Free-Breathing and Ungated Dynamic MRI Using Navigator-Less Spiral SToRM

Abdul Haseeb Ahmed, Ruixi Zhou, Yang Yang, Prashant Nagpal, Michael Salerno, Mathews Jacob

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from undersampled measurements; extensive cardiac and respiratory motion often results in the Casorati matrix not being sufficiently low-rank. Therefore, we exploit the non-linear structure of the dynamic data, which gives the low-rank kernel matrix. Unlike prior work that rely on navigators to estimate the manifold structure, we propose a kernel low-rank matrix completion method to directly fill in the missing k-space data from variable density spiral acquisitions. We validate the proposed scheme using simulated data and in-vivo data. Our results show that the proposed scheme provides improved reconstructions compared to the classical methods such as low-rank and XD-GRASP. The comparison with breath-held cine data shows that the quantitative metrics agree, whereas the image quality is marginally lower.

Original languageEnglish
Article number9137715
Pages (from-to)3933-3943
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number12
DOIs
StatePublished - Dec 2020

Keywords

  • Cardiac reconstruction
  • cardiac MRI
  • free-breathing
  • kernel methods
  • manifold models
  • non-ECG gated

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