Image-Based Quantification and Analysis of Longitudinal Lung Nodule Deformations

基于图像的纵向肺结节变形的量化和分析

• 批准号: 8521659
• 负责人: Nathan D. Cahill
• 金额: $ 20.28万
• 依托单位: KITWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8521659
• 关键词: Accounting; Air; Algorithms; Appearance; Automobile Driving; Benign; Biological Metamorphosis; Brain Neoplasms; Cancerous; Characteristics; Chest; Clinical; Computational algorithm; Computer software; Computing Methodologies; Contracts; Data; Data Set; Detection; Development; Diagnosis; Disease; Elasticity; Evaluation; Glass; Goals; Growth; Image; Image Analysis; Infiltrative Growth; Intraobserver Variability; Knowledge; Lesion; Longitudinal Studies; Lung; Lung nodule; Malignant Neoplasms; Malignant neoplasm of lung; Manuals; Measurement; Measures; Medical Imaging; Methods; Metric; Modeling; Monitor; Morphology; Motion; Nature; Nodule; Pathology; Patients; Pattern; Phase; Positioning Attribute; Radiology Specialty; Recovery; Risk; Scanning; Simulate; Software Validation; Solid; Structure of parenchyma of lung; Testing; Time; Traumatic Brain Injury; Treatment Protocols; Variant; Workload; X-Ray Computed Tomography; base; clinical application; clinically significant; design; follow-up; heart motion; heuristics; image registration; imaging Segmentation; improved; longitudinal analysis; lung imaging; lymph nodes; neuroimaging; novel; open source; outcome forecast; public health relevance; radiologist; research clinical testing; respiratory; response; tool; treatment response; tumor; validation studies

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.

描述（由申请人提供）：随着时间的推移，基于图像的病理评估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而变得复杂。本应用程序提出了一种纵向图像分析的计算方法，通过补偿非线性背景运动，捕获、说明和量化病理相关的随时间变化。虽然该方法是通用的，但我们将使用最具挑战性但常规进行的纵向成像研究之一作为驱动临床问题，即肺癌评估使用定期CT成像来评估患者对治疗的特异性反应和重新分期。我们特别关注亚实性结节，也称为磨玻璃混浊（GGOs），必须仔细监测其恶性体征，但由于肺部整体运动对其外观有重大影响，因此难以跨扫描进行比较。我们建议继续发展和应用我们的“几何变形”形变图像配准方法。与以前的方法不同，几何变态处理生长或收缩病理，其形态受到非刚性全局背景变形的额外影响。这是通过同时估计全局背景和病变变形来实现的。这种登记方法的临床应用是：1)简化数据解释，减少工作量，增加人工放射科医生审查的吞吐量，同时提高纵向测量的准确性，通过显示对齐扫描，强调非刚性背景运动消除后病变的变化；2)实现病灶对治疗反应的体素度量，包括估计病灶的变形幅度、内部成分变化和浸润性生长模式随时间的变化。几何变态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方法来继续证明其广泛的适用性和实用性