PURPOSE Diffusion in restricted media, such as the neuron axons, is nonGaussian 1. Mean Apparent Propagator Magnetic Resonance Imaging (MAPMRI 2,3 ) is a reconstruction model for diffusion MRI which is able to estimating the nonGaussianity 2 (NG) of the diffusion signal. This study aims at investigating the minimum requirements of a diffusion weighted acquisition, in terms of Signal to Noise Ratio (SNR) and maximum bvalue, for MAPMRI to capture the NG of the signal. METHODS We used Camino (http://camino.cs.ucl.ac.uk/) MonteCarlo to simulate the diffusion signal inside a pack of parallel cylinders oriented along the z axis (100000 spins 4 , 1000 timesteps 4, radius 0.5μm, and 0.1μm of space between the cylinders). We acquired the diffusion signal using 10 different sampling schemes, Δ=57.9ms, δ=13.8ms, TE=91.3ms, and considering 10 b 0, 60 directions at bvalue= 700s/mm 2 ( first shell), and 60 directions at bvalues 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, and 10000 s/mm 2, r espectively, per sampling scheme (second shell). We fit both Diffusion Tensor Imaging 5 (DTI) and MAPMRI to the simulated data and the NG index was computed using MAPMRI. Successively, the Mean Squared Error (MSE) between ground truth and fitted signal was calculated for both DTI and MAPMRI. The same analyses were performed also adding Rician Noise to the diffusion signal with SNR=[40, 30, 20] considering 100 different instances of noise per sampling scheme. The MSE in this case was calculated with respect to the noiseless ground truth signal.
Characterization of diffusion MRI signal non Gaussianity using MAPMRI
Brusini, Lorenza;Zucchelli, Mauro;OBERTINO, SILVIA;MENEGAZ, Gloria
2016-01-01
Abstract
PURPOSE Diffusion in restricted media, such as the neuron axons, is nonGaussian 1. Mean Apparent Propagator Magnetic Resonance Imaging (MAPMRI 2,3 ) is a reconstruction model for diffusion MRI which is able to estimating the nonGaussianity 2 (NG) of the diffusion signal. This study aims at investigating the minimum requirements of a diffusion weighted acquisition, in terms of Signal to Noise Ratio (SNR) and maximum bvalue, for MAPMRI to capture the NG of the signal. METHODS We used Camino (http://camino.cs.ucl.ac.uk/) MonteCarlo to simulate the diffusion signal inside a pack of parallel cylinders oriented along the z axis (100000 spins 4 , 1000 timesteps 4, radius 0.5μm, and 0.1μm of space between the cylinders). We acquired the diffusion signal using 10 different sampling schemes, Δ=57.9ms, δ=13.8ms, TE=91.3ms, and considering 10 b 0, 60 directions at bvalue= 700s/mm 2 ( first shell), and 60 directions at bvalues 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, and 10000 s/mm 2, r espectively, per sampling scheme (second shell). We fit both Diffusion Tensor Imaging 5 (DTI) and MAPMRI to the simulated data and the NG index was computed using MAPMRI. Successively, the Mean Squared Error (MSE) between ground truth and fitted signal was calculated for both DTI and MAPMRI. The same analyses were performed also adding Rician Noise to the diffusion signal with SNR=[40, 30, 20] considering 100 different instances of noise per sampling scheme. The MSE in this case was calculated with respect to the noiseless ground truth signal.
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