Abstract #2092

SNR-Dependent Quality Assessment of Compressed-Sensing-Accelerated Diffusion Spectrum Imaging Using a Fiber Crossing Phantom

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.