Radiology

CT-based deep learning radiogenomics for predicting key glioma genotypes (IDH, ATRX, EGFR, TP53).

PURPOSE: Molecular subtyping guides diagnosis and targeted therapy for gliomas. Although MRI-the current imaging standard-can be time-consuming and is sometimes contraindicated, computed tomography (CT) is faster, more widely available, and often preferable in emergency and resource-limited settings. We evaluated whether CT-based radiogenomic signatures combined with machine learning could accurat...

Shortening MRI scanning time for acute ischemic stroke: analysis of the effect of 3.0T MRI compressed sensing deep learning reconstruction.

BACKGROUND: Acute ischemic stroke requires rapid and accurate MRI diagnosis. This study aimed to evaluate whether 3.0T brain MRI with compressed sensing deep learning reconstruction (CS‑DLR) can reduce scanning time while maintaining diagnostic image quality. METHODS: We retrospectively enrolled 69 patients with acute ischemic stroke who underwent 3.0T MRI. Conventional images and CS‑DLR reconstru...

Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis.

OBJECTIVE: To systematically evaluate and quantify the diagnostic accuracy and performance of machine ML techniques for the detection of NAFLD, and to...

Update on the structure-Function relationship in glaucoma.

The relationship between structural and functional damage in glaucoma, the structure-function relationship, forms the cornerstone of disease assessmen...

Profiling cognition and brain metabolism in amyotrophic lateral sclerosis and frontotemporal dementia.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are described as a disease continuum, given their shared clinical, genetic and p...

Synthesis of Amyloid Images Using a Generative Adversarial Network from 2-Dimensional 18F-FDG Images and Evaluation for Clinical Use.

The use of amyloid PET to assess patient suitability of disease-modifying drugs for Alzheimer disease is increasing. This study aimed to synthesize am...

Body-Donor-Derived Data in Medical Artificial Intelligence: From Foundational Resources to Trustworthy Applications.

In recent years, artificial intelligence in medicine has evolved from single recognition tasks toward structural understanding, spatial reasoning, and...

A Data-Driven Normalization Framework for Subject-Specific Cerebrovascular Reactivity Assessment in Cerebrovascular Disease.

BACKGROUND: Assessment of cerebrovascular reactivity (CVR) has been reported using acetazolamide-augmented blood oxygenation level-dependent (BOLD) MR...

Rapid Musculoskeletal MRI in 2026: Clinical Integration of Deep Learning Reconstruction.

Advances in MRI hardware and acceleration strategies have enabled substantial reductions in musculoskeletal MRI acquisition times over the past decade...

Comparison of volume- and surface-based magnetic resonance imaging morphometry algorithms in the detection of focal cortical dysplasia.

OBJECTIVE: Detection of focal cortical dysplasia (FCD) remains a major challenge in presurgical epilepsy diagnostics. Magnetic resonance imaging (MRI)...

Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.

BACKGROUND: Paediatric chest imaging is central to diagnosing respiratory and cardiopulmonary disease, particularly in low- and middle-income countrie...

Multiparametric MRI-Based Habitat Radiomics Combined with Deep Transfer Learning for Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma.

The objective of the study is to develop and validate a multiparametric MRI (mpMRI)-based model that integrated with habitat-based radiomics, deep tra...

Low-kVp techniques for radiation dose optimization in abdominopelvic contrast-enhanced CT: a scoping review.

Contrast-enhanced computed tomography (CECT) of the abdomen and pelvis is widely used for diagnostic imaging but contributes substantially to cumulati...

The impact of scan time on dynamic [Formula: see text]-FAPI-04 total-body PET parametric imaging generated by deep learning models.

PURPOSE: To date, some studies have employed deep learning techniques to directly generate dynamic positron emission tomography (PET) parametric image...

Comparative effectiveness and cost-effectiveness of an artificial intelligence workflow for small renal mass diagnosis on computed tomography.

INTRODUCTION: Small renal masses are increasingly detected on imaging, but their accurate classification as benign, indolent, or aggressive remains ch...

A mechatronic and artificial intelligence-driven framework for automated non-invasive knee abnormality screening using multimodal sensor data.

Current knee-abnormality detection relies on costly Magnetic Resonance Imaging (MRI) and subjective clinical evaluation, limiting accessibility. This ...

Impact of Deep-Learning Reconstruction on MRI Workflows: A Retrospective Analysis at a Large Academic Tertiary Center.

PURPOSE: Artificial intelligence is increasingly integrated in clinical practice. In radiological imaging, deep-learning (DL)-based image reconstructi...

Domain-specific adaptation for MR image synthesis with text-guided diffusion.

Deep learning in medical imaging is severely constrained by data scarcity. Data synthesis offers a promising solution, but existing generative models ...

GPDM: generation-prior diffusion model for accelerated direct attenuation and scatter correction of whole-body 18F-FDG PET.

Accurate attenuation and scatter correction is essential in positron emission tomography (PET) for reliable visual interpretation and quantitative ana...

Clinical Evaluation of Deep Learning-Reconstructed Postcontrast 3D T1-Weighted Volume Interpolated Breath-Hold Examination (VIBE) Compared with Standard VIBE for Detection of Internal Auditory Canal Lesions.

BACKGROUND AND PURPOSE: Contrast-enhanced volume interpolated breath-hold examination (VIBE) is commonly used for evaluating internal auditory canal (...