Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom.

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Medical Imaging Pub Date : 2024-03-01 Epub Date: 2024-03-08 DOI:10.1117/1.JMI.11.2.024002

Chengyue Wu, David A Hormuth, Ty Easley, Federico Pineda, Gregory S Karczmar, Thomas E Yankeelov

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Abstract

Purpose: Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Approach: A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to $300 μ m / 60 s$ ) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to $300 μ m / 1$ to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity.

Results: The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization.

Conclusion: An in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.

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通过动态数字模型系统评估基于核磁共振成像的肿瘤相关血管形态和血液动力学特征。

目的：定量成像生物标记物的验证是一项具有挑战性的任务，因为很难测量目标生物过程的基本真实情况。本文建立了一个基于数字模型的框架，以动态对比增强磁共振成像（DCE-MRI）为基础，系统地验证肿瘤相关血管形态和血流动力学的定量特征：方法：采用数字模型提供一个地面实况血管系统，在该系统内模拟 45 个合成肿瘤。对高空间分辨率 DCE-MRI 数据（空间/时间分辨率 = 30 至 300 μm/60 s）进行形态学分析，以确定肿瘤相关血管（TAV）动脉输入定位的准确性。然后结合高空间分辨率和高时间分辨率（空间/时间分辨率=60至300微米/1至10秒）DCE-MRI数据进行血液动力学分析，确定估算肿瘤相关血压、血管抽取率、间质压力和间质流速的准确性：观察到的采集设置效果表明，在优化 DCE-MRI 方案进行形态学分析时，提高空间分辨率是有帮助的，但并非必要，因为即使使用最低的调查空间分辨率，也能高精度地恢复 TAV 的位置和动脉输入。在优化用于血液动力学分析的 DCE-MRI 方案时，提高用于血管分割的图像的空间分辨率至关重要，而用于动力学参数拟合的图像的空间和时间分辨率也需要同时优化：结论：我们建立了一个硅验证框架，以系统地量化图像采集设置对准确估计肿瘤相关特征能力的影响。

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

Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.10

自引率

4.20%

发文量

期刊介绍： JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.