Improved Diffusion-Weighted Hyperpolarized 129Xe Lung MRI with Patch-Based Higher-Order, Singular Value Decomposition Denoising

IF 3.8 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology Pub Date : 2024-12-01 DOI:10.1016/j.acra.2024.06.029

Stephanie A. Soderlund , Abdullah S. Bdaiwi , Joseph W. Plummer , Jason C. Woods , Laura L. Walkup , Zackary I. Cleveland

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引用次数: 0

Abstract

Rationale and Objectives

Hyperpolarized xenon (¹²⁹Xe) MRI is a noninvasive method to assess pulmonary structure and function. To measure lung microstructure, diffusion-weighted imaging—commonly the apparent diffusion coefficient (ADC)—can be employed to map changes in alveolar-airspace size resulting from normal aging and pulmonary disease. However, low signal-to-noise ratio (SNR) decreases ADC measurement certainty, and biases ADC to spuriously low values. Further, these challenges are most severe in regions of the lung where alveolar simplification or emphysematous remodeling generate abnormally high ADCs. Here, we apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance image SNR, thereby reducing uncertainty and bias in diffusion measurements.

Materials and Methods

GLHOSVD denoising was employed in simulated images and gas phantoms with known diffusion coefficients to validate its effectiveness and optimize parameters for analysis of diffusion-weighted ¹²⁹Xe MRI. GLHOSVD was applied to data from 120 subjects (34 control, 39 cystic fibrosis (CF), 27 lymphangioleiomyomatosis (LAM), and 20 asthma). Image SNR, ADC, and distributed diffusivity coefficient (DDC) were compared before and after denoising using Wilcoxon signed-rank analysis for all images.

Results

Denoising significantly increased SNR in simulated, phantom, and in-vivo images, showing a greater than 2-fold increase (p < 0.001) across diffusion-weighted images. Although mean ADC and DDC remained unchanged (p > 0.05), ADC and DDC standard deviation decreased significantly in denoised images (p < 0.001).

Conclusion

When applied to diffusion-weighted ¹²⁹Xe images, GLHOSVD improved image quality and allowed airspace size to be quantified in high-diffusion regions of the lungs that were previously inaccessible to measurement due to prohibitively low SNR, thus providing insights into disease pathology.

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利用基于补丁的高阶奇异值分解去噪改进扩散加权超极化 129Xe 肺部 MRI。

原理和目的：超极化氙（129Xe）磁共振成像是一种评估肺部结构和功能的无创方法。为测量肺部微观结构，可采用弥散加权成像--通常是表观弥散系数（ADC）--来绘制正常衰老和肺部疾病导致的肺泡空隙大小变化图。然而，低信噪比（SNR）会降低 ADC 测量的确定性，并使 ADC 偏向虚假的低值。此外，这些挑战在肺泡简化或肺气肿重塑产生异常高 ADC 的肺部区域最为严重。在这里，我们应用全局局部高阶奇异值分解（GLHOSVD）去噪技术来提高图像信噪比，从而减少弥散测量的不确定性和偏差：在已知扩散系数的模拟图像和气体模型中采用 GLHOSVD 去噪，以验证其有效性并优化用于扩散加权 129Xe MRI 分析的参数。GLHOSVD 应用于 120 名受试者（34 名对照组、39 名囊性纤维化（CF）组、27 名淋巴管瘤（LAM）组和 20 名哮喘组）的数据。使用 Wilcoxon 符号秩分析比较了所有图像去噪前后的图像信噪比、ADC 和分布扩散系数（DDC）：在模拟、模型和活体图像中，去噪明显提高了信噪比，增幅超过 2 倍（P 0.05），去噪后图像的 ADC 和 DDC 标准偏差明显降低（P 结论：去噪后图像的信噪比和 ADC 标准偏差明显降低：当应用于扩散加权 129Xe 图像时，GLHOSVD 提高了图像质量，并允许对以前由于信噪比过低而无法测量的肺部高扩散区域的气腔大小进行量化，从而提供了对疾病病理的深入了解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.