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快速准确的 T 映射信号偏移补偿。

Fast and accurate compensation of signal offset for T mapping.

机构信息

Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Botanická 68a, 602 00, Brno, Czech Republic.

Department of Radiology, Faculty of Medicine and Dentistry, Palacky University, tř. Svobody 8, 77126, Olomouc, Czech Republic.

出版信息

MAGMA. 2019 Aug;32(4):423-436. doi: 10.1007/s10334-019-00737-3. Epub 2019 Feb 7.

Abstract

OBJECTIVE

T maps are more vendor independent than other MRI protocols. Multi-echo spin-echo signal decays to a non-zero offset due to imperfect refocusing pulses and Rician noise, causing T overestimation by the vendor's 2-parameter algorithm. The accuracy of the T estimate is improved, if the non-zero offset is estimated as a third parameter. Three-parameter Levenberg-Marquardt (LM) T estimation takes several minutes to calculate, and it is sensitive to initial values. We aimed for a 3-parameter fitting algorithm that was comparably accurate, yet substantially faster.

METHODS

Our approach gains speed by converting the 3-parameter minimisation problem into an empirically unimodal univariate problem, which is quickly minimised using the golden section line search (GS).

RESULTS

To enable comparison, we propose a novel noise-masking algorithm. For clinical data, the agreement between the GS and the LM fit is excellent, yet the GS algorithm is two orders of magnitude faster. For synthetic data, the accuracy of the GS algorithm is on par with that of the LM fit, and the GS algorithm is significantly faster. The GS algorithm requires no parametrisation or initialisation by the user.

DISCUSSION

The new GS T mapping algorithm offers a fast and much more accurate off-the-shelf replacement for the inaccurate 2-parameter fit in the vendor's software.

摘要

目的

T 映射比其他 MRI 协议更具供应商独立性。由于不完全重聚焦脉冲和瑞利噪声,多回波自旋回波信号会衰减到非零偏移,从而导致供应商的 2 参数算法高估 T 值。如果将非零偏移估计为第三个参数,则可以提高 T 值的估计准确性。三参数 Levenberg-Marquardt(LM)T 估计需要几分钟的时间来计算,并且对初始值很敏感。我们的目标是开发一种具有可比性准确性但速度更快的三参数拟合算法。

方法

我们的方法通过将三参数最小化问题转换为经验上单峰的单变量问题来提高速度,然后使用黄金分割线搜索(GS)快速最小化该问题。

结果

为了进行比较,我们提出了一种新的噪声掩蔽算法。对于临床数据,GS 拟合与 LM 拟合之间的一致性非常好,但 GS 算法的速度要快两个数量级。对于合成数据,GS 算法的准确性与 LM 拟合相当,并且 GS 算法的速度要快得多。GS 算法不需要用户进行参数化或初始化。

讨论

新的 GS T 映射算法提供了一种快速且更准确的现成替代品,可替代供应商软件中不准确的 2 参数拟合。

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