Radiology

Prostate cancer tissue mapping and stratification using DRAQ5 and Eosin fluorescent labels integrated with AI classification and segmentation algorithms.

BACKGROUND: Fluorescent microscopy using the DRAQ5 and Eosin probes has been shown in the literature to be capable of producing rapid tissue characterization through synthetic H&E-like pseudoimages, which can be potentially utilized in the clinic. This study focuses on developing deep learning models for classification and segmentation of prostate tissue labeled with DRAQ5&Eosin. The fluorophores ...

Retinal imaging and AI for non-invasive detection of diabetic macrovascular complications.

BACKGROUND: Diabetes mellitus accelerates vascular degeneration and increases the risk of major macrovascular complications, including Peripheral Arterial Disease (PAD) and aortic pathologies, collectively termed Peripheral Arterial and Aortic Diseases (PAAD). These conditions are strongly associated with adverse cardiovascular outcomes but often remain underdiagnosed in diabetic populations due t...

Breast Arterial Calcification on Mammography and Cardiovascular Outcomes in Women: A Meta-analysis.

Breast arterial calcification (BAC) is commonly observed on screening mammography and may provide a low-cost opportunistic marker to enhance cardiovas...

Reducing breath-hold time in liver MRI: Clinical performance of deep learning-accelerated post-contrast T1 VIBE sequences.

BACKGROUND: Post-contrast liver MRI often requires long breath-holds, risking motion artifacts that can reduce diagnostic quality. We assessed whether...

Enriched resting-state EEG prediction of cognitive decline in prodromal Alzheimer's disease: a machine-learning approach.

OBJECTIVE: We evaluated the accuracy of standard machine learning (ML) algorithms in predicting 1-year cognitive decline in Alzheimer's disease patien...

Perfusion-derived cerebral CT angiography with artificial intelligence iterative reconstruction as a replacement for routine cerebral CT angiography in acute ischemic stroke.

OBJECTIVES: To investigate the feasibility of replacing routine cerebral CT angiography (CTA) with CT perfusion (CTP)-derived cerebral CTA by using ar...

Ultrasound Localization Microscopy Learned from power doppler by uncertainty frequency density estimation and semantic consistency awareness.

Ultrasound Localization Microscopy (ULM) achieves micron-level vascular visualization beyond the resolution of conventional ultrasound imaging by trac...

Neural signatures of human psychological resilience driven by acute stress.

Neurophysiological mechanisms underlying psychological resilience-the ability to overcome adversity-have been extensively studied in animals. However,...

A generalizable deep learning system for cardiac MRI.

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision...

Clinical Feasibility of Deep Learning Contrast Synthesis From MR Fingerprinting in Knee Osteoarthritis.

BACKGROUND: Magnetic Resonance Fingerprinting (MRF) enables rapid quantitative parameter mapping from which synthetic clinical contrast images can be ...

Effectiveness of Thoracic Paravertebral Block in Minimally Invasive Vertebroplasty: A Retrospective Cohort Study Focusing on Pain Trajectories and Sagittal Spinal Alignment.

PURPOSE: To evaluate the effectiveness of ultrasound-guided thoracic paravertebral block (TPVB) combined with general anesthesia (GA) versus GA alone ...

Whole-body magnetic resonance imaging for cancer screening in asymptomatic adults: a multicenter study.

OBJECTIVES: Whole-body MRI is increasingly used for preventive health screening; however, the prevalence and distribution of incidental oncologically ...

Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation.

Purpose To develop and validate deep learning models for detecting bone metastases on abdominal and thoracic CT scans, considering lesion visibility, ...

U-Net-based transfer learning for automated tumour segmentation enabling fully automated [18F]F-DOPA PET analysis in paediatric gliomas.

BACKGROUND: PET imaging with [18F]F-DOPA shows great promise for assessing paediatric gliomas. Manual tumour delineation and parameter extraction are ...

An interpretable machine learning model combining MRI-DKI habitat radiomic features and clinical biomarkers for noninvasive prediction of lymphatic metastasis in rectal cancer: a prospective study.

OBJECTIVE: Tumor heterogeneity exerts a significant influence on lymphovascular invasion (LVI) and lymph node metastasis (LNM) in rectal cancer (RC), ...

Deep learning for temporal super-resolution 4D Flow MRI.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparen...

Milliwatt ultrasound for navigation in visually degraded environments on palm-sized aerial robots.

Tiny palm-sized aerial robots have exceptional agility and cost-effectiveness in navigating confined and cluttered environments. However, their limite...

The forgotten spectrum: Reviving ultrasound for robust autonomy.

Seemingly outdated ultrasound combined with edge-AI denoising can make autonomy more robust when vision fails.

Efficient machine learning models leveraging DCE‑MRI morphological and dynamic features allow accurate breast lesion classification.

We propose an ensemble learning approach to classify malignant versus benign breast lesions leveraging morphological and dynamic features derived from...

TSMPI-Net: a time-series generative adversarial network for short-frame-interval magnetic particle imaging.

OBJECTIVE: Magnetic particle imaging (MPI) is an emerging imaging modality that offers high sensitivity and potential for high-speed imaging. In many ...