Tim Sprenger1, 2, Brice Fernandez3, Jonathan I. Sperl1, Vladimir Golkov1, 2, Michael Bach4, Ek T. Tan5, Kevin F. King6, Christopher J. Hardy5, Luca Marinelli5, Michael Czisch7, Philipp Smann7, Axel Haase8, Marion I. Menzel1
1GE Global Research, Garching, Germany; 2Technical University Munich, Garching, Germany; 3GE Healthcare, Munich, Germany; 4German Cancer Research Center, Heidelberg, Germany; 5GE Global Research, Niskayuna, NY, United States; 6GE Healthcare, Waukesha, WI, United States; 7Max Planck Institute for Psychiatry, Munich, Germany; 8Technische Universitt Mnchen, Garching, Germany
Conventional diffusion spectrum imaging (DSI) requires Nyquist or full-sampling of q-space, and hence suffers from long acquisition times. To overcome this limitation, compressed-sensing-accelerated DSI has been proposed recently. For this technique q-space is randomly undersampled and subsequently reconstructed exploiting the signal sparsity in an appropriate transform domain. This work studies the SNR-dependent performance of compressed sensing-accelerated DSI using a fiber crossing phantom and by that validates the superior signal recovery and denoising properties.