TY - GEN
T1 - Fast dictionary generation and searching for magnetic resonance fingerprinting
AU - Xie, Jun
AU - Lyu, Mengye
AU - Zhang, Jian
AU - Hui, Edward S.
AU - Wu, Ed X.
AU - Wang, Ze
N1 - Funding Information:
*Research supported by Hangzhou Qianjiang Endowed Professor Program and the Youth 1000 Talent Program of China, National Natural Science Foundation of China (No. 61671198) and Hangzhou Innovation Seed Fund.
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - A super-fast dictionary generation and searching (DGS) algorithm was developed for MR parameter quantification using magnetic resonance fingerprinting (MRF). MRF is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But it has a multiplicative computation complexity, resulting in a big burden of dictionary generating, saving, and retrieving, which can easily be intractable for any state-of-art computers. Based on retrospective analysis of the dictionary matching object function, a multi-scale ZOOM like DGS algorithm, dubbed as MRF-ZOOM, was proposed. MRF ZOOM is quasi-parameter-separable so the multiplicative computation complexity is broken into additive one. Evaluations showed that MRF ZOOM was hundreds or thousands of times faster than the original MRF parameter quantification method even without counting the dictionary generation time in. Using real data, it yielded nearly the same results as produced by the original method. MRF ZOOM provides a super-fast solution for MR parameter quantification.
AB - A super-fast dictionary generation and searching (DGS) algorithm was developed for MR parameter quantification using magnetic resonance fingerprinting (MRF). MRF is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But it has a multiplicative computation complexity, resulting in a big burden of dictionary generating, saving, and retrieving, which can easily be intractable for any state-of-art computers. Based on retrospective analysis of the dictionary matching object function, a multi-scale ZOOM like DGS algorithm, dubbed as MRF-ZOOM, was proposed. MRF ZOOM is quasi-parameter-separable so the multiplicative computation complexity is broken into additive one. Evaluations showed that MRF ZOOM was hundreds or thousands of times faster than the original MRF parameter quantification method even without counting the dictionary generation time in. Using real data, it yielded nearly the same results as produced by the original method. MRF ZOOM provides a super-fast solution for MR parameter quantification.
UR - http://www.scopus.com/inward/record.url?scp=85032177690&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8037551
DO - 10.1109/EMBC.2017.8037551
M3 - Conference article published in proceeding or book
C2 - 29060592
AN - SCOPUS:85032177690
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3256
EP - 3259
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -