School of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
Department of Radiology, Temple University, Philadelphia, USA.
Biomed Eng Online. 2019 Feb 4;18(1):12. doi: 10.1186/s12938-019-0631-8.
Arterial spin labeling (ASL) provides a noninvasive way to measure cerebral blood flow (CBF). The CBF estimation from ASL is heavily contaminated by noise and the partial volume (PV) effect. The multiple measurements of perfusion signals in the ASL sequence are generally acquired and were averaged to suppress the noise. To correct the PV effect, several methods were proposed, but they were all performed directly on the averaged image, thereby ignoring the inherent perfusion information of mixed tissues that are embedded in multiple measurements. The aim of the present study is to correct the PV effect of ASL sequence using the inherent perfusion information in the multiple measurements.
In this study, we first proposed a statistical perfusion model of mixed tissues based on the distribution of multiple measurements. Based on the tissue mixture that was obtained from the high-resolution structural image, a structure-based expectation maximization (sEM) scheme was developed to estimate the perfusion contributions of different tissues in a mixed voxel from its multiple measurements. Finally, the performance of the proposed method was evaluated using both computer simulations and in vivo data.
Compared to the widely used linear regression (LR) method, the proposed sEM-based method performs better on edge preservation, noise suppression, and lesion detection, and demonstrates a potential to estimate the CBF within a shorter scanning time. For in vivo data, the corrected CBF values of gray matter (GM) were independent of the GM probability, thereby indicating the effectiveness of the sEM-based method for the PV correction of the ASL sequence.
This study validates the proposed sEM scheme for the statistical perfusion model of mixed tissues and demonstrates the effectiveness of using inherent perfusion information in the multiple measurements for PV correction of the ASL sequence.
动脉自旋标记（ASL）提供了一种无创的方法来测量脑血流（CBF）。ASL 中的 CBF 估计受到噪声和部分容积（PV）效应的严重污染。ASL 序列中的灌注信号通常会进行多次测量并进行平均，以抑制噪声。为了校正 PV 效应，提出了几种方法，但它们都是直接在平均图像上进行的，从而忽略了嵌入在多次测量中的混合组织的固有灌注信息。本研究的目的是使用多次测量中的固有灌注信息来校正 ASL 序列的 PV 效应。
在本研究中，我们首先基于多次测量的分布提出了一种混合组织的统计灌注模型。基于从高分辨率结构图像中获得的组织混合物，开发了一种基于结构的期望最大化（sEM）方案，以从混合体素的多个测量中估计不同组织的灌注贡献。最后，使用计算机模拟和体内数据评估了所提出方法的性能。
与广泛使用的线性回归（LR）方法相比，所提出的基于 sEM 的方法在边缘保持、噪声抑制和病灶检测方面表现更好，并具有在更短的扫描时间内估计 CBF 的潜力。对于体内数据，灰质（GM）的校正 CBF 值与 GM 概率无关，这表明 sEM 基于方法对于 ASL 序列的 PV 校正有效。
本研究验证了所提出的基于 sEM 的混合组织统计灌注模型方案，并证明了在多次测量中使用固有灌注信息校正 ASL 序列的 PV 效应的有效性。
