基于广义交叉验证优化的奇异值分解对动态对比增强数据进行去卷积分析。

Deconvolution analysis of dynamic contrast-enhanced data based on singular value decomposition optimized by generalized cross validation.

作者信息

Murase Kenya, Yamazaki Youichi, Miyazaki Shohei

机构信息

Department of Medical Physics and Engineering, Division of Medical Technology and Science, Course of Health Science, Graduate School of Medicine, Osaka University, Suita, Japan.

出版信息

Magn Reson Med Sci. 2004;3(4):165-75. doi: 10.2463/mrms.3.165.

DOI:10.2463/mrms.3.165

PMID:16093635

Abstract

PURPOSE

To present an implementation of generalized cross validation (GCV) for automatically determining the regularization parameter--i.e., the threshold value in deconvolution analysis based on truncated singular value decomposition (TSVD) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data--and to investigate the usefulness of this approach in comparison with TSVD with a fixed threshold value (TSVD-F).

METHODS

Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various cerebral blood flows (CBFs), cerebral blood volumes (CBVs), and signal-to-noise ratios (SNRs) for three different types of residue functions (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The TSVD with GCV (TSVD-G) and TSVD-F with a fixed threshold value of 0.2 were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIF, and the estimated values were compared with the assumed values. Additionally, the optimal threshold value was determined from the threshold value in TSVD-F giving the mean CBF value closest to the assumed value and was compared with the threshold value determined with TSVD-G.

RESULTS

With TSVD-G, the CBF estimation was substantially improved over a wide range of CBFs for all types of residue functions at the cost of more noise than was seen with TSVD-F. The dependency of the threshold value determined with TSVD-G on the CBF, CBV, and SNR was similar to that of the optimal threshold value, with some discrepancy being observed for the box-shaped residue function, although they did not always agree in terms of absolute value.

CONCLUSION

Given an improved SNR, TSVD-G is useful for quantification of CBF with deconvolution analysis of DCE-MRI data.

摘要

目的

介绍一种广义交叉验证（GCV）的实现方法，用于自动确定正则化参数，即在基于动态对比增强磁共振成像（DCE-MRI）数据的截断奇异值分解（TSVD）的去卷积分析中的阈值，并与具有固定阈值的TSVD（TSVD-F）相比，研究该方法的实用性。

方法

使用计算机模拟，我们在三种不同类型的残留函数（指数型、三角型和箱型）下，针对各种脑血流量（CBF）、脑血容量（CBV）和信噪比（SNR），从建模为伽马变量函数的动脉输入函数（AIF）生成感兴趣体积（VOI）中造影剂的时间依赖性浓度。我们还考虑了AIF中的延迟和弥散效应。使用带有GCV的TSVD（TSVD-G）和固定阈值为0.2的TSVD-F，从VOI和AIF中模拟的浓度-时间曲线估计CBF值，并将估计值与假定值进行比较。此外，从TSVD-F中给出最接近假定值的平均CBF值的阈值确定最佳阈值，并与用TSVD-G确定的阈值进行比较。