Brankov Jovan G, Yang Yongyi, Wernick Miles N
Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.
IEEE Trans Med Imaging. 2004 Feb;23(2):202-12. doi: 10.1109/TMI.2003.822822.
In this paper, we explore the use of a content-adaptive mesh model (CAMM) for tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first represented by a mesh model, an efficient image description based on nonuniform sampling. In the CAMM, image samples (represented as mesh nodes) are placed most densely in image regions having fine detail. Tomographic image reconstruction in the mesh domain is performed by maximum-likelihood (ML) or maximum a posteriori (MAP) estimation of the nodal values from the measured data. A CAMM greatly reduces the number of unknown parameters to be determined, leading to improved image quality and reduced computation time. We demonstrated the method in our experiments using simulated gated single photon emission computed tomography (SPECT) cardiac-perfusion images. A channelized Hotelling observer (CHO) was used to evaluate the detectability of perfusion defects in the reconstructed images, a task-based measure of image quality. A minimum description length (MDL) criterion was also used to evaluate the effect of the representation size. In our application, both MDL and CHO suggested that the optimal number of mesh nodes is roughly five to seven times smaller than the number of projection bins. When compared to several commonly used methods for image reconstruction, the proposed approach achieved the best performance, in terms of defect detection and computation time. The research described in this paper establishes a foundation for future development of a (four-dimensional) space-time reconstruction framework for image sequences in which a built-in deformable mesh model is used to track the image motion.
在本文中，我们探讨了使用内容自适应网格模型（CAMM）进行断层图像重建。在所提出的框架中，待重建的图像首先由网格模型表示，这是一种基于非均匀采样的高效图像描述。在CAMM中，图像样本（表示为网格节点）最密集地放置在具有精细细节的图像区域。在网格域中的断层图像重建通过对测量数据进行节点值的最大似然（ML）或最大后验（MAP）估计来执行。CAMM大大减少了待确定的未知参数数量，从而提高了图像质量并减少了计算时间。我们在实验中使用模拟门控单光子发射计算机断层扫描（SPECT）心脏灌注图像演示了该方法。使用通道化霍特林观察者（CHO）来评估重建图像中灌注缺损的可检测性，这是一种基于任务的图像质量度量。还使用最小描述长度（MDL）准则来评估表示大小的影响。在我们的应用中，MDL和CHO都表明，最佳网格节点数量大约比投影箱数量小五到七倍。与几种常用的图像重建方法相比，所提出的方法在缺陷检测和计算时间方面取得了最佳性能。本文所述的研究为未来开发用于图像序列的（四维）时空重建框架奠定了基础，其中使用内置的可变形网格模型来跟踪图像运动。
