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Article: Rigid motion correction for magnetic resonance fingerprinting with sliding-window reconstruction and image registration

TitleRigid motion correction for magnetic resonance fingerprinting with sliding-window reconstruction and image registration
Authors
KeywordsMagnetic resonance fingerprinting
Sliding-window reconstruction
Motion correction
Image registration
Issue Date2019
PublisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/mri
Citation
Magnetic Resonance Imaging, 2019, v. 57, p. 303-312 How to Cite?
AbstractMagnetic resonance fingerprinting (MRF) can be used to simultaneously obtain multiple parameter maps from a single pulse sequence. However, patient motion during MRF acquisition may result in blurring and artifacts in estimated parameter maps. In this work, a novel motion correction method was proposed to correct for rigid motion in MRF. The proposed method involved sliding-window reconstruction to obtain intermediate images followed by image registration to estimate rigid motion information between these images. Finally, the motion-corrupted k-space data were corrected with the estimated motion parameters and then reconstructed to obtain the parameter maps via the conventional MRF processing pipeline. The proposed method was evaluated using both simulations and in vivo MRF experiments with intently different types of motion. For motion-corrupted data, the proposed method yielded brain T1, T2 and proton density maps with obviously reduced blurring and artifacts and lower normalized root-mean-square error, compared to MRF without motion correction. In conclusion, motion-corrected MRF using the proposed method has the potential to produce accurate parameter maps in the presence of in-plane rigid motion.
Persistent Identifierhttp://hdl.handle.net/10722/279156
ISSN
2017 Impact Factor: 2.564
2015 SCImago Journal Rankings: 1.139

 

DC FieldValueLanguage
dc.contributor.authorXu, Z-
dc.contributor.authorYe, H-
dc.contributor.authorLyu, M-
dc.contributor.authorHe, H-
dc.contributor.authorZhong, J-
dc.contributor.authorMei, Y-
dc.contributor.authorChen, Z-
dc.contributor.authorWu, EX-
dc.contributor.authorChen, W-
dc.contributor.authorFeng, Q-
dc.contributor.authorFeng, Y-
dc.date.accessioned2019-10-21T02:20:38Z-
dc.date.available2019-10-21T02:20:38Z-
dc.date.issued2019-
dc.identifier.citationMagnetic Resonance Imaging, 2019, v. 57, p. 303-312-
dc.identifier.issn0730-725X-
dc.identifier.urihttp://hdl.handle.net/10722/279156-
dc.description.abstractMagnetic resonance fingerprinting (MRF) can be used to simultaneously obtain multiple parameter maps from a single pulse sequence. However, patient motion during MRF acquisition may result in blurring and artifacts in estimated parameter maps. In this work, a novel motion correction method was proposed to correct for rigid motion in MRF. The proposed method involved sliding-window reconstruction to obtain intermediate images followed by image registration to estimate rigid motion information between these images. Finally, the motion-corrupted k-space data were corrected with the estimated motion parameters and then reconstructed to obtain the parameter maps via the conventional MRF processing pipeline. The proposed method was evaluated using both simulations and in vivo MRF experiments with intently different types of motion. For motion-corrupted data, the proposed method yielded brain T1, T2 and proton density maps with obviously reduced blurring and artifacts and lower normalized root-mean-square error, compared to MRF without motion correction. In conclusion, motion-corrected MRF using the proposed method has the potential to produce accurate parameter maps in the presence of in-plane rigid motion.-
dc.languageeng-
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/mri-
dc.relation.ispartofMagnetic Resonance Imaging-
dc.subjectMagnetic resonance fingerprinting-
dc.subjectSliding-window reconstruction-
dc.subjectMotion correction-
dc.subjectImage registration-
dc.titleRigid motion correction for magnetic resonance fingerprinting with sliding-window reconstruction and image registration-
dc.typeArticle-
dc.identifier.emailLyu, M: mylyu@connect.hku.hk-
dc.identifier.emailWu, EX: ewu@eee.hku.hk-
dc.identifier.authorityWu, EX=rp00193-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.mri.2018.11.001-
dc.identifier.pmid30439513-
dc.identifier.scopuseid_2-s2.0-85059171987-
dc.identifier.hkuros307649-
dc.identifier.volume57-
dc.identifier.spage303-
dc.identifier.epage312-
dc.publisher.placeUnited States-

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