Radiology

Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.

. Arterial wall stiffness can provide valuable information on the proper function of the cardiovascular system. Ultrasound elasticity imaging techniques have shown great promise as a low-cost and non-invasive tool to enable localized maps of arterial wall stiffness. Such techniques rely upon motion detection algorithms that provide arterial wall displacement estimation.. In this study, we propose ...

Deep Learning-Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation.

OBJECTIVE: This study aimed to evaluate the clinical performance of a deep learning-based motion correction algorithm (MCA) in projection domain for coronary computed tomography angiography (CCTA).

Combining hyperspectral imaging techniques with deep learning to aid in early pathological diagnosis of melanoma.

BACKGROUND: Cutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers....

A CT-based Deep Learning Radiomics Nomogram for the Prediction of EGFR Mutation Status in Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Accurately assessing epidermal growth factor receptor (EGFR) mutation status in head and neck squamous cell carcinoma (HNSCC...

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep...

Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data.

The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantific...

Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model.

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are autoimmune inflammatory disorders of the central nervous system (CNS) w...

MRI-based deep learning model for differentiation of hepatic hemangioma and hepatoblastoma in early infancy.

UNLABELLED: Hepatic hemangioma (HH) and hepatoblastoma (HBL) are common pediatric liver tumors and present with similar clinical manifestations with l...

Robotic ultrasound imaging: State-of-the-art and future perspectives.

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiati...

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept.

The patient setup technique currently in practice in most radiotherapy departments utilises on-couch cone-beam computed tomography (CBCT) imaging. Pat...

Performance of deep learning-based autodetection of arterial stenosis on head and neck CT angiography: an independent external validation study.

PURPOSE: To externally validate the performance of automated stenosis detection on head and neck CT angiography (CTA) and investigate the impact facto...

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging.

PURPOSE OF REVIEW: Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algor...

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on ResAttU-Net for Transcranial Brain Hemorrhage Detection.

OBJECTIVE: Hemorrhagic stroke is a leading threat to human's health. The fast-developing microwave-induced thermoacoustic tomography (MITAT) technique...

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis.

BACKGROUND: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based...

Comparative verification of control methodology for robotic interventional neuroradiology procedures.

PURPOSE: The use of robotics is emerging for performing interventional radiology procedures. Robots in interventional radiology are typically controll...

Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.

Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Ima...

Unsupervised abnormality detection in neonatal MRI brain scans using deep learning.

Analysis of 3D medical imaging data has been a large topic of focus in the area of Machine Learning/Artificial Intelligence, though little work has be...

Deep-learning visualization enhancement method for optical coherence tomography angiography in dermatology.

Optical coherence tomography angiography (OCTA) in dermatology usually suffers from low image quality due to the highly scattering property of the ski...

Deep learning for artery-vein classification in optical coherence tomography angiography.

Major retinopathies can differentially impact the arteries and veins. Traditional fundus photography provides limited resolution for visualizing retin...

An unsupervised two-step training framework for low-dose computed tomography denoising.

BACKGROUND: Although low-dose computed tomography (CT) imaging has been more widely adopted in clinical practice to reduce radiation exposure to patie...