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使用局部低秩和子空间建模的高保真体素内不相干运动参数映射。

High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling.

机构信息

Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA.

Department of Radiology, Stanford University, Stanford, CA, USA.

出版信息

Neuroimage. 2024 Apr 15;292:120601. doi: 10.1016/j.neuroimage.2024.120601. Epub 2024 Apr 7.

Abstract

PURPOSE

Intravoxel incoherent motion (IVIM) is a quantitative magnetic resonance imaging (MRI) method used to quantify perfusion properties of tissue non-invasively without contrast. However, clinical applications are limited by unreliable parameter estimates, particularly for the perfusion fraction (f) and pseudodiffusion coefficient (D*). This study aims to develop a high-fidelity reconstruction for reliable estimation of IVIM parameters. The proposed method is versatile and amenable to various acquisition schemes and fitting methods.

METHODS

To address current challenges with IVIM, we adapted several advanced reconstruction techniques. We used a low-rank approximation of IVIM images and temporal subspace modeling to constrain the magnetization dynamics of the bi-exponential diffusion signal decay. In addition, motion-induced phase variations were corrected between diffusion directions and b-values, facilitating the use of high SNR real-valued diffusion data. The proposed method was evaluated in simulations and in vivo brain acquisitions in six healthy subjects and six individuals with a history of SARS-CoV-2 infection and compared with the conventionally reconstructed magnitude data. Following reconstruction, IVIM parameters were estimated voxel-wise.

RESULTS

Our proposed method reduced noise contamination in simulations, resulting in a 60%, 58.9%, and 83.9% reduction in the NRMSE for D, f, and D*, respectively, compared to the conventional reconstruction. In vivo, anisotropic properties of D, f, and D* were preserved with the proposed method, highlighting microvascular differences in gray matter between individuals with a history of COVID-19 and those without (p = 0.0210), which wasn't observed with the conventional reconstruction.

CONCLUSION

The proposed method yielded a more reliable estimation of IVIM parameters with less noise than the conventional reconstruction. Further, the proposed method preserved anisotropic properties of IVIM parameter estimates and demonstrated differences in microvascular perfusion in COVID-affected subjects, which weren't observed with conventional reconstruction methods.

摘要

目的

体素内不相干运动(IVIM)是一种定量磁共振成像(MRI)方法,用于无创地量化组织的灌注特性,而无需对比剂。然而,临床应用受到不可靠的参数估计的限制,特别是对于灌注分数(f)和假性扩散系数(D*)。本研究旨在开发一种高保真重建方法,以可靠地估计 IVIM 参数。该方法具有通用性,适用于各种采集方案和拟合方法。

方法

为了解决 IVIM 目前面临的挑战,我们采用了几种先进的重建技术。我们使用 IVIM 图像的低秩逼近和时间子空间建模来约束双指数扩散信号衰减的磁化动力学。此外,还校正了扩散方向和 b 值之间的运动引起的相位变化,从而可以使用高 SNR 实值扩散数据。该方法在模拟和六名健康受试者以及六名有 SARS-CoV-2 感染史的个体的体内脑采集进行了评估,并与常规重建的幅度数据进行了比较。重建后,以体素为单位估计 IVIM 参数。

结果

与常规重建相比,我们的方法减少了模拟中的噪声污染,使 D、f 和 D的 NRMSE 分别降低了 60%、58.9%和 83.9%。在体内,D、f 和 D的各向异性特性得以保留,与没有 COVID-19 病史的个体相比,有 COVID-19 病史的个体的灰质之间存在微血管差异(p = 0.0210),而常规重建则没有观察到这种差异。

结论

与常规重建相比,该方法能更可靠地估计 IVIM 参数,且噪声更少。此外,该方法保留了 IVIM 参数估计的各向异性特性,并显示了 COVID 影响个体的微血管灌注差异,而常规重建方法则没有观察到这些差异。

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