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- Publisher Website: 10.1016/j.mri.2019.11.010
- Scopus: eid_2-s2.0-85074796160
- PMID: 31726211
- WOS: WOS:000504801100018
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Article: MRF-ZOOM for the unbalanced steady-state free precession (ubSSFP) magnetic resonance fingerprinting
Title | MRF-ZOOM for the unbalanced steady-state free precession (ubSSFP) magnetic resonance fingerprinting |
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Authors | |
Keywords | Magnetic resonance fingerprinting Fast searching ubSSFP FISP T1 |
Issue Date | 2020 |
Publisher | Elsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/mri |
Citation | Magnetic Resonance Imaging, 2020, v. 65, p. 146-154 How to Cite? |
Abstract | In magnetic resonance fingerprinting (MRF), tissue parameters are determined by finding the best-match to the acquired MR signal from a predefined signal dictionary. This dictionary searching (DS) process is generally performed in an exhaustive manner, which requires a large predefined dictionary and long searching time. A fast MRF DS algorithm, MRF-ZOOM, was recently proposed based on DS objective function optimization. As a proof-of-concept study, MRF-ZOOM was only tested with one of the earliest MRF sequences but not with the recently more popular unbalanced steady state free precession MRF sequence (MRF-ubSSFP, or MRF-FISP). Meanwhile noise effects on MRF and MRF-ZOOM have not been examined. The purpose of this study was to address these open questions and to verify whether MRF-ZOOM can be combined with a dictionary-compression based method to gain further speed. Numerical simulations were performed to evaluate the DS objective function properties, noise effects on MRF, and to compare MRF-ZOOM with other methods in terms of speed and accuracy. In-vivo experiments were performed as well. Evaluation results showed that premises of MRF-ZOOM held for MRF-FISP; noise did not affect MRF-ZOOM more than the conventional MRF method; when SNR ≥ 1, MRF quantification yielded accurate results. Dictionary compression introduced quantification errors more to T2 quantification. MRF-ZOOM was thousands of times faster than the conventional MRF method. Combining MRF-ZOOM with dictionary compression showed no benefit in terms of fitting speed. In conclusion, MRF-ZOOM is valid for MRF- FISP, and can remarkably save MRF dictionary generation and searching time without sacrificing matching accuracy. |
Persistent Identifier | http://hdl.handle.net/10722/280270 |
ISSN | 2023 Impact Factor: 2.1 2023 SCImago Journal Rankings: 0.647 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, Z | - |
dc.contributor.author | Cui, D | - |
dc.contributor.author | Zhang, J | - |
dc.contributor.author | Wu, EX | - |
dc.contributor.author | Hui, ES | - |
dc.date.accessioned | 2020-01-21T11:51:00Z | - |
dc.date.available | 2020-01-21T11:51:00Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Magnetic Resonance Imaging, 2020, v. 65, p. 146-154 | - |
dc.identifier.issn | 0730-725X | - |
dc.identifier.uri | http://hdl.handle.net/10722/280270 | - |
dc.description.abstract | In magnetic resonance fingerprinting (MRF), tissue parameters are determined by finding the best-match to the acquired MR signal from a predefined signal dictionary. This dictionary searching (DS) process is generally performed in an exhaustive manner, which requires a large predefined dictionary and long searching time. A fast MRF DS algorithm, MRF-ZOOM, was recently proposed based on DS objective function optimization. As a proof-of-concept study, MRF-ZOOM was only tested with one of the earliest MRF sequences but not with the recently more popular unbalanced steady state free precession MRF sequence (MRF-ubSSFP, or MRF-FISP). Meanwhile noise effects on MRF and MRF-ZOOM have not been examined. The purpose of this study was to address these open questions and to verify whether MRF-ZOOM can be combined with a dictionary-compression based method to gain further speed. Numerical simulations were performed to evaluate the DS objective function properties, noise effects on MRF, and to compare MRF-ZOOM with other methods in terms of speed and accuracy. In-vivo experiments were performed as well. Evaluation results showed that premises of MRF-ZOOM held for MRF-FISP; noise did not affect MRF-ZOOM more than the conventional MRF method; when SNR ≥ 1, MRF quantification yielded accurate results. Dictionary compression introduced quantification errors more to T2 quantification. MRF-ZOOM was thousands of times faster than the conventional MRF method. Combining MRF-ZOOM with dictionary compression showed no benefit in terms of fitting speed. In conclusion, MRF-ZOOM is valid for MRF- FISP, and can remarkably save MRF dictionary generation and searching time without sacrificing matching accuracy. | - |
dc.language | eng | - |
dc.publisher | Elsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/mri | - |
dc.relation.ispartof | Magnetic Resonance Imaging | - |
dc.subject | Magnetic resonance fingerprinting | - |
dc.subject | Fast searching | - |
dc.subject | ubSSFP | - |
dc.subject | FISP | - |
dc.subject | T1 | - |
dc.title | MRF-ZOOM for the unbalanced steady-state free precession (ubSSFP) magnetic resonance fingerprinting | - |
dc.type | Article | - |
dc.identifier.email | Cui, D: cuidi00@hku.hk | - |
dc.identifier.email | Wu, EX: ewu@eee.hku.hk | - |
dc.identifier.email | Hui, ES: edshui@hku.hk | - |
dc.identifier.authority | Wu, EX=rp00193 | - |
dc.identifier.authority | Hui, ES=rp01832 | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.1016/j.mri.2019.11.010 | - |
dc.identifier.pmid | 31726211 | - |
dc.identifier.pmcid | PMC6907731 | - |
dc.identifier.scopus | eid_2-s2.0-85074796160 | - |
dc.identifier.hkuros | 308990 | - |
dc.identifier.hkuros | 311195 | - |
dc.identifier.volume | 65 | - |
dc.identifier.spage | 146 | - |
dc.identifier.epage | 154 | - |
dc.identifier.isi | WOS:000504801100018 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0730-725X | - |