep Learning Approach.

RATIONALE AND OBJECTIVES: Accurate contouring of the Gross Tumor Volume (GTV) in High-Grade Gliomas (HGGs) is a cornerstone of effective Radiation Therapy (RT) planning, as it influences tumor control and spares normal tissue, thereby directly impacting treatment precision. However, the standard manual approach to GTV contouring requires considerable time and is prone to inter-observer variability...

Relationship between resource utilization and diagnostic accuracy of large language models for efficient multimodal reasoning in radiologic image interpretation.

OBJECTIVES: We evaluated the diagnostic performance and resource efficiency of three multimodal-reasoning-models for radiological image interpretation. METHODS: Using three multimodal-reasoning-models, we analyzed 73 cases under different conditions (Imaging-Only and Combined-Descriptive-Text) with three system prompt types (basic [without system prompt], original [specialized-role], and chain-of-...

Multifeature Ultrasound-Based Classification for Breast Lesions: A Comparative Study of PONS Image Enhancement Technology.

OBJECTIVE: To overcome critical limitations of B-mode ultrasound in artificial intelligence diagnostics-including poor image quality and operator vari...

Cross-sequence semi-supervised learning for multi-parametric MRI-based visual pathway delineation.

Objective.Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Explor...

Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.

The application of machine learning (ML) and artificial intelligence (AI) algorithms in medical imaging is an emerging area of interest, particularly ...

Image Quality Assessment of Artificial Intelligence Iterative Reconstruction for Low-dose Bronchial Artery CTA in Preoperative Hemoptysis Patients.

OBJECTIVES: To investigate the feasibility and image quality of artificial intelligence iterative reconstruction (AIIR) for computed tomography angiog...

Medical Microwave Imaging Using Physics-Guided Deep Learning Part 2: The Inverse Solver.

Deep learning has the potential to address the bottleneck of conventional medical microwave tomography, which is ill-posed and has a high computation ...

Robust Physics-Based Deep MRI Reconstruction via Diffusion Purification.

Deep learning (DL) supervised techniques have been extensively employed in magnetic resonance imaging (MRI) reconstruction, delivering notable perform...

Coronary artery segmentation in non-contrast cardiac CT using anatomy-informed contrastive learning and synthetic data.

Non-contrast cardiac CT (NCCT) offers a low-dose, cost-effective alternative to coronary CT angiography (CCTA) for large-scale coronary artery disease...

Short-TR Acquisition Time-of-flight MR Angiography with Deep Learning Reconstruction: Technical Feasibility and Initial Clinical Evaluation in Moyamoya Disease.

PURPOSE: To develop and evaluate short-TR acquisition time-of-flight (STRA-TOF) MR angiography (MRA), which combines an optimized STRA with deep learn...

Deep learning-based restoration of PET images from a dual-panel breast dedicated scanner.

Positron Emission Tomography (PET) is important for breast cancer diagnosis and monitoring, but high costs restrict access. Dual-panel scanners can re...

Multicenter evaluation of interpretable AI for coronary artery disease diagnosis from PET biomarkers.

Positron emission tomography (PET)/computed tomography (CT) for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluat...

Individuation of predictive parameters of posterior cranial fossa decompression in pediatric patients with Chiari I malformation and autism spectrum disorder.

PURPOSE: There are no specific guidelines for posterior cranial fossa decompression (PCFD) in asymptomatic Chiari Malformation Type I (CM-I) patients ...

Effect of Helicobacter pylori infection on deep learning-assisted detection of gastric neoplastic lesions under white light endoscopy.

BACKGROUND: Artificial intelligence (AI)-assisted endoscopy facilitates upper gastrointestinal lesion detection. Whether Helicobacter pylori (H. pylor...

Deep-learning image reconstruction algorithms for CT: A task-based image quality assessment of four CT systems using a phantom.

PURPOSE: The purpose of this study was to assess the performance of iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorit...

Pixel-level Radiomics and Deep Learning for Predicting Ki-67 Expression in Breast Cancer Based on Dual-modal Ultrasound Images.

RATIONALE AND OBJECTIVES: This study aimed to develop a deep learning model using a novel pixel-level radiomics approach based on two-dimensional (2D)...

Ultrashort echo time MRI radiomics as a predictor of clinical outcomes in patellar tendinopathy: Insights from a large prospective clinical trial.

PURPOSE: To evaluate the predictive utility of radiomic features extracted from ultrashort echo time (UTE) MRI in comparison to conventional proton de...

Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound.

Artificial intelligence (AI) is revolutionizing medical imaging, particularly in chronic liver diseases assessment. AI technologies, including machine...

Initial diagnosis and management of acute ischemic stroke: recent update and future direction.

Acute ischemic stroke (AIS) outcomes depend critically on rapid, accurate early diagnosis in the emergency department. Traditional prehospital tools a...

Single-inspiratory quantitative CT nomogram for enhanced PRISm and COPD differentiation: a cross-sectional study with interpretable diagnostic boundaries.

BACKGROUND: Differentiating preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD) is challenging. Traditional ...