MICRO-CT-BASED PERFUSION IMAGING IN SMALL ANIMALS

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。次级项目的主要支助 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 表示子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 由于对高空间和时间分辨率的组合要求，灌注的体内测量对现有的小动物成像技术（诸如磁共振显微镜（MRM）、microCT、microPET和microSPECT）提出了挑战。在Micro-CT灌注中，除了这些要求之外还添加了辐射剂量限制。在采样中，我们已经解决了这些问题，通过使用双源/检测器Micro-CT系统，和新的范例，相同的时间密度曲线可以再现在一系列不同的旋转角度的连续注射的体积的对比度的数量。 所使用的造影剂的高时间分辨率要求和总体积强加了使重建过程复杂化的欠采样，因为其导致显著的条纹伪影。我们展示了一种方法，使用组合静态微CT集与动态，但角度欠采样采集。我们提供了一个迭代重建的解决方案，使用良好采样的静态micro-CT集作为先验。针对医学图像的可压缩性，提出了一种稀疏先验正则化加权l2范数优化算法。在这项工作中实现了一个事先的全变差（TV）作为其简单的正则化。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. In vivo measurements of perfusion presents a challenge to existing small animal imaging techniques such as magnetic resonance microscopy (MRM), microCT, microPET, and microSPECT, due to combined requirements for high spatial and temporal resolution. In Micro-CT perfusion to these requirements are added the radiation dose restrictions. In sampling, we have addressed these issues by using a dual source/detector Micro-CT system, and the novel paradigm that the same time density curves can be reproduced in a number of consecutive injections of ¿L volumes of contrast at a series of different angles of rotation. The high temporal resolution requirements and total volume of contrast agent used imposes undersampling which complicates the reconstruction process, since it causes significant streaking artifacts. We demonstrate an approach using a combined static micro-CT set with dynamic but angularly undersampled acquisitions. We provide an iterative reconstruction solution that uses the well sampled static micro-CT set as a prior. A sparseness prior regularized weighted l2 norm optimization is proposed to mitigate streaking artifacts based on the fact that most medical images are compressible. A prior Total Variation (TV) is implemented in this work as the regularizer for its simplicity.