دورية أكاديمية
أعرض في EDS

Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF.

التفاصيل البيبلوغرافية
العنوان: Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF.
المؤلفون: Mehta BB; Department of Radiology, Case Western Reserve University, Cleveland, Ohio., Ma D; Department of Radiology, Case Western Reserve University, Cleveland, Ohio., Pierre EY; Imaging Division, The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia., Jiang Y; Department of Radiology, Case Western Reserve University, Cleveland, Ohio., Coppo S; Department of Radiology, Case Western Reserve University, Cleveland, Ohio., Griswold MA; Department of Radiology, Case Western Reserve University, Cleveland, Ohio.; Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
المصدر: Magnetic resonance in medicine [Magn Reson Med] 2018 Dec; Vol. 80 (6), pp. 2485-2500. Date of Electronic Publication: 2018 May 06.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Wiley Country of Publication: United States NLM ID: 8505245 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1522-2594 (Electronic) Linking ISSN: 07403194 NLM ISO Abbreviation: Magn Reson Med Subsets: MEDLINE
أسماء مطبوعة: Publication: 1999- : New York, NY : Wiley
Original Publication: San Diego : Academic Press,
مواضيع طبية MeSH: Magnetic Resonance Imaging*, Brain/*diagnostic imaging , Image Processing, Computer-Assisted/*methods, Algorithms ; Artifacts ; Databases, Factual ; Humans ; Motion ; Pattern Recognition, Automated ; Phantoms, Imaging ; Prospective Studies ; Retrospective Studies
مستخلص: Purpose: The purpose of this study is to increase the robustness of MR fingerprinting (MRF) toward subject motion.
Methods: A novel reconstruction algorithm, MOtion insensitive MRF (MORF), was developed, which uses an iterative reconstruction based retrospective motion correction approach. Each iteration loops through the following steps: pattern recognition, metric based identification of motion corrupted frames, registration based motion estimation, and motion compensated data consistency verification. The proposed algorithm was validated using in vivo 2D brain MRF data with retrospective in-plane motion introduced at different stages of the acquisition. The validation was performed using qualitative and quantitative comparisons between results from MORF, the iterative multi-scale (IMS) algorithm, and with the IMS results using data without motion for a ground truth comparison. Additionally, the MORF algorithm was evaluated in prospectively motion corrupted in vivo 2D brain MRF datasets.
Results: For datasets corrupted by in-plane motion both prospectively and retrospectively, MORF noticeably reduced motion artifacts compared with iterative multi-scale and closely resembled the results from data without motion, even when ∼54% of data was motion corrupted during different parts of the acquisition.
Conclusions: MORF improves the insensitivity of MRF toward rigid-body motion occurring during any part of the MRF acquisition.
(© 2018 International Society for Magnetic Resonance in Medicine.)
معلومات مُعتمدة: R21 EB026764 United States EB NIBIB NIH HHS; 1R01EB016728-01A1 United States GF NIH HHS; 5R01EB017219-02 United States GF NIH HHS; International Siemens Healthcare
فهرسة مساهمة: Keywords: MR fingerprinting; motion compensation; motion estimation; pattern recognition; quantitative imaging; relaxation time
تواريخ الأحداث: Date Created: 20180508 Date Completed: 20190930 Latest Revision: 20190930
رمز التحديث: 20221213
DOI: 10.1002/mrm.27227
PMID: 29732610
قاعدة البيانات: MEDLINE
الوصف
تدمد:1522-2594
DOI:10.1002/mrm.27227