MICRO-CT-BASED PERFUSION IMAGING IN SMALL ANIMALS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.