Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data


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Abstract

PurposeWe present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1+-inhomogeneity or flip angle inhomogeneity to enhance the noise robustness of the reconstruction while intrinsically accounting for stimulated echo contributions using T2-distribution data alone.MethodsSimulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well.ResultsIn comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs.ConclusionThe proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions.HighlightsAn accurate determination of T2 distribution in short T2 pool regime is not feasible, given the shortest achievable echo time is ˜ 7–10 ms.The voxel wise estimation of myelin water fraction value depends on three scalar values rather than entire T2 distributions (refer to appendix F).The proposed method utilizes 3D spatial correlations present in in-vivo tissues and underlying effective B1+-inhomogeneity map and iteratively refines both maps to enhance the noise robustness of the reconstruction.Using measured B1+-map to correct for stimulated echo contributions would be suboptimal for reasons mentioned in the discussion section.

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