ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement
Journal:
arXiv
Published Date:
Mar 14, 2025
Abstract
In clinical imaging, magnetic resonance (MR) image volumes are often acquired
as stacks of 2D slices with decreased scan times, improved signal-to-noise
ratio, and image contrasts unique to 2D MR pulse sequences. While this is
sufficient for clinical evaluation, automated algorithms designed for 3D
analysis perform poorly on multi-slice 2D MR volumes, especially those with
thick slices and gaps between slices. Super-resolution (SR) methods aim to
address this problem, but previous methods do not address all of the following:
slice profile shape estimation, slice gap, domain shift, and non-integer or
arbitrary upsampling factors. In this paper, we propose ECLARE (Efficient
Cross-planar Learning for Anisotropic Resolution Enhancement), a self-SR method
that addresses each of these factors. ECLARE uses a slice profile estimated
from the multi-slice 2D MR volume, trains a network to learn the mapping from
low-resolution to high-resolution in-plane patches from the same volume, and
performs SR with anti-aliasing. We compared ECLARE to cubic B-spline
interpolation, SMORE, and other contemporary SR methods. We used realistic and
representative simulations so that quantitative performance against ground
truth can be computed, and ECLARE outperformed all other methods in both signal
recovery and downstream tasks. Importantly, as ECLARE does not use external
training data it cannot suffer from domain shift between training and testing.
Our code is open-source and available at
https://www.github.com/sremedios/eclare.
