Radiology

Reconstruction of MRI from undersampled k-spaces of double-contrast volume acquisitions using deep neural networks.

Magnetic Resonance Image (MRI) reconstruction from undersampled k-space using Deep Neural Networks (DNNs) has been extensively investigated. Undersampling accelerates the MRI acquisition process while addressing challenges such as motion artifact, signal decay, geometric distortion, and high Specific Absorption Rate (SAR). This article introduces a novel approach for the reconstruction of undersam...

Predictive analytics and risk stratification models in internal medicine: from risk scores to real-time machine learning.

Internal medicine involves high-stakes, time-sensitive decisions (such as triaging acute illnesses, escalating care, providing thromboprophylaxis, planning discharges, and managing chronic diseases) often under uncertainty. Risk stratification tools convert limited bedside data into actionable categories. Predictive analytics, by contrast, draws on richer electronic health record data streams to e...

NeuroHarm‑Kit: An Open‑Source Toolbox for Benchmarking Deep‑Learning Harmonization of Multi‑Site T1‑Weighted MRI.

Multi‑site magnetic resonance imaging (MRI) studies enable studying brain structure across diverse populations, but scanner‑related variability remain...

Artificial Intelligence Interpretation of Point-of-Care Lung Ultrasound in Microgravity.

INTRODUCTION: Long-duration space missions demand reliable, portable, and autonomous medical diagnostic tools. Lung ultrasound (LUS) is ideal for spac...

A Systematic Evaluation of Image Preprocessing in Deep Learning Detection and Segmentation of Intracranial Metastatic Disease.

BACKGROUND AND PURPOSE: Image preprocessing is an essential, though often overlooked, part of machine learning, and it is unclear how preprocessing te...

Impact of Deep Learning-Based Denoising on Image Quality and Diagnostic Confidence in Neurovascular Ultrahigh-Resolution Photon-Counting CT Angiography.

BACKGROUND AND PURPOSE: Ultrahigh-resolution (UHR) photon-counting detector (PCD)-CT angiography enables detailed visualization of the neurovasculatur...

ASNR-ESNR White Paper on Sustainability in Neuroradiology.

BACKGROUND: This joint American Society of Neuroradiology-European Society of Neuroradiology (ASNR-ESNR) white paper addresses the call for sustainabl...

Cardiac Natural Mechanical Wave Detection and Speed Estimation Using Deep Learning-Based 2-D Ultrasound Imaging: A Feasibility Study.

OBJECTIVE: Cardiac natural mechanical wave (NMW) propagation speed is a marker of the mechanical properties and pathological state of tissue, and its ...

An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis.

Characterizing in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building on prior spatiotemporal fetal...

Application of a deep learning method based on CBCT panoramic reconstruction images for differentiating operationally defined cystic and neoplastic jaw lesions from osteomyelitis: a single-center retrospective study.

This single-center retrospective study developed and internally validated a two-dimensional deep learning model based on cone-beam computed tomography...

BEAMSTER: Brain mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with Expert Segmentations.

We present a retrospective dataset of contrast-enhanced T1-weighted magnetic resonance imaging scans from 140 patients with brain metastases who under...

Automatic lesion segmentation in ⁶⁸Ga-PSMA PET/CT and ¹⁷⁷Lu-PSMA SPECT/CT: added value of PET-guided SPECT in a bicentric study.

BACKGROUND: Manual segmentation of prostate cancer metastases on PSMA PET/CT and SPECT/CT is time-consuming and poorly scalable, particularly in highl...

Clinical Evaluation of A-LIKNet: Deep Learning-accelerated Single-breath-hold CINE Magnetic Resonance Imaging for Cardiac Function Assessment.

OBJECTIVES: To evaluate the diagnostic accuracy and quantitative agreement of A-LIKNet (attention-incorporated network for sharing low-rank, image, an...

A novel cross-attentive network for classifying cervical metastatic lymph nodes on B- and D-mode ultrasound images in oral squamous cell carcinoma.

OBJECTIVES: This study proposes a deep convolutional neural network model that integrates B-mode and D-mode ultrasound images to classify metastatic l...

Leveraging Swin Transformer for Enhanced Diagnosis of Alzheimer's Disease Using Multi-Shell Diffusion MRI.

OBJECTIVE: This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural i...

EMI Cancellation for Shielding-Free Ultra-Low-Field MRI.

OBJECTIVE: Ultra-Low-Field Magnetic Resonance Imaging (ULF MRI) offers low cost and portability but suffers from electromagnetic interference (EMI) in...

From Speech to Sonography: Spectral Networks for Ultrasound Microstructure Classification.

The frequency dependence of backscattered radiofrequency (RF) signals produced by ultrasound scanners carries rich information related to the tissue m...

End-to-End Autonomous Quantification of Brain Aneurysm and Parent Artery Morphology at CT Angiography.

Purpose To develop and validate an end-to-end autonomous platform for the quantification and visualization of brain aneurysm and parent artery morphol...

SELFIE: Self-Supervised Learning for Fast Dynamic Golden-Angle Radial MRI.

Accelerated golden-angle radial acquisitions are widely used for dynamic MRI, but compressed sensing (CS)-based reconstruction presents residual artif...

PET/MR: history, current state, and technology.

Integrated PET/MR combines the molecular sensitivity of PET with the superior soft-tissue contrast and multiparametric capabilities of MRI, enabling s...