Sparse MRI: The application of compressed sensing for rapid MR imaging

• Published in: Magnetic resonance in medicine Vol. 58; no. 6; pp. 1182 - 1195
• Main Authors: Lustig, Michael, Donoho, David, Pauly, John M.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.12.2007
• Subjects: Algorithms; Artificial Intelligence; Brain - anatomy & histology; compressed sensing; compressive sampling; Data Compression - methods; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - instrumentation; Magnetic Resonance Imaging - methods; nonlinear reconstruction; Pattern Recognition, Automated - methods; Phantoms, Imaging; random sampling; rapid MRI; Reproducibility of Results; Sensitivity and Specificity; sparse reconstruction; sparsity
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.21391

The sparsity which is implicit in MR images is exploited to significantly undersample k‐space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finite‐differences or their wavelet coefficients. According to the recently developed mathematical theory of compressed‐sensing, images with a sparse representation can be recovered from randomly undersampled k‐space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise‐like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo‐random variable‐density undersampling of phase‐encodes. The reconstruction is performed by minimizing the ℓ1 norm of a transformed image, subject to data fidelity constraints. Examples demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin‐echo brain imaging and 3D contrast enhanced angiography. Magn Reson Med, 2007. © 2007 Wiley‐Liss, Inc.