Motion robust magnetic resonance imaging via efficient Fourier aggregation

Oren Solomon, Rémi Patriat, Henry C Braun, Tara E Palnitkar, Steen Moeller, Edward J. Auerbach, Kamil Ugurbil, Guillermo Sapiro, Noam Harel

Research output: Contribution to journalArticlepeer-review

Abstract

We present a method for suppressing motion artifacts in anatomical magnetic resonance acquisitions. Our proposed technique, termed MOTOR-MRI, can recover and salvage images which are otherwise heavily corrupted by motion induced artifacts and blur which renders them unusable. Contrary to other techniques, MOTOR-MRI operates on the reconstructed images and not on k-space data. It relies on breaking the standard acquisition protocol into several shorter ones (while maintaining the same total acquisition time) and subsequent efficient aggregation in Fourier space of locally sharp and consistent information among them, producing a sharp and motion mitigated image. We demonstrate the efficacy of the technique on T2-weighted turbo spin echo magnetic resonance brain scans with severe motion corruption from both 3 T and 7 T scanners and show significant qualitative and quantitative improvement in image quality. MOTOR-MRI can operate independently, or in conjunction with additional motion correction methods.

Original languageEnglish (US)
Article number102638
JournalMedical Image Analysis
Volume83
DOIs
StatePublished - Jan 2023

Bibliographical note

Funding Information:
This study was funded by the following National Institution of Health Grants: R01 NS081118 , R01 NS113746 , S10 OD025256 , P41EB027061 and P50 NS123109 .

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Fourier aggregation
  • Image deblurring
  • Magnetic resonance imaging
  • Motion artifacts
  • Robust imaging

PubMed: MeSH publication types

  • Journal Article

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