Super-resolution musculoskeletal MRI using deep learning.

ABSTRACT:

Purpose

To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods.

Methods

We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-res

SUBMITTER: Chaudhari AS 

PROVIDER: S-EPMC6107420 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature