Neelakantan Sunder, Ismail Mostafa K, Mukherjee Tanmay, Smith Bradford J, Rizi Rahim, Avazmohammadi Reza
Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Proc SPIE Int Soc Opt Eng. 2023 Feb;12466. doi: 10.1117/12.2653648. Epub 2023 Apr 3.
There are several lung diseases that lead to alterations in regional lung mechanics, including acute respiratory distress syndrome. Such alterations can lead to localized underventilation of the affected areas resulting in the overdistension of the surrounding healthy regions. They can also lead to the surrounding alveoli expanding unevenly or distorting. Therefore, the quantification of the regional deformation in the lungs offers insights into identifying the regions at risk of lung injury. Although few recent studies have developed image processing techniques to quantify the regional deformation in the lung from dynamic imaging, the presence and extent of in the lung, and its correlation with volumetric deformation, remain poorly understood. In this study, we present a method that uses the four-dimensional displacement field obtained from image registration to quantify both regional volumetric and distortional deformation in the lung. We used dynamic computed tomography scans in a healthy rat over the course of one respiratory cycle in free breathing. Non-rigid image registration was performed to quantify voxel displacement during respiration. The deformation gradient was calculated using the displacement field and its determinant was used to quantify regional volumetric deformation. Regional distortion was calculated as the ratio of maximum to minimum principal stretches using the isochoric part of the Cauchy green tensor. We found an inverse correlation between volumetric strains and distortion indicating that poorly expanding alveoli tend to experience larger distortion. The combination of regional volumetric strains and distortion may serve as high-fidelity biomarkers to identify the regions at risk of most adverse lung injuries.
有几种肺部疾病会导致局部肺力学改变,包括急性呼吸窘迫综合征。这种改变会导致受影响区域局部通气不足,进而导致周围健康区域过度扩张。它们还会导致周围肺泡不均匀扩张或变形。因此,对肺部区域变形进行量化有助于识别有肺损伤风险的区域。尽管最近很少有研究开发图像处理技术来从动态成像中量化肺部的区域变形,但肺部变形的存在、程度及其与容积变形的相关性仍知之甚少。在本研究中,我们提出了一种方法,该方法使用从图像配准获得的四维位移场来量化肺部的区域容积变形和扭曲变形。我们在自由呼吸的一个呼吸周期内,对一只健康大鼠进行了动态计算机断层扫描。进行非刚性图像配准以量化呼吸过程中的体素位移。使用位移场计算变形梯度,其行列式用于量化区域容积变形。使用柯西-格林张量的等容部分,将区域扭曲计算为最大主应变与最小主应变之比。我们发现容积应变与扭曲之间呈负相关,这表明扩张不良的肺泡往往会经历更大的扭曲。区域容积应变和扭曲的组合可能作为高保真生物标志物来识别最易发生严重肺损伤的区域。