Can we predict motion artifacts in clinical MRI before the scan completes?

Malte Hoffmann; Nalini M Singh; Adrian V Dalca; Bruce Fischl; Robert Frost

Can we predict motion artifacts in clinical MRI before the scan completes?

Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson Med Sci Meet Exhib. 2023 May:2023:1372.

Authors

Malte Hoffmann^{1

2}, Nalini M Singh^{3

4}, Adrian V Dalca^{1

2

3}, Bruce Fischl^{1

2

3

4}, Robert Frost^{1

2}

Affiliations

¹ Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.
² Department of Radiology, Harvard Medical School, Boston, MA 02115, USA.
³ Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
⁴ Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA.

PMID: 37692094
PMCID: PMC10490829

Abstract

Subject motion can cause artifacts in clinical MRI, frequently necessitating repeat scans. We propose to alleviate this inefficiency by predicting artifact scores from partial multi-shot multi-slice acquisitions, which may guide the operator in aborting corrupted scans early.

Keywords: AI-guided radiology; brain; deep learning; image quality; neuroimaging.

Abstract

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