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To synthesize diffusion MR measurements from Monte-Carlo simulation usingtissue models with sizes comparable to those of scan voxels. Larger regionsenable restricting structures to be modeled in greater detail and improveaccuracy and precision in synthesized diffusion-weighted measurements.  We employ a localized intersection checking algorithm during substrateconstruction and dynamical simulation. Although common during dynamicssimulation, a dynamically constructed intersection map is also applied hereduring substrate construction, facilitating construction of much largersubstrates than would be possible with a naive "brute-force" approach. Weinvestigate the approach's performance using a packed cylinder model of whitematter, investigating optimal execution time for simulations, convergence ofsynthesized signals and variance in diffusion-weighted measurements over a widerange of acquisition parameters. The scheme is demonstrated with cylinder-basedsubstrates but is also readily applicable to other geometric primitives, suchas spheres or triangles.  The algorithm enables models with far larger substrates to be run with noadditional computational cost. The improved sampling reduces bias and variancein synthetic measurements.  The new method improves accuracy, precision, and reproducibility of syntheticmeasurements in Monte-Carlo simulation-based data synthesis. The largersubstrates it makes possible are better able to capture the complexity of thetissue we are modeling, leading to reduced bias and variance in synthesiseddata, compared to existing implementation of MC simulations.

Realistic voxel sizes and reduced signal variation in Monte-Carlo simulation for diffusion MR data synthesis