Tomographic image reconstruction using content-adaptive mesh modeling
We propose the use of a content-adaptive mesh model (CAMM) for tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first modeled by an efficient mesh representation. The image is then obtained through estimation of the nodal values from the measured data. The use of a CAMM can greatly alleviate the ill-posed nature of the reconstruction problem, thereby leading to improved quality in the reconstructed images. In addition, it can lead to development of efficient numerical reconstruction algorithms. Finally, it can be useful for motion-tracking of image sequences. The proposed methods are tested using simulated gated cardiac-perfusion SPECT images. Our results indicate that, among the methods tested, the proposed approach achieves the best performance in terms of image quality and computation time, and can also reduce the memory requirement.