Radiology

AI-powered evaluation of dementia severity based on clinical data and visual scoring systems (MTA, ERICA, GCA) from MRI.

Dementia, particularly Alzheimer's disease (AD), is a growing concern in aging populations, with mild cognitive impairment (MCI) frequently progressing to AD. While existing studies often rely on comprehensive neuropsychological evaluations and assessments by neurologists and neuroradiologists, these approaches are not always feasible in routine or rural clinical practice. Current diagnostic metho...

Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.

Cell state transitions underlie the emergence of diverse cell types and are traditionally defined by changes in gene expression. Yet these transitions also involve coordinated shifts in cell morphology and behavior, which remain poorly characterized in densely packed epithelia. We developed a quantitative live-imaging and computational framework to track thousands of individual cells over time in ...

Automated Detection of Patent Ductus Arteriosus in Pediatric Patients Using Doppler Ultrasonography Videos Based on a Transformer Model.

Patent ductus arteriosus (PDA) is a common congenital heart defect that requires timely and accurate detection to guide clinical management. Although ...

Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis.

Deep learning (DL) has driven major progress in medical imaging diagnosis. However, its effectiveness is often limited by the scarcity of large annota...

Deep learning-based automatic field of view planning for prostate MRI in oblique coronal and oblique axial planes.

Accurate field-of-view (FoV) prescription in oblique coronal and axial planes is essential for high-quality prostate MRI but remains operator-dependen...

Machine learning and regional homogeneity reveal early and subtle brain changes in type 1 diabetes.

BACKGROUND AND PURPOSE: Type 1 diabetes mellitus (T1DM) usually begins early in life, and its development impacts brain functioning and cognitive proc...

Four Directions, One Solution: Enabling Rapid Diffusion Tensor MRI for Ultra-Low Field Using Deep Learning.

PURPOSE: This study revisits the tetrahedral encoding strategy originally proposed to accelerate Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) ...

Precision theranostics in oncology: integrating antibody-drug conjugates, radioimmunotherapy, and immuno‑PET for adaptive cancer care.

The century-old vision of a "magic bullet" in oncology is being realized through the paradigm of precision theranostics, which formally integrates tar...

Geometry of the cumulant series in diffusion MRI.

Water diffusion gives rise to micron-scale sensitivity of diffusion MRI (dMRI) to cellular-level tissue structure. Precision medicine and quantitative...

VQ-Wave: A Physics-Driven Spatiotemporal Deep Learning Approach for Noncontrast-Enhanced Lung Ventilation and Perfusion MRI.

PURPOSE: To develop a robust deep learning framework for noncontrast-enhanced functional lung MRI, overcoming the limitations of spectral decompositio...

Multi-dimensional MRI representation and privileged learning approaches to functional outcome prediction for ischemic stroke patients.

Accurate prediction of long-term functional outcomes for stroke patients remains a clinical challenge, despite advances in diagnostics and treatments....

Hyperpolarized 129Xe MRI Features Associated with Interstitial Lung Disease Identified Using an Interpretable Diagnostic Algorithm.

RATIONALE AND OBJECTIVES: Hyperpolarized 129Xe magnetic resonance imaging and spectroscopy (MRI/MRS) have been used to identify numerous imaging and s...

Integrating ConvNeXt tiny and radiomics in a nomogram to differentiate true progression from pseudoprogression in glioma.

PURPOSE: Differentiating true progression (TP) from pseudoprogression (PsP) in glioma is challenging due to overlapping enhancement patterns on conven...

Zero-TE MRI-based attenuation correction for bone components on chest [18F] FDG PET/MRI: accuracy, repeatability, and external validation of an unsupervised deep learning approach using unpaired PET/CT data.

OBJECTIVE: In positron emission tomography (PET)/magnetic resonance imaging (MRI), attenuation correction (AC) for PET of the head is achieved by MRI ...

Photon absorption remote sensing (PARS): comprehensive absorption imaging enabling label-free biomolecule characterization and mapping.

Label-free optical absorption microscopy techniques continue to evolve as promising tools for label-free histopathological imaging of cells and tissue...

Application of multimodal ultrasound radiomics in the diagnosis of superficial lymph node tuberculosis.

OBJECTIVE: To develop preoperative diagnostic models for superficial lymph node tuberculosis (LNTB) using radiomic features extracted from multimodal ...

Rapid Multi-Parametric Quantitative MRI via Deep Learning-Based Synthetic-to-Real Reconstruction and 3D SSFP-MOLED Imaging.

Multi-parametric quantitative magnetic resonance imaging (mqMRI) holds significant clinical potential through multi-parametric tissue characterization...

Accuracy of Fluoroscopic Navigation for Predicting Leg Length and Offset in Primary Hip Arthroplasty.

BACKGROUND: Fluoroscopic-assisted computer navigation is widely used to guide intraoperative decisions on leg length (LL) and offset in primary total ...

Feasibility and Reproducibility of a Structure-Guided Deep Learning Model for Automatic Detection of the Standard Sagittal Plane in First-Trimester Nuchal Translucency Assessment Using 3D Ultrasound.

OBJECTIVES: Accurate nuchal translucency (NT) measurement for assessing the risk of fetal genetic abnormalities requires precise acquisition of the mi...

Multimodal Nomogram for the Prenatal Risk Assessment of Hypoplastic Left Heart Syndrome Using Self-Supervised Learning.

OBJECTIVES: This retrospective study aims to develop and validate a multimodal nomogram for the prenatal risk assessment of hypoplastic left heart syn...