Advanced measures to describe diffusion features from single shell diffusion MRI acquisitions

What is it about?

Diffusion Tensor Imaging (DTI) is the most employed method to assess white matter properties using quantitative parameters derived from diffusion MRI, but it presents known limitations that restrict the evaluation of complex structures. In this work, to overcome these limitations and facilitate the widespread use of advanced diffusion metrics in clinical studies, we propose a new approach called Apparent Measures Using Reduced Acquisitions (AMURA). The method allows the estimation of alternative diffusion measures such as RTOP, RTAP and PA, while reducing the number of necessary samples and the computational cost. AMURA can mimic the sensitivity of more advanced measures to microstructural changes when only a small number of shells (even one) is available. To do so, AMURA assumes a prior model for the behavior of the diffusion, yielding simplified expressions that can be computed easily even from single-shell acquisitions. One additional advantage of AMURA is that it can be easily integrated into the processing pipeline of current existing single-shell dMRI protocols and databases to unveil anatomical details that may remain hidden in traditional DT-based studies. AMURA has proved its potential in some exploratory studies with clinical data.

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Photo by Accuray on Unsplash

Photo by Accuray on Unsplash

Why is it important?

Diffusion MRI (dMRI) has gained much interest from the neuroimaging community over the last two decades, due to its ability to analyze in vivo structures within the white matter of the brain. The current trend in dMRI analysis is the calculation of increasingly advanced metrics focused on subtle aspects of the diffusion and brain microstructure. However, for the calculation of these advanced measures, the acquisition requirements are getting higher and higher: (1) Increasingly powerful MRI scanners; (2) Denser q-space sampling: higher number of shells (b-values) and higher number of samples per shell. However, not all centers can afford such expensive dedicated equipment. In addition, an increase in the number of samples at each acquisition leads to longer acquisition times. Thus, these techniques are not totally compatible with clinical and research acquisitions that can be performed in most medical centers. To alleviate this problem, this work aims to propose novel advanced measures that provide similar clinical information to existing ones but using commercial scanners and a limited number of samples.

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