A g‐factor metric for k‐t‐GRAPPA‐ and PEAK‐GRAPPA‐based parallel imaging
A g‐factor metric for k‐t‐GRAPPA‐ and PEAK‐GRAPPA‐based parallel imaging
复制标题
基于 kâtâGRAPPAâ 和 PEAKâGRAPPAâ 的并行成像的 gâfactor 度量
DOI:
10.1002/mrm.25386
复制
发表时间:
2014
影响因子:
3.3
通讯作者:
B. Jung
中科院分区:
文献类型:
--
作者:
R. Ramb;C. Binter;G. Schultz;J. Assländer;F. Breuer;M. Zaitsev;S. Kozerke;B. Jung
PurposeThe aim of this work is to derive a theoretical framework for quantitative noise and temporal fidelity analysis of time‐resolved k‐space‐based parallel imaging methods.TheoryAn analytical formalism of noise distribution is derived extending the existing g‐factor formulation for nontime‐resolved generalized autocalibrating partially parallel acquisition (GRAPPA) to time‐resolved k‐space‐based methods. The noise analysis considers temporal noise correlations and is further accompanied by a temporal filtering analysis.MethodsAll methods are derived and presented for k‐t‐GRAPPA and PEAK‐GRAPPA. A sliding window reconstruction and nontime‐resolved GRAPPA are taken as a reference. Statistical validation is based on series of pseudoreplica images. The analysis is demonstrated on a short‐axis cardiac CINE dataset.ResultsThe superior signal‐to‐noise performance of time‐resolved over nontime‐resolved parallel imaging methods at the expense of temporal frequency filtering is analytically confirmed. Further, different temporal frequency filter characteristics of k‐t‐GRAPPA, PEAK‐GRAPPA, and sliding window are revealed.ConclusionThe proposed analysis of noise behavior and temporal fidelity establishes a theoretical basis for a quantitative evaluation of time‐resolved reconstruction methods. Therefore, the presented theory allows for comparison between time‐resolved parallel imaging methods and also nontime‐resolved methods. Magn Reson Med 74:125–135, 2015. © 2014 Wiley Periodicals, Inc.