Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms

• Published in: NMR in biomedicine Vol. 33; no. 12; pp. e4267 - n/a
• Main Authors: Reymbaut, Alexis, Mezzani, Paolo, Almeida Martins, João P., Topgaard, Daniel
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.12.2020
• Subjects: Accuracy; Algorithms; Anisotropy; Biological products; Diffusion; diffusion MRI; Electrical Engineering, Electronic Engineering, Information Engineering; Elektroteknik och elektronik; Engineering and Technology; in silico validation; Inversion; Laplace inversion; Laplace transforms; Magnetic resonance imaging; Mathematical analysis; microstructure; Noise; Signal Processing; Signalbehandling; Statistics; Teknik; tensor-valued diffusion encoding; Tensors; Tissues; Variance; diffusion MRI; in silico validation; microstructure; Laplace inversion; tensor-valued diffusion encoding
• ISSN: 0952-3480; 1099-1492
• DOI: 10.1002/nbm.4267

In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor‐valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one‐dimensional Gamma fit, a generalized two‐term cumulant approach, and two‐dimensional and four‐dimensional Monte‐Carlo‐based inversion techniques, using a clinically feasible tensor‐valued acquisition scheme. In particular, we compare their performance at different signal‐to‐noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two‐dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite‐SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite‐SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two‐dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four‐dimensional Monte Carlo inversion presents no infinite‐SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated. We compare in silico the performances of four algorithms designed to invert tensor‐valued diffusion MRI data: a Gamma fitting (Gamma), a generalized cumulant approach (Cov), and two‐dimensional and four‐dimensional Monte‐Carlo‐based techniques (MC‐2D, MC‐4D). The statistical descriptors estimated from these methods present varying levels of accuracy and precision. Unlike Gamma and Cov, the MC techniques do not present biases at infinite signal‐to‐noise ratio. However, they are limited by their noise sensitivity and MC‐2D requires a powder‐averaged signal of sufficient rotational invariance.