Cone-Beam Tomography with Truncated Projections

描述（由申请人提供）： 该项目的长期目标是解决锥束层析成像中的截断问题，并实现和自由分发适用于最常见的锥束成像配置的图像重建软件。 具体目标是：1）设计、实现并公开提供用于锥形束计算机断层摄影（CBCT）几何结构的快速准确的图像重建代码，其中源和探测器旋转一次（或略多于一次），并且投影总是轴向截短（也可能被截断transaxially），2）设计，实施，并公开提供，用于CBCT几何形状的快速精确图像重建代码，其适合于具有在大约180度的角度范围上测量的投影的基于C形臂的CT，并且具有相关的截断图案，以及3）设计和实现简单实用的校准方法，从该校准方法中自动获得几何重建参数并将其传递给用于目标1和2的扫描器配置的重建算法。 该方法涉及设计算法，该算法不受通常伴随着截断投影数据的错误信息的传播的影响。六锥束配置将被考虑，并将通过组装基本的数学工具，已成功地解决某些特定的锥束截断问题，在过去的算法。将使用计算机模拟数据和台式和物理扫描仪的体模测量值对算法进行测试。将通过扩展现有的分析方法设计自动校准，并使用模拟和真实的数据对卡方方法进行测试。 该项目的健康效益涉及到锥束断层扫描从目前主要作为高对比度成像工具的状态过渡到快速、定量、体积成像模式，广泛应用于图像引导和诊断。

DESCRIPTION (provided by applicant): The long term objective of this project is to solve the truncation problem in cone-beam tomography, and to implement and freely distribute image reconstruction software suitable for the most common cone-beam imaging configurations. The specific aims are: 1) to devise, implement, and make publicly available, fast accurate image reconstruction code for cone-beam computed tomography (CBCT) geometries where the source and detector rotate once (or slightly more than once) about the patient, and the projections are always truncated axially (and may also be truncated transaxially), 2) to devise, implement, and make publicly available, fast accurate image reconstruction code for CBCT geometries tailored to C-arm based CT with projections measured over an angular range of about 180 degrees, and with relevant patterns of truncation, and 3) to design and implement simple practical calibration methods from which geometric reconstruction parameters are automatically obtained and passed to the reconstruction algorithms for the scanner configurations of aims 1 and 2. The methods involve devising algorithms that are impervious to the propagation of false information that is normally concomitant with truncated projection data. Six cone-beam configurations will be considered, and algorithms will be devised by assembling fundamental mathematical tools which have been successful in solving certain specific cone-beam truncation problems in the past. The algorithms will be tested with computer simulated data and phantom measurements from benchtop and physical scanners. Automated calibration will be devised by extending existing analytic approaches, and tested against chi-squared approaches using simulated and real data. The health benefits of this project relate to the transition of cone-beam tomography from its current status as primarily a high-contrast imaging tool to a fast, quantitative, volume imaging modality with widespread applications in image guidance and diagnosis.