Three-dimensional self super-resolution for pelvic floor MRI using a convolutional neural network with multi-orientation data training.

ABSTRACT:

Purpose

High-resolution pelvic magnetic resonance (MR) imaging is important for the high-resolution and high-precision evaluation of pelvic floor disorders (PFDs), but the data acquisition time is long. Because high-resolution three-dimensional (3D) MR data of the pelvic floor are difficult to obtain, MR images are usually obtained in three orthogonal planes: axial, sagittal, and coronal. The in-plane resolution of the MR data in each plane is high, but the through-plane resolution is low. Thus, we aimed to achieve 3D super-resolution using a convolutional neural network (CNN) approach to capture the intrinsic similarity of low-resolution 3D MR data from three orientations.

Methods

We used a two-dimensional (2D) super-resolution CNN model to solve the 3D super-resolution pro

SUBMITTER: Feng F 

PROVIDER: S-EPMC9013299 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature