Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data

Abstract

To accelerate the acquisition of simultaneously high spatial and angular resolution diffusion imaging. Accelerated imaging is achieved by recovering the diffusion signal at all voxels simultaneously from under-sampled k-q space data using a compressed sensing algorithm. The diffusion signal at each voxel is modeled as a sparse complex Gaussian mixture model. The joint recovery scheme enables incoherent under-sampling of the 5-D k-q space, obtained by randomly skipping interleaves of a multishot variable density spiral trajectory. This sampling and reconstruction strategy is observed to provide considerably improved reconstructions than classical k-q under-sampling and reconstruction schemes. The complex model enables to account for the noise statistics without compromising the computational efficiency and theoretical convergence guarantees. The reconstruction framework also incorporates compensation of motion induced phase errors that result from the multishot acquisition. Reconstructions of the diffusion signal from under-sampled data using the proposed method yields accurate results with errors less that 5% for different accelerations and b-values. The proposed method is also shown to perform better than standard k-q acceleration schemes. The proposed scheme can significantly accelerate the acquisition of high spatial and angular resolution diffusion imaging by accurately reconstructing crossing fiber architectures from under-sampled data.