Super‐resolution for multislice diffusion tensor imaging

Abstract

Diffusion weighted magnetic resonance images are often acquired with single shot multislice imaging sequences, because of their short scanning times and robustness to motion. To minimize noise and acquisition time, images are generally acquired with either anisotropic or isotropic low resolution voxels, which impedes subsequent posterior image processing and visualization. In this article, we propose a super-resolution method for diffusion weighted imaging that combines anisotropic multislice images to enhance the spatial resolution of diffusion tensor data. Each diffusion weighted image is reconstructed from a set of arbitrarily oriented images with a low through-plane resolution. The quality of the reconstructed diffusion weighted images was evaluated by diffusion tensor metrics and tractography. Experiments with simulated data, a hardware DTI phantom, as well as in vivo human brain data were conducted. Our results show a significant increase in spatial resolution of the diffusion tensor data while preserving high signal to noise ratio.

Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model

“…It is therefore expected that GFAs of the interpolated ODFs are in the range of those of the first and second reference ODFs. The more GFA is outside of this range, the more interpolation error exists in the interpolated data (Poot et al, 2013). To this end, the proposed method has generated superior results than the other method for all of the 4 datasets.…”

“…Scherrer et al (2012) used a combination of three orthogonal acquisitions to create high resolution diffusion weighted images. An extension of this work to arbitrary directions is proposed in (Poot et al, 2013). In addition, there are SR methods that improve the image resolution using postprocessing techniques (Manjon et al, 2010).…”

Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model

“…For diffusion tensor fields, different interpolation, smoothing and/or regularization approaches have been proposed (Coulon et al, 2004;Wang et al, 2004;Arsigny et al, 2006;Weickert and Welk, 2006;Frindel et al, 2009;Poot et al, 2013). These techniques have been mostly investigated in the context of brain DTI.…”

Dictionary Based Super-Resolution for Diffusion MRI

“…The first class of methods are based on performing multiple low-resolution acquisitions, followed by the fusion of the information in these images to generate high-resolution images. To this end, fusing images spatially shifted at sub-voxel level [5], as well as fusing multiple anisotropic images with high resolution only along one axis [2,6] have been explored. In a fairly similar spirit, combining diffusion-weighted (DW) images acquired at two different resolutions to infer high-resolution diffusion parameters using a Bayesian model has also been proposed [7].…”

“…Second, this method does not rely on repeated acquisitions from the same subject, allowing it to be used with legacy data and under various clinical acquisition schemes. Third, this method may still be readily applied when the imaging protocol involves multiple acquisitions, as an additional step after reconstructing a single image from multiple low resolution acquisitions [2,5,6].…”