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Purpose: Slice-to-volume registration and super-resolution reconstruction(SVR-SRR) is commonly used to generate 3D volumes of the fetal brain from 2Dstacks of slices acquired in multiple orientations. A critical initial step inthis pipeline is to select one stack with the minimum motion as a reference forregistration. An accurate and unbiased motion assessment (MA) is thus crucialfor successful selection. Methods: We presented a MA method that determines theminimum motion stack based on 3D low-rank approximation using CANDECOMP/PARAFAC(CP) decomposition. Compared to the current 2D singular value decomposition(SVD) based method that requires flattening stacks into matrices to obtainranks, in which the spatial information is lost, the CP-based method canfactorize 3D stack into low-rank and sparse components in a computationallyefficient manner. The difference between the original stack and its low-rankapproximation was proposed as the motion indicator. Results: Compared toSVD-based methods, our proposed CP-based MA demonstrated higher sensitivity indetecting small motion with a lower baseline bias. Experiments on randomlysimulated motion illustrated that the proposed CP method achieved a highersuccess rate of 95.45% in identifying the minimum motion stack, compared toSVD-based method with a success rate of 58.18%. We further demonstrated thatcombining CP-based MA with existing SRR-SVR pipeline significantly improved 3Dvolume reconstruction. Conclusion: The proposed CP-based MA method showedsuperior performance compared to SVD-based methods with higher sensitivity tomotion, success rate, and lower baseline bias, and can be used as a prior stepto improve fetal brain reconstruction.

A Motion Assessment Method for Reference Stack Selection in Fetal Brain MRI Reconstruction Based on Tensor Rank Approximation