AUTHORS: Askin Incebacak NC, Sui Y, Gui Levy L, Merlini L, Sa de Almeida J, Courvoisier S, Wallace TE, Antoine Klauser A, Afacan O, Warfield SK, Hüppi P, Lazeyras F

Journal of Neuroimaging, 1: 12, August 2021


ABSTRACT

Background and Purpose: Super-resolutionreconstruction (SRR) can be used to reconstruct 3-dimensional (3D) high-resolution (HR) volume from several 2-dimensional (2D) low-resolution (LR) stacks of MRI slices. The purpose is to compare lengthy 2D T2-weighted HR image acquisition of neonatal subjects with 3D SRR from several LR stacks in terms of image quality for clinical and morphometric assessments.
Methods: LR brain images were acquired from neonatal subjects to reconstruct isotropic 3D HR volumes by using SRR algorithm. Quality assessments were done by an experienced pediatric radiologist using scoring criteria adapted to newborn anatomical landmarks.
TheWilcoxon signed-rank test was used to compare scoring results between HR and SRR images. For quantitative assessments, morphology-based segmentation was performed on both HR and SRR images and Dice coefficients between the results were computed.
Additionally, simple linear regression was performed to compare the tissue volumes.
Results: No statistical difference was found between HR and SRR structural scores using Wilcoxon signed-rank test (p=.63, Z=.48). Regarding segmentation results, R2 values for the volumes of gray matter, white matter, cerebrospinal fluid, basal ganglia, cerebellum, and total brain volume including brain stem ranged between .95 and .99. Dice coefficients between the segmented regions fromHRand SRR ranged between .83±.04 and .96±.01.
Conclusion: Qualitative and quantitative assessments showed that 3D SRR of several LR images produces images that are of comparable quality to standard 2D HR image acquisition for healthy neonatal imaging without loss of anatomical details with similar edge definition allowing the detection of fine anatomical structures and permitting comparable morphometric measurement.

Download PDF


BibTex


Module: