Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis

Abstract

Diffusion weighted magnetic resonance imaging, or DWI, is one of the most promising tools for the analysis of neural microstructure and the structural connectome of the human brain. The application of DWI to map early development of the human connectome in-utero, however, is challenged by intermittent fetal and maternal motion that disrupts the spatial correspondence of data acquired in the relatively long DWI acquisitions. Fetuses move continuously during DWI scans. Reliable and accurate analysis of the fetal brain structural connectome requires careful compensation of motion effects and robust reconstruction to avoid introducing bias based on the degree of fetal motion. In this paper we introduce a novel robust algorithm to reconstruct in-vivo diffusion-tensor MRI (DTI) of the moving fetal brain and show its effect on structural connectivity analysis. The proposed algorithm involves multiple steps of image registration incorporating a dynamic registration-based motion tracking algorithm to restore the spatial correspondence of DWI data at the slice level and reconstruct DTI of the fetal brain in the standard (atlas) coordinate space. A weighted linear least squares approach is adapted to remove the effect of intra-slice motion and reconstruct DTI from motion-corrected data. The proposed algorithm was tested on data obtained from 21 healthy fetuses scanned in-utero at 22–38 weeks gestation. Significantly higher fractional anisotropy values in fiber-rich regions, and the analysis of whole-brain tractography and group structural connectivity, showed the efficacy of the proposed method compared to the analyses based on original data and previously proposed methods. The results of this study show that slice-level motion correction and robust reconstruction is necessary for reliable in-vivo structural connectivity analysis of the fetal brain. Connectivity analysis based on graph theoretic measures show high degree of modularity and clustering, and short average characteristic path lengths indicative of small-worldness property of the fetal brain network. These findings comply with previous findings in newborns and a recent study on fetuses. The proposed algorithm can provide valuable information from DWI of the fetal brain not available in the assessment of the original 2D slices and may be used to more reliably study the developing fetal brain connectome.

Microvascular perfusion of the placenta, developing fetal liver, and lungs assessed with intravoxel incoherent motion imaging

“…Data dropout due to motion was considerable, although this could be mitigated by the on‐demand repetition of the IVIM scan, once excessive fetal motion has been detected. Motion could be mitigated by using more advanced image processing which includes slicewise motion correction, as suggested in various works …”

“…Motion could be mitigated by using more advanced image processing which includes slicewise motion correction, as suggested in various works. [37][38][39] In conclusion, in utero IVIM imaging of the placenta, fetal liver, and lungs is a novel application for portraying gestational changes in microvascular perfusion fraction and diffusion characteristics. IVIM potentially reveals trajectories of microstructural and functional maturation of the vasculature and changes in circulation, providing a reference against which pathological changes can be evaluated.…”

Motion-Robust Spatially Constrained Parameter Estimation in Renal Diffusion-Weighted MRI by 3D Motion Tracking and Correction of Sequential Slices

“…In-vivo experiments with bootstrap sampling showed that the proposed method significantly reduced the uncertainty of parameter estimation. Precise quantitative diffusion-weighted imaging has the potential to be reliably used in clinic to identify renal pathologies where kidney function is compromised [21, 11, 9, 2]. Our motion-compensated DW-MRI framework can also be used with other signal decay models of kidneys such as combined diffusion tensor-IVIM model [20] or 3-compartment signal decay model [24].…”

“…Our proposed motion-robust spatially constrained parameter estimation (MOSCOPE) technique for kidney diffusion-weighted MRI is based on robust state estimation [1] for dynamic motion modeling, and 3D slice-to-volume image registration, which has been used in several challenging body imaging applications [5, 6, 10, 12, 13, 18]. This approach uses the image features of 2D diffusion-sensitized slices which are the smallest packets of k-space data, each acquired in about 200ms.…”

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

“…It was shown that in the case of continuous intermittent subject movements, slice-level motion tracking, correction, and robust reconstruction 25 (MT-SVR in this article) led to significantly more accurate DTI results than the state-of-the-art volume-to-volume registration methods such as those described previously. 33 Slice-level motion tracking is achieved through robust slice-to-volume registration 25 due to the high sampling rate of slice acquisitions in DWI, a framework for detecting and filtering motion-corrupted slice data, and a state-space estimation method based on method by Agamennoni et al 34 As a method that solely relies on imaging data rather than prospectively-designed intrinsic or extrinsic motion navigators, this technique has been successfully adopted and used in extremely challenging applications such as fetal DTI 35 where motion effects are complex. It has also shown significant value in restoring useful information through retrospective processing of previously acquired motion-corrupted DWI scans.…”