Optimizing MDCT display for detection and diagnosis of pulmonary embolism

优化 MDCT 显示以检测和诊断肺栓塞

• 批准号: 7327800
• 负责人: WALTER F GOOD
• 金额: $ 37.13万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7327800
• 关键词: Address; Algorithms; Arteries; Blood Vessels; Clinical; Consultations; Data; Data Set; Depth Perception; Detection; Development; Diagnosis; Disabled Persons; Doctor of Philosophy; Embolism; Environment; Evaluation; Fatigue; Future; Glass; Goals; Gold; Image; Imagery; Lung; Measures; Methods; Modality; Numbers; Operative Surgical Procedures; Pattern; Performance; Peripheral; Procedures; Process; Property; Psychophysiology; Pulmonary Embolism; Pulmonary vessels; Radiology Specialty; Range; Rate; Reader; Reading; Relative (related person); Reporting; Research Personnel; Resolution; Slice; Speed; Standards of Weights and Measures; Structure; Study Section; Surface; System; Technology; Thick; Time; To specify; Trees; Voting; Work; X-Ray Computed Tomography; desire; detector; gaze; improved; lung volume; programs; radiologist; stereoscopic

DESCRIPTION (provided by applicant): Multi-detector CT (MDCT) is rapidly becoming the primary imaging modality for detection and diagnosis of pulmonary embolism (PE). Its main limitations are its inability to reliably depict sub-segmental arteries and the large volume of image data that must be reviewed for each study. This application proposes to implement a stereographic display of appropriately segmented arterial trees, which can be manipulated in real time, with the intent of improving both the accuracy and efficiency of these studies. Most previous methods for segmenting pulmonary vessels are less than optimal in that, while they generally combine the eigenvectors of the Hessian matrix to derive a local scalar measure of cylindricity, in the process they lose information about the directions of the local curvatures. By exploiting all information in structure tensor fields derived from 3D datasets, and in particular by employing tensor voting methods to identify voxels comprising surfaces of vessels and bifurcations, we expect to significantly improve the segmentation process and the depiction of small arteries. A flexible mechanism will be developed for displaying various stereographic views of the segmented data in real time, at the option of the radiologist. Rendering methods, tailored specifically to viewing PE, will be developed. Multiple raycasting algorithms will be incorporated into the system because certain methods are better for detection while others are better for assessing a feature once it has been detected. Images comprised of local parameters used in segmenting vascular trees, or parameters that characterize statistical properties of vessel distributions, will be displayable at the readers' discretion. A retrospective LROC study (8 readers, 4 display modes, 100 cases) will be performed to evaluate the newly developed methods. This study will address performance and efficiency as well as certain psychophysical issues such as subjective acceptance of the display, speed of operation, pattern of gaze in 3D versus 2D, and the relative propensity of the various display modes to induce fatigue. To compensate for an imperfect gold standard for case verification, mixture distribution analysis will also be applied and compared to LROC results. This study should identify a set of images containing PEs that can be readily seen on stereo displays, but cannot be detected as easily on traditional displays, or vice versa - which should help clarify benefits of stereo for radiographic applications and provide useful information for making future refinements to the display.

描述(申请人提供)：多层螺旋CT(MDCT)正迅速成为检测和诊断肺栓塞(PE)的主要成像方式。它的主要局限性是不能可靠地描绘亚节段动脉，以及每项研究必须审查的大量图像数据。该应用程序建议实现适当分割的动脉树的立体显示，其可以实时操作，目的是提高这些研究的准确性和效率。大多数以前的肺血管分割方法都不是最优的，因为它们通常结合Hessian矩阵的特征向量来得到局部标量的圆柱度度量，在这个过程中它们丢失了关于局部曲率方向的信息。通过利用来自3D数据集的结构张量场中的所有信息，特别是通过使用张量投票方法来识别包含血管表面和分支的体素，我们期望显著地改进分割过程和对小动脉的描绘。将开发一种灵活的机制，用于根据放射科医生的选择，实时显示分割数据的各种立体图。将开发专门为观看PE量身定做的渲染方法。多种光线投射算法将被整合到系统中，因为某些方法更适合于检测，而另一些方法则更适合于在检测到特征后对其进行评估。由用于分割血管树的局部参数或表征血管分布的统计特性的参数组成的图像将可由读者自行决定显示。将进行一项回顾性的LROC研究(8个阅读器，4种显示模式，100例)，以评估新开发的方法。这项研究将解决性能和效率以及某些心理物理问题，如对显示器的主观接受、操作速度、3D和2D模式下的凝视模式，以及各种显示模式引发疲劳的相对倾向。为了弥补病例验证的黄金标准不完善，还将应用混合分布分析并将其与LROC结果进行比较。这项研究应该确定一组包含PE的图像，这些图像可以很容易地在立体显示器上看到，但在传统显示器上不易检测到，反之亦然--这应该有助于阐明立体图像对射线照相应用的好处，并为未来对显示器进行改进提供有用的信息。