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Abstract #4410

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

Alexis Reymbaut1,2, Paolo Mezzani1,3, João Pedro de Almeida Martins1,2, and Daniel Topgaard1,2
1Physical Chemistry, Lund University, Lund, Sweden, 2Random Walk Imaging AB, Lund, Sweden, 3Physics, Università degli Studi di Milano, Milan, Italy

In first approximation, the diffusion signal writes as the Laplace transform of an intra-voxel diffusion tensor distribution (DTD). Several algorithms have been introduced to estimate the DTD’s statistical descriptors (mean diffusivity, variance of isotropic diffusivities, mean squared diffusion anisotropy, etc.) by inverting data obtained from tensor-valued diffusion encoding schemes. However, the trueness and precision of these estimations have not been systematically assessed and compared across methods. Here, we compare such estimations in silico for a 1D Gamma fit, a generalized two-term cumulant approach, and 2D and 4D Monte-Carlo inversion techniques, using a common and clinically feasible tensor-valued acquisition scheme.

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