Robbins Constance M, Qian Kuanren, Zhang Yongjie Jessica, Kainerstorfer Jana M
Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States.
University of Pittsburgh, Department of Radiology, Pittsburgh, Pennsylvania, United States.
J Biomed Opt. 2024 Sep;29(9):096001. doi: 10.1117/1.JBO.29.9.096001. Epub 2024 Sep 14.
Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth.
To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression.
Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast.
When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths.
This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression.
近红外光学成像方法已显示出有望用于监测乳腺癌新辅助化疗（NAC）的反应，其内源对比度来自氧合血红蛋白和脱氧血红蛋白。空间频域成像（SFDI）可用于以低成本和便携式形式检测这种对比度，但其成像深度有限。局部组织压缩可能用于减小有效肿瘤深度。
为了评估SFDI在预测治疗反应方面的潜力，我们旨在预测在组织压缩下，肿瘤大小、硬度和血红蛋白浓度的变化将如何在SFDI测量的对比度中得到反映。
对含内含物的软材料进行压缩的有限元分析与蒙特卡罗模拟相结合，以预测测量的光学对比度。
当不考虑压缩对血容量的影响时，压缩产生的对比度增益随内含物的大小和硬度增加而增加，随内含物深度增加而减小。采用压缩导致血容量减少的模型时，压缩会降低成像对比度，对于较浅深度处较大和较硬的内含物，这种影响更大。
这项计算建模研究是朝着使用SFDI和局部压缩跟踪NAC引起的肿瘤变化迈出的第一步。
