Strain measurement during tissue deformation is crucial to elucidate relationships between mechanical loading and functional changes in biological tissues. When combined with specified loading conditions, assessment of strain fields can be used to craft models that accurately represent the mechanical behavior of soft tissue. Inhomogeneities in strain fields may be indicative of normal or pathological inhomogeneities in mechanical properties. In this study, we present the validation of a modified Demons registration algorithm for non-contact, marker-less strain measurement of tissue undergoing uniaxial loading. We validate the algorithm on a synthetic dataset composed of artificial deformation fields applied to a speckle image, as well as images of aortic sections of varying perceptual quality. Initial results indicate that Demons outperforms recent Optical Flow and Digital Image Correlation methods in terms of accuracy and robustness to low image quality, with similar runtimes. Demons achieves at least 8% lower maximal deviation from ground truth on 50% biaxial and shear strain applied to aortic images. To illustrate utility, we quantified strain fields of multiple human aortic specimens undergoing uniaxial tensile testing, noting the formation of strain concentrations in areas of rupture. The modified Demons algorithm captured a large range of strains (up to 50%) and provided spatially resolved strain fields that could be useful in the assessment of soft tissue pathologies.

组织变形过程中的应变测量对于阐明机械负荷与生物组织功能变化之间的关系至关重要。当与指定的加载条件结合使用时，应变场的评估可用于制作可精确表示软组织机械行为的模型。应变场中的不均匀性可以指示机械性质的正常或病理性不均匀性。在这项研究中，我们提出了一种经过修改的恶魔配准算法，用于非接触，无标记的单轴加载组织应变测量的验证。我们在合成数据集上验证该算法，该合成数据集包含应用于散斑图像的人工变形场以及不同感知质量的主动脉截面图像。初步结果表明，在运行时间相似的情况下，恶魔在低图像质量的准确性和鲁棒性方面优于最近的光流和数字图像相关方法。在应用于主动脉图像的50％双轴和剪切应变上，恶魔与地面真相的最大偏差至少降低了8％。为了说明实用性，我们量化了经受单轴拉伸测试的多个人类主动脉标本的应变场，并指出了破裂区域中应变浓度的形成。修改后的恶魔算法捕获了大范围的应变（高达50％），并提供了空间分辨的应变场，可用于评估软组织病理学。在应用于主动脉图像的50％双轴和剪切应变上，恶魔与地面真相的最大偏差至少降低了8％。为了说明其实用性，我们对经历单轴拉伸测试的多个人类主动脉样本的应变场进行了量化，并指出了破裂区域中应变集中的形成。修改后的恶魔算法捕获了大范围的应变（高达50％），并提供了空间分辨的应变场，可用于评估软组织病理学。在应用于主动脉图像的50％双轴和剪切应变上，恶魔与地面真相的最大偏差至少降低了8％。为了说明实用性，我们量化了经受单轴拉伸测试的多个人类主动脉标本的应变场，并指出了破裂区域中应变浓度的形成。修改后的恶魔算法捕获了大范围的应变（高达50％），并提供了空间分辨的应变场，可用于评估软组织病理学。