MP-RAVE: IR-Prepared T1-Weighted Radial Stack-of-Stars 3D GRE imaging with retrospective motion correction

Eddy Solomon, Eyal Lotan, Elcin Zan, Daniel K. Sodickson, Kai Tobias Block, Hersh Chandarana

Research output: Contribution to journalArticlepeer-review


Purpose: To describe an inversion-recovery T1-weighted radial stack-of-stars 3D gradient echo (GRE) sequence with comparable image quality to conventional MP-RAGE and to demonstrate how the radial acquisition scheme can be utilized for additional retrospective motion correction to improve robustness to head motion. Methods: The proposed sequence, named MP-RAVE, has been derived from a previously described radial stack-of-stars 3D GRE sequence (RAVE) and includes a 180° inversion recovery pulse that is generated once for every stack of radial views. The sequence is combined with retrospective 3D motion correction to improve robustness. The effectiveness has been evaluated in phantoms and healthy volunteers and compared to conventional MP-RAGE acquisition. Results: MP-RAGE and MP-RAVE anatomical images were rated “good” to “excellent” in overall image quality, with artifact level between “mild” and “no artifacts”, and with no statistically significant difference between methods. During head motion, MP-RAVE showed higher inherent robustness with artifacts confined to local brain regions. In combination with motion correction, MP-RAVE provided noticeably improved image quality during different head motion and showed statistically significant improvement in image sharpness. Conclusion: MP-RAVE provides comparable image quality and contrast to conventional MP-RAGE with improved robustness to head motion. In combination with retrospective 3D motion correction, MP-RAVE can be a useful alternative to MP-RAGE, especially in non-cooperative or pediatric patients.

Original languageEnglish (US)
Pages (from-to)202-210
Number of pages9
JournalMagnetic resonance in medicine
Issue number1
StatePublished - Jul 2023


  • motion correction
  • radial sampling

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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