MICRO-CT-BASED PERFUSION IMAGING IN SMALL ANIMALS

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 由于对高空间和时间分辨率的综合要求，体内血流灌注测量对现有的小动物成像技术提出了挑战，如磁共振显微镜(MRM)、microCT、microPET和microSPECT。在Micro-CT的这些灌注要求中都增加了辐射剂量的限制。在采样方面，我们通过使用双源/探测器Micro-CT系统解决了这些问题，以及在一系列不同旋转角度的多次连续注射L体积的对比度中可以重现相同时间密度曲线的新范例。高时间分辨率的要求和使用的造影剂的总量造成了欠采样，这使得重建过程变得复杂，因为它会导致明显的条纹伪影。我们展示了一种结合使用静态微CT集和动态但角度采样不足的采集的方法。我们提供了一种迭代重建方案，它使用采样良好的静态Micro-CT集作为先验。基于大多数医学图像是可压缩的这一事实，提出了一种稀疏性先验正则化加权L2范数优化算法来消除条纹伪影。本文采用先验全变差(TV)作为正则化方法，简化了正则化过程。

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.