Keywords: Data Processing, Diffusion/other diffusion imaging techniquesA naive answer to this question is to loop over all voxels re-do the training and apply machine learning estimators of your favorite model. This is grossly computationally inefficient. We propose a matrix pseudoinversion-based method that can estimate nonlinear biophysical model parameters from a large set of voxels with independent acquisition protocols in a few minutes. Our framework can be tailored to any convolution-based model. Furthermore, the protocols are not required to have shells and there are no limits to the protocol differences among voxels. This method is readily extendable for simultaneously varying diffusion times, B-tensor shapes, TE, etc.
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