Robust resolution improvement of 3D UTE-MR angiogram of normal vasculatures using super-resolution convolutional neural network.

ABSTRACT: Contrast-enhanced UTE-MRA provides detailed angiographic information but at the cost of prolonged scanning periods, which may impose moving artifacts and affect the promptness of diagnosis and treatment of time-sensitive diseases like stroke. This study aims to increase the resolution of rapidly acquired low-resolution UTE-MRA data to high-resolution using deep learning. A total of 20 and 10 contrast-enhanced 3D UTE-MRA data were collected from healthy control and stroke-bearing Wistar rats, respectively. A newly designed 3D convolutional neural network called ladder-shaped residual dense generator (LSRDG) and other state-of-the-art models (SR-ResNet, MRDG64) were implemented, trained, and validated on healthy control data and tested on stroke data. For healthy control data, significantly

SUBMITTER: Tessema AW 

PROVIDER: S-EPMC11920596 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature