Image-Based Quantification and Analysis of Longitudinal Lung Nodule Deformations

描述（由申请人提供）：由于图像采集和患者定位的可变性、呼吸和心脏运动引起的变形以及比较未对齐图像的放射学临床标准，随时间推移对病理进行基于图像的评价变得复杂。本申请提出了一种用于纵向图像分析的计算方法，其通过补偿非线性背景运动来捕获、图示和量化随时间的病理相关变化。虽然该方法是通用的，我们将使用最具挑战性的，但常规进行的纵向成像研究作为一个驱动的临床问题，即肺癌评估使用定期CT成像评价患者特异性治疗反应和重新分期。特别是，我们专注于亚实性结节，也被称为磨玻璃样阴影（GSPs），必须仔细监测恶性肿瘤的迹象，但由于整体肺部运动对其外观的重大影响，很难在扫描之间进行比较。我们建议继续发展和新的应用我们的“几何变形”变形图像配准方法。与以前的方法不同，几何变形处理生长或收缩的病理，其形态还受到非刚性全局背景变形的影响。这通过同时估计全局背景和病变变形来实现。这种配准方法的临床应用是：1）通过显示在消除非刚性背景运动之后强调病变变化的对准扫描，简化数据解释，以减少工作量并增加人工放射科医师检查中的吞吐量，同时增加纵向测量的准确性; 2）实现病变对治疗的响应的逐体素度量，其包括估计病变的变形幅度、内部组成变化和随时间的浸润性生长模式。几何变形最初是为神经成像中的变化检测而开发的，第一个目标是通过定制用于神经成像的方法来继续证明其广泛的适用性和实用性。 连续胸部CT登记。其次，假设使用物理启发的弹性来对肺组织的患者特异性的不均匀弹性进行建模将使得能够更准确地恢复联合估计的病变变形。几何变形将被扩展到集成局部自适应弹性正则化和测试我们的假设，提高目标配准精度和提高精度的恢复病变变形。最后，将在回顾性临床评价中证明配准软件的临床实用性。这将通过将从两种几何变形方法导出的基于图像的纵向测量与从替代图像配准策略导出的纵向测量进行比较，并将提取的逐体素病变变形和生长特征与良性病变与癌性病变的患者诊断相关联来完成。

DESCRIPTION (provided by applicant): Image-based evaluation of pathologies over time is complicated by variability in image acquisition and patient positioning, deformations due to respiratory and cardiac motion, and the clinical standard in radiology of comparing unaligned images. This application proposes a computational method for longitudinal image analysis that captures, illustrates and quantifies pathology-related changes over time, by compensating for non-linear background motion. Although the method is general, we will use one of the most challenging yet routinely conducted longitudinal imaging studies as a driving clinical problem, namely lung cancer assessment using periodic CT imaging for evaluating patient-specific response to therapy and for restaging. In particular, we focus on subsolid nodules, also known as ground-glass opacities (GGOs), which must be carefully monitored for signs of malignancy but which are difficult to compare across scans because of the significant impact that global lung motion has on their appearance. We propose continued development and novel application of our "geometric metamorphosis" deformable image registration method. Unlike previous approaches, geometric metamorphosis handles growing or contracting pathologies whose morphology is additionally impacted upon by non-rigid global background deformations. This is achieved by simultaneously estimating both the global background and lesion deformations. Clinical applications of such a registration method are to 1) ease data interpretation for reduced workload and increased throughput in manual radiologist review while simultaneously increasing the accuracy of longitudinal measurements, by displaying aligned scans that emphasize lesion change after non-rigid background motion has been eliminated; 2) enable voxel- wise metrics of lesion response to treatment, which includes estimating a lesion's deformation magnitude, internal composition changes and infiltrative growth pattern over time. Geometric metamorphosis was originally developed for change detection in neuroimaging, and the first goal is to continue to demonstrate its wide applicability and utility by customizing the method for serial chest CT registration. Second, it is hypothesized that using physically-inspired heuristics to model the patient-specific, inhomogeneous elasticity of lung tissue will enable more accurate recovery of the jointly estimated lesion deformations. Geometric metamorphosis will be extended to integrate locally adaptive elastic regularization and test our hypotheses of improved target registration accuracy and improved accuracy of the recovered lesion deformations. Finally, the clinical utility of the registration software will be demonstrated within a retrospectve clinical evaluation. This will be done by comparing the image-based longitudinal measurements derived from both geometric metamorphosis methods with those from alternative image registration strategies, and correlating extracted voxel-wise lesion deformations and growth characteristics with patient diagnoses of benign versus cancerous lesions.