Comparison of EPI Distortion Correction Methods in Diffusion Tensor MRI Using a Novel Framework

Diffusion tensor imaging (DTI) has been used to evaluate white matter (WM) integrity in major depressive disorder (MDD), with several studies reporting differences between depressed patients and controls. However, these findings are variable and taken from relatively small studies often using suboptimal analytic approaches. The presented DTI study examined WM integrity in large samples of medication-free MDD patients (n ¼ 134) and healthy controls (n ¼ 54) using voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) approaches, and rigorous statistical thresholds. Compared with health control subjects, MDD patients show no significant differences in fractional anisotropy, radial diffusivity, mean diffusivity, and axonal diffusivity with either the VBM or the TBSS approach. Our findings suggest that disrupted WM integrity does not have a major role in the neurobiology of MDD in this relatively large study using optimal imaging acquisition and analysis; however, this does not eliminate the possibility that certain patient subgroups show WM disruption associated with depression.

Abstract-By shortening the acquisition time of MRI, Echo Planar Imaging (EPI) enables the acquisition of a large number of images in a short time, compatible with clinical constraints as required for diffusion or functional MRI. However such images are subject to large, local distortions disrupting their correspondence with the underlying anatomy. The correction of those distortions is an open problem, especially in regions where large deformations occur.We propose a new block-matching registration method to perform EPI distortion correction based on the acquisition of two EPI with opposite phase encoding directions (PED). It relies on new transformations between blocks adapted to the EPI distortion model, and on an adapted optimization scheme to ensure an opposite symmetric transformation. We present qualitative and quantitative results of the block-matching correction using different metrics on a phantom dataset and on in-vivo data. We show the ability of the blockmatching to robustly correct EPI distortion even in strongly affected areas.

Registration of diffusion weighted datasets remains a challenging task in the process of quantifying diffusion indexes. Respiratory and cardiac motion, as well as echo-planar characteristic geometric distortions, may greatly limit accuracy on parameter estimation, specially for the liver. This work proposes a methodology for the non-rigid registration of multiparametric abdominal diffusion weighted imaging by using different well-known metrics under the groupwise paradigm. A three-stage validation of the methodology is carried out on a computational diffusion phantom, a watery solution phantom and a set of voluntary patients. Diffusion estimation accuracy has been directly calculated on the computational phantom and indirectly by means of a residual analysis on the real data. On the other hand, effectiveness in distortion correction has been measured on the phantom. Results have shown statistical significant improvements compared to pairwise registration being able to cope with elastic deformations.