Melisa Lucía Giménez1,2, Pablo Jiménez1,2, Leandro Andrés Pedraza Pérez1,2, Diana Betancourth2, Analía Zwick2,3,4, and Gonzalo Agustín Álvarez1,2,3,4
1Departamento de Física Médica, Instituto Balseiro, Universidad Nacional de Cuyo, CNEA, San Carlos de Bariloche, Argentina, 2Centro Atómico Bariloche, CNEA, San Carlos de Bariloche, Argentina, 3Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET), San Carlos de Bariloche, Argentina, 4Instituto de Nanociencia y Nanotecnología, CNEA,CONICET, San Carlos de Bariloche, Argentina
1Departamento de Física Médica, Instituto Balseiro, Universidad Nacional de Cuyo, CNEA, San Carlos de Bariloche, Argentina, 2Centro Atómico Bariloche, CNEA, San Carlos de Bariloche, Argentina, 3Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina (CONICET), San Carlos de Bariloche, Argentina, 4Instituto de Nanociencia y Nanotecnología, CNEA,CONICET, San Carlos de Bariloche, Argentina
We show using simulations and proof-of-principle experiments with phantoms that mimic axon-bundles, that optimal estimation of the underlying microstructure-size distribution is obtained either using sharp or smooth gradient spin-echo modulations.
Figure 3. Comparison of the information gain for estimating restriction-size distributions with NOGSE trapezoidal and sinusoidal modulations. (a) Difference between the information gain (sensitivity) of the trapezoidal and sinusoidal modulations as a function of the mean and standard deviation values for a Lognormal distribution. Colored squares represent the measured size distributions of the prepared phantoms. (b) Restriction-size distribution reconstruction using NOGSE of the used phantoms. They correspond to the colored squares in (a).
Figure 1. Transverse plane MRI of a phantom of aramid fibers bundles immersed in water in a 50 ml plastic tube. Different packing densities were designed for producing different size distributions.
