Super‐resolution reconstruction of diffusion parameters from diffusion‐weighted images with different slice orientations

Abstract

Purpose: Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the tradeoff between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images. Method: An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusionweighted images, optimally samples the q-and k-space, and performs motion correction with b-matrix rotation.Results: Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-meansquare error.

A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging

“…The quadratic term in (8) corresponds to the assumption that the noise components in Y k 's follow Gaussian distributions which is usually assumed in SRR methods, see e.g. [17, 21]. For other type of distributions, such as Rician distribution, the quadratic term can be replaced by the corresponding log-likelihood functions as was done in [50].…”

“…For other type of distributions, such as Rician distribution, the quadratic term can be replaced by the corresponding log-likelihood functions as was done in [50]. However, the Gaussian assumption is valid for high SNR (SNR>5) [21, 51]. Moreover, using a quadratic term in (8) usually leads to computationally efficient algorithms which is preferred in solving large scale optimization problems.…”

“…To address this problem, more recently, [20] introduced a method that used the diffusion tensor imaging (DTI) technique to model the diffusion signal in q-space. It was extended in [21, 22] to allow the LR images to be acquired along different sets of gradients in q-space. Though this approach does reduce acquisition time and is robust to motion, a very simplistic diffusion tensor model is not appropriate for modeling more complex diffusion phenomena (crossing fibers).…”

A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI

“…Thus, these methods require the same number of measurements (e.g., 60 gradient directions) for each LR acquisition. To address this problem, more recently, [8] introduced a method that used the diffusion tensor imaging (DTI) technique to model the diffusion signal in q-space. However, a very simplistic diffusion tensor model was assumed, which is not appropriate for modeling more complex diffusion phenomena (crossing fibers).…”

High resolution T1 estimation from multiple low resolution magnetic resonance images

“…Therefore, precise and accurate high resolution T 1 mapping is infeasible in clinical practice. Recent work has shown that super-resolution (SR) reconstruction methods can provide MR images with an improved trade-off between acquisition time, SNR and spatial resolution [12][13][14][15], by reconstructing an isotropic high resolution image from a set of images with a high in-plane and low through-plane resolution.…”