To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging

Abstract

Multi-slice (MS) super-resolution reconstruction (SRR) methods have been proposed to improve the trade-off between resolution, signal-to-noise ratio and scan time in magnetic resonance imaging. MS-SRR consists in the estimation of an isotropic high-resolution image from a series of anisotropic MS images with a low through-plane resolution, where the anisotropic low-resolution images can be acquired according to different acquisition schemes. However, it is yet unclear how these schemes compare in terms of statistical performance criteria, especially for regularized MS-SRR. In this work, the estimation performance of two commonly adopted MS-SRR acquisition schemes based on shifted and rotated MS images respectively are evaluated in a Bayesian framework. The maximum a posteriori estimator, which introduces regularization by incorporating prior knowledge in a statistically well-defined way, is put forward as the estimator of choice and its accuracy, precision, and Bayesian mean squared error (BMSE) are used as performance criteria. Analytic calculations as well as Monte Carlo simulation experiments show that the rotated scheme outperforms the shifted scheme in terms of precision, accuracy, and BMSE. Furthermore, the superior performance of the rotated scheme is confirmed in real data experiments and in retrospective simulation experiments with and without inter-image motion. Results show that the rotated scheme allows regularized MS-SRR with a higher accuracy and precision than the shifted scheme, besides being more resilient to motion.

3D whole heart k-space-based super-resolution cardiac T1 mapping using rotated stacks

“…For the first time in quantitative cardiac imaging, a k-space-based SRR scheme with differently rotated LR stacks could be applied. It could be shown that it outperformed the results from a parallel stacks acquisition with shifts along the SE direction with respect to robustness against motion in between the LR stacks, which is in accordance previously published results for the brain (Shilling et al 2009, Nicastro et al 2022. Furthermore, the whole heart could be covered with SRR using rotated stacks, while a SRR approach with parallel stacks was limited to the ventricles in the same acquisition time.…”

“…As shown in figure 12, SRR rot showed less pronounced artefacts due to motion compared to SRR transl , which agreed with (Nicastro et al 2022). Motion in SRR transl led to strong zig-zag artefacts, while motion in SRR rot led to minor artefacts such as blurring.…”

Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping

“…In future work, we intend to investigate, given a fixed acquisition time and relying on Cramér-Rao lower bound analysis, the best slice direction and contrast weighting combination of each of the LR images in terms of the precision with which qMRI parameters can be estimated with our proposed SRR-joint framework. Preliminary results of this study for SRR-static have recently been reported by Nicastro et al (Nicastro et al, 2020).…”

Mouse brain MR super-resolution using a deep learning network trained with optical imaging data

“…For example, in order to improve the resolution along the slice direction, multiple images can be acquired, each shifted by a known sub-voxel distance along the slice direction, and images with enhanced through-plane resolution can be reconstructed ( 13 ). To improve in-plane resolution, acquiring multiple images with rotated scans has also been reported ( 14 , 15 ). Also, some work incorporated extra information from inter-scan subject motions or image distortions to achieve SR ( 6 , 16 ).…”