Motion‐robust diffusion compartment imaging using simultaneous multi‐slice acquisition

Abstract

Purpose To achieve motion‐robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi‐slice, diffusion‐weighted brain MRI. Methods Simultaneous multi‐slice (SMS) acquisition enables fast and dense sampling of k‐ and q‐space. We propose to achieve motion‐robust DCI via slice‐level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice‐to‐volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction. Results We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT‐SMR) to multi b‐value SMS diffusion‐weighted brain MRI of healthy volunteers and motion‐corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing‐fiber regions show robust reconstruction in the presence of motion. Conclusion The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non‐cooperative patients.

Retrospective Distortion and Motion Correction for Free‐Breathing DW‐MRI of the Kidneys Using Dual‐Echo EPI and Slice‐to‐Volume Registration

“…Currently, we applied TOPUP, which uses the least‐squares difference between the two images to align them and to correct for the effects of distortion. Incorporating alternative registration metrics such as mutual information, normalized cross‐correlation, or metrics based on the DW models, 33–35 might improve the reliability of the matches between the two images. Alternatively, the T 2 attenuation could be estimated and accounted for in the second echo images of the dual echo acquisition in order to correct for the change of intensity of the second echo images 36 …”

SLIMM: Slice localization integrated MRI monitoring

“…In an SMS acquisition scheme, patches taken from N simultaneously acquired slices sample the anatomy at positions that are at distances of each other, where FOV s is the field-of-view in the slice select direction (i.e., the number of slices times slice thickness). The N slices that are acquired at the same time, have the same motion state; therefore they can regularize SVR and improve its accuracy ( Marami et al, 2019 ). Our simultaneous- N -slice-based similarity minimization is thus written as where the subscript n i denotes the index of the i th slice from the volume, and indexes patches that are selected from the i -th input slice.…”

“…Compared to VVR-based techniques, motion correction using retrospective SVR followed by image reconstruction has shown significantly improved results in a range of MRI applications including diffusion-weighted imaging (DWI) of non-cooperative patients, e.g. ( Bastiani et al, 2019 ; Hutter et al, 2018 ; Marami et al, 2016 ; 2019 ), fetal brain MRI ( Alansary et al, 2017 ; Ebner et al, 2019b ; Gholipour et al, 2010 ; Kainz et al, 2015 ; Marami et al, 2017 ), fetal cardiac MRI ( van Amerom et al, 2019 ; Lloyd et al, 2019 ), and body MRI ( Ebner et al, 2019a ; Kurugol et al, 2017 ).…”

Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI

“…To facilitate a wide range of further analyses, the signal representation must be model-free, whilst also offering rank-reduction properties needed to make the data self-consistent. Prior work in dMRI slice-to-volume reconstruction has used representations based on the diffusion tensor ( Fogtmann, Seshamani, Kroenke, Cheng, Chapman, Wilm, Rousseau, Studholme, 2014 , Jiang, Xue, Counsell, Anjari, Allsop, Rutherford, Rueckert, Hajnal, 2009 , Marami, Salehi, Afacan, Scherrer, Rollins, Yang, Estroff, Warfield, Gholipour, 2017 , Marami, Scherrer, Afacan, Erem, Warfield, Gholipour, 2016 ), multi-tensor models ( Marami et al., 2018 ), single-shell spherical harmonics ( Deprez et al., 2019 ), or Gaussian processes ( Andersson, Graham, Drobnjak, Zhang, Filippini, Bastiani, 2017 , Scherrer, Gholipour, Warfield, 2012 ), which can limit the supported input data or downstream analysis. Here, we use a data-driven signal representation for multi-shell dMRI based on spherical harmonics and a radial decomposition (SHARD) that was shown to have compelling low-rank properties highly suitable for motion correction applications ( Christiaens et al., 2019a ).…”