Accelerated cardiac diffusion tensor imaging using joint low-rank and sparsity constraints

Sen Ma, Christopher T. Nguyen, Anthony G. Christodoulou, Daniel Luthringer, Jon Kobashigawa, Sang Eun Lee, Hyuk Jae Chang, Debiao Li

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Objective: The purpose of this paper is to accelerate cardiac diffusion tensor imaging (CDTI) by integrating low-rankness and compressed sensing. Methods: Diffusion-weighted images exhibit both transform sparsity and low-rankness. These properties can jointly be exploited to accelerate CDTI, especially when a phase map is applied to correct for the phase inconsistency across diffusion directions, thereby enhancing low-rankness. The proposed method is evaluated both ex vivo and in vivo, and is compared to methods using either a low-rank or sparsity constraint alone. Results: Compared to using a low-rank or sparsity constraint alone, the proposed method preserves more accurate helix angle features, the transmural continuum across the myocardium wall, and mean diffusivity at higher acceleration, while yielding significantly lower bias and higher intraclass correlation coefficient. Conclusion: Low-rankness and compressed sensing together facilitate acceleration for both ex vivo and in vivo CDTI, improving reconstruction accuracy compared to employing either constraint alone. Significance: Compared to previous methods for accelerating CDTI, the proposed method has the potential to reach higher acceleration while preserving myofiber architecture features, which may allow more spatial coverage, higher spatial resolution, and shorter temporal footprint in the future.

Original languageEnglish
Article number8239837
Pages (from-to)2219-2230
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number10
Publication statusPublished - 2018 Oct

Bibliographical note

Funding Information:
Manuscript received July 25, 2017; revised November 21, 2017; accepted December 18, 2017. Date of publication December 25, 2017; date of current version September 18, 2018. This work was supported by the U.S. National Institutes of Health under Grants 1R01HL124649, R21 EB024701-01, and T32HL116273. (Corresponding author: Debiao Li.) S. Ma is with the Department of Bioengineering, University of California, and also with the Biomedical Imaging Research Institute, Cedars-Sinai Medical Center.

Funding Information:
The authors would acknowledge the Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) (2012027176) for the support of patient recruitment and in vivo data collection.

Publisher Copyright:
© 1964-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering


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