Radiology

HP-DIL: Deep heterogeneity profiling with graph-informed disentangled interaction learning for MRI-based liver fibrosis staging.

Liver fibrosis staging (LFS) informs treatment decisions and prognostic assessment in liver disease. Multiparametric MRI enables non-invasive, quantitative characterization of fibrosis-related tissue changes across the whole liver. Although deep-learning-based MRI analysis has advanced automated LFS, two bottlenecks remain: (i) etiology- and tissue-level heterogeneities reduce feature consistency ...

BLADE: Breast Lesion Analysis with Domain Expertise for DCE-MRI Diagnosis.

Dynamic Contrast-Enhanced Magnetic Reso nance Imaging (DCE-MRI) is pivotal in breast cancer diag nosis, yet radiologists face challenges in interpreting its complex data due to the lack of robust automated tools. Current lesion diagnosis systems struggle with limited datasets and insufficient integration of domain knowledge. To overcome these limitations, we propose Breast Lesion Analysis with Dom...

Deep Learning-Based Contrast-Enhanced Ultrasound for Ki-67 Assessment and Prognosis in Hepatocellular Carcinoma.

Ki-67 is a critical prognostic marker for hepatocellular carcinoma (HCC), yet its clinical assessment relies on invasive biopsy. This study aimed to d...

Integrating AI-assisted image enhancement with physics-based synthesis of low-field MRI from high-field MRI.

OBJECTIVE: Low-field magnetic resonance imaging (MRI) offers distinct advantages in terms of affordability, portability, and accessibility. However, i...

Comparative Evaluation of 3D-QALAS Myelin Volume Fraction, Inhomogeneous Magnetization Transfer, and Myelin Water Fraction.

BACKGROUND AND PURPOSE: Accurate in vivo quantification of myelin remains challenging despite advances in MRI. We evaluated three-dimensional syntheti...

Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on...

Deep learning for pediatric synovial recess distension detection in hemophilia: synthetic image augmentation with styleGAN2-ADA.

PURPOSE: Pediatric musculoskeletal ultrasound (MSKUS) datasets are scarce, especially for rare, sex-linked conditions such as hemophilia. Models train...

Imaging in the diagnosis and characterization of tennis leg.

Tennis leg is a common cause of acute posteromedial calf pain and encompasses a spectrum of injuries involving the posterior calf. Although initially ...

Exploring three-dimensional reconstruction with Neural Radiance Field (NeRF) for coronary roadmap navigation and view-planning in X-ray coronary angiography: A feasibility study.

BACKGROUND AND OBJECTIVES: Three-dimensional (3D) reconstruction from X-ray coronary angiograms could enhance diagnosis and guide treatment of coronar...

Predicting adult functional outcomes in childhood-onset attention-deficit/hyperactivity disorder using multimodal MRI and machine learning: A prospective follow-up study.

Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that often persists into adulthood, leading to extens...

Pregnancy changes the variability of brain signaling.

We have shown that pregnancy alters brain structure and brain activity, yet its effects on neural dynamics are unknown. This is the first study to inv...

Deep Learning-Based Multimodal Fusion of Ultrasound, Cytology, and Clinical Features to Distinguish Follicular Thyroid Carcinoma from Adenoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: Preoperative differentiation between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) remains challen...

Multimodal large language models in brain tumor imaging: clinical applications and future perspectives.

The use of multimodal data is essential for the precise diagnosis and treatment of brain tumors. In this context, multimodal data encompass multiseque...

Initial Multicenter Evaluation of a Novel Anchoring Exchange Device: AI-based Quantitative Distal Tip-Motion Analysis.

PURPOSE: Exchange maneuvers during intracranial angioplasty and stent deployment can cause unintended distal tip motion, potentially leading to vessel...

Alzheimer's and Parkinson's Detection with Video-Based Hybrid Deep Learning from Brain MRI.

Dementia, which refers to disorders related to human memory, significantly affects the human brain, and a person with it can experience certain diffic...

MRI-based spatio-temporal atlas of ganglionic eminence.

OBJECTIVE: Fetal brain magnetic resonance imaging (MRI) provides insights into the architecture of the human brain. Recently, an increasing interest h...

MEDI-SLATE: medical imaging slide-lecture aligned teaching ensemble.

Slide-based lectures remain the primary means by which undergraduate students learn about the mathematical, physical, and systems-level foundations of...

Real-time target tracking using image registration and motion prediction.

PURPOSE: Real-time tracking of anatomical targets is critical in numerous clinical interventions. In magnetic resonance-guided therapies, targets are ...

Artificial Intelligence in Spine Imaging Interpretation.

Spinal disorders, one of the leading causes of disability worldwide, are routinely assessed on imaging studies. Recent advancements in artificial inte...

Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

We present a large whole-body and total-body curated dataset of dual-modality 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-Positron Emission Tomography/Compu...