Multi-Shell DTI suffers from low SNR for high b-value data and prolonged scan time. The Gaussian noise assumption is typically violated due to multi-coil imaging and magnitude forming thus requiring special treatment to avoid biases in the DTI estimates. To this end, we propose a model-based reconstruction technique to exploit the Gaussian noise in the raw k-space data and enable acceleration of the DTI measurement. We show the acceleration potential and quantitative accuracy of the proposed method for mono- and bi-exponential fitting approaches on freely available DTI data and full brain DTI measurements of one healthy volunteer.
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