Prospective acceleration of diffusion tensor imaging with compressed sensing using adaptive dictionaries

Journal article

Purpose: Diffusion MRI requires acquisition of multiple diffusion‐weighted images, resulting in long scan times. Here, we investigate combining compressed sensing and a fast imaging sequence to dramatically reduce acquisition times in cardiac diffusion MRI.

Methods: Fully sampled and prospectively undersampled diffusion tensor imaging data were acquired in five rat hearts at acceleration factors of between two and six using a fast spin echo (FSE) sequence. Images were reconstructed using a compressed sensing framework, enforcing sparsity by means of decomposition by adaptive dictionaries. A tensor was fit to the reconstructed images and fiber tractography was performed.

Results: Acceleration factors of up to six were achieved, with a modest increase in root mean square error of mean apparent diffusion coefficient (ADC), fractional anisotropy (FA), and helix angle. At an acceleration factor of six, mean values of ADC and FA were within 2.5% and 5% of the ground truth, respectively. Marginal differences were observed in the fiber tracts.

Conclusion: We developed a new k‐space sampling strategy for acquiring prospectively undersampled diffusion‐weighted data, and validated a novel compressed sensing reconstruction algorithm based on adaptive dictionaries. The k‐space undersampling and FSE acquisition each reduced acquisition times by up to 6× and 8×, respectively, as compared to fully sampled spin echo imaging. Magn Reson Med 76:248–258, 2016.

Authors: McClymont, D; Teh, I; Whittington, H; Grau, V; Schneider, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Magnetic Resonance in Medicine
• Volume: 76
• Issue: 1
• Pages: 248-258
• Publication date: 2015-08-24
• Acceptance date: 2015-07-16
• DOI: 10.1002/mrm.25876
• EISSN: 1522-2594
• ISSN: 0740-3194
• Pmid: 26302363
• Language: English
• Keywords: heart structure; adaptive dictionaries; diffusion tensor imaging; SBTMR; prospective undersampling; diffusion MRI; compressed sensing