Abstract:
PURPOSE:MR fingerprinting (MRF) is a technique for quantitative tissue mapping using pseudorandom measurements. To estimate tissue properties such as T1 , T2 , proton density, and B0 , the rapidly acquired data are compared against a large dictionary of Bloch simulations. This matching process can be a very computationally demanding portion of MRF reconstruction. THEORY AND METHODS:We introduce a fast group matching algorithm (GRM) that exploits inherent correlation within MRF dictionaries to create highly clustered groupings of the elements. During matching, a group specific signature is first used to remove poor matching possibilities. Group principal component analysis (PCA) is used to evaluate all remaining tissue types. In vivo 3 Tesla brain data were used to validate the accuracy of our approach. RESULTS:For a trueFISP sequence with over 196,000 dictionary elements, 1000 MRF samples, and image matrix of 128 × 128, GRM was able to map MR parameters within 2s using standard vendor computational resources. This is an order of magnitude faster than global PCA and nearly two orders of magnitude faster than direct matching, with comparable accuracy (1-2% relative error). CONCLUSION:The proposed GRM method is a highly efficient model reduction technique for MRF matching and should enable clinically relevant reconstruction accuracy and time on standard vendor computational resources.
journal_name
Magn Reson Medjournal_title
Magnetic resonance in medicineauthors
Cauley SF,Setsompop K,Ma D,Jiang Y,Ye H,Adalsteinsson E,Griswold MA,Wald LLdoi
10.1002/mrm.25439subject
Has Abstractpub_date
2015-08-01 00:00:00pages
523-8issue
2eissn
0740-3194issn
1522-2594journal_volume
74pub_type
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