Yan Wang

UCSF University of California, San Francisco | UCSF · Department of Radiology and Biomedical Imaging

Background Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task. Objective To evaluates a novel deep-learning algorithm, named vessel attention (VA)-Unet, for the efficient detection and segmentation of IAs. Methods This retrospective study was conducted using head CT angiography (CTA) examinations de...

Objectives: To determine if three-dimensional (3D) radiomic features of contrast-enhanced CT (CECT) images improve prediction of rapid abdominal aortic aneurysm (AAA) growth. Methods: This longitudinal cohort study retrospectively analyzed 195 consecutive patients (mean age, 72.4 years ± 9.1) with a baseline CECT and a subsequent CT or MR at lea...

Objectives: To compare the visibility of intracranial aneurysm wall and thickness quantification between 7 and 3 T vessel wall imaging and evaluate the association between aneurysm size and wall thickness. Methods: Twenty-nine patients with 29 unruptured intracranial aneurysms were prospectively recruited for 3D T1-weighted vessel wall MRI at bo...

Background: The segmentation of cardiac medical images is a crucial step for calculating clinical indices such as wall thickness, ventricular volume, and ejection fraction. Methods: In this study, we introduce a method named LsUnet that combines multi-channel, fully convolutional neural network, and annular shape level-set methods for efficientl...

Objective Accurate diagnosis and measurement of intracranial aneurysms are challenging. This study aimed to develop a 3D convolutional neural network (CNN) model to detect and segment intracranial aneurysms (IA) on 3D rotational DSA (3D-RA) images. Methods 3D-RA images were collected and annotated by 5 neuroradiologists. The annotated images were...

Background Non-contrast 3D black blood MRI is a promising tool for abdominal aortic aneurysm (AAA) surveillance, permitting accurate aneurysm diameter measurements needed for patient management. Purpose To evaluate whether automated AAA volume and diameter measurements obtained from computer-aided segmentation of non-contrast 3D black blood MRI ar...

The left ventricular (LV) end-systolic (ES) pressure volume relationship (ESPVR) is the cornerstone of systolic LV function analysis. We describe a 2D real-time (RT) MRI-based method (RTPVR) with separate software tools for 1) semi-automatic level set-based shape prior method (LSSPM) of the LV, 2) generation of synchronized pressure area loops and...

This study aimed to develop a cardiorespiratory-resolved 3D magnetic resonance imaging (5D MRI: x-y-z-cardiac-respiratory) approach based on 3D motion tracking for investigating the influence of respiration on cardiac ventricular function. A highly-accelerated 2.5-minute sparse MR protocol was developed for a continuous acquisition of cardiac image...

This study reports on the development and evaluation of a novel segmentation method for extracting the internal jugular vein and the adjacent carotid artery from magnetic resonance (MR) images of patients with pulsatile tinnitus. A narrow band level set method with combined shape and appearance constraints was developed and applied to high-resoluti...

Purpose Low-porosity endovascular stents, known as flow diverters (FDs), have been proposed as an effective and minimally invasive treatment for sidewall intracranial aneurysms (IAs). Although it has been reported that the efficacy of a FD is substantially influenced by its porosity, clinical doctors would clearly prefer to do their interventions o...

Purpose Segmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semiautomatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of three‐dimensional (3D) magnetic resonance (MR) images throughout the entire cardiac...

Cardiac progenitor cells (CPCs) being multipotent offer a promising source for cardiac repair due to their ability to proliferate and multiply into cardiac lineage cells. Here, we explored a novel strategy for human CPCs generation from human induced pluripotent stem cells (hiPSCs) using a cardiogenic small molecule, isoxazole (ISX-9) and their abi...

Background: Interleukin-32 (IL-32) is a newly discovered proinflammatory cytokine. However, there are limited data regarding IL-32 as a biomarker for heart failure (HF). In this study, we assessed the prognostic value of IL-32 in patients with chronic HF after myocardial infarction (MI). Methods and results: Over a period of 1.8years, we prospec...

Objectives: To evaluate an accelerated 4D flow MRI method that provides high temporal resolution in a clinically feasible acquisition time for intracranial velocity imaging. Materials and methods: Accelerated 4D flow MRI was developed by using a pseudo-random variable-density Cartesian undersampling strategy (CIRCUS) with the combination of k-t,...

Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abd...

Objective: This work presents a highly-accelerated, self-gated, free-breathing 3D cardiac cine MRI method for cardiac function assessment. Materials and methods: A golden-ratio profile based variable-density, pseudo-random, Cartesian undersampling scheme was implemented for continuous 3D data acquisition. Respiratory self-gating was achieved by...

This article proposes a 4D segmentation method by considering the 3D t data as a 4D hyper object, using a D4Q81 lattice in a lattice Bhatnagar-Gross-Krook (LBGK) simulation, where time is considered a fourth dimension for defining directions of particle momentum in the LBGK model. They implemented 4D-LBGK on 20 4D hypersphere and hypercube images w...

Purpose: Segmentation of aneurysms plays an important role in interventional planning. Yet, the segmentation of both the lumen and the thrombus of an intracranial aneurysm in computed tomography angiography (CTA) remains a challenge. This paper proposes a multilevel segmentation methodology for efficiently segmenting intracranial aneurysms in CTA...

Segmentation of giant cerebral aneurysms composed of lumen and thrombus in computed tomography angiography remains a challenge for image processing community. We propose a multilevel object detection scheme based on lattice Boltzmann method (LBM) to tackle this problem. The method consists of first extracting the lumen using the LBM with some param...