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Fast T2 Mapping with Multiple Echo, Caesar Cipher Acquisition and Model-Based Reconstruction

delete2014-04-18
delete17
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OA
AI
C
Christopher L. Lankford
R
Richard Dortch
M
Mark D. Does *
DOI:10.1002/mrm.25221delete
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摘要

摘要

En 中文
PurposeFast, quantitative T-2 mapping is of value to both clinical and research environments. However, many protocols utilizing fast spin echo (FSE) pulse sequences contain acceleration-induced artifacts that are compounded when fitting parameter maps, especially in the presence of imperfect refocusing. This work presents a B-1-corrected, model-based reconstruction and associated Cartesian FSE phase-encode ordering that provides enhanced accuracy in T-2 estimates compared with other common accelerated protocols. Theory and MethodsThe method, known as multiple echo, Caesar cipher acquisition and model-based reconstruction (ME-CAMBREC), directly fitted T-2, flip angle, and proton density maps on a row-by-row basis to k-space data using the extended phase graph model. Regularization was enforced in order to minimize noise amplification effects. ME-CAMBREC was evaluated in computational and physical phantoms, as well as human brain, and compared with other FSE-based T-2 mapping protocols, DESPOT2, and parallel imaging acceleration. ResultsIn computational, phantom, and human experiments, ME-CAMBREC provided T-2 maps with fewer artifacts and less or similar error compared with other methods tested at moderate-to-high acceleration factors. In vivo, ME-CAMBREC provided error rates approximately one-half those of other methods. ConclusionDirectly fitting multi-echo data to k-space using the extended phase graph can increase fidelity of T-2 maps significantly, especially when using an appropriate phase-encode ordering. Magn Reson Med 73:1065-1074, 2015. (c) 2014 Wiley Periodicals, Inc.
Keyword:
T-2
fast imaging
extended phase graph
model-based reconstruction
nonlinear reconstruction

期刊

Magnetic Resonance in Medicine 封面图
Magnetic Resonance in Medicine
IF:
3
论文数:
1.2W
被引数:
3.1W

机构

V
vanderbilt university
学者数:
5.1W
论文数: 4.1W
被引数: 59
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