Paper Info
Title
Tomographic image reconstruction using content-adaptive mesh modeling
Abstract
In this work 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.
Year
DOI
Venue
2001
10.1109/ICIP.2001.959139
ICIP
Keywords
Field
DocType
adaptive signal processing,cardiology,image motion analysis,image reconstruction,image representation,image sequences,medical image processing,single photon emission computed tomography,CAMM,computation time,content-adaptive mesh modeling,efficient mesh representation,efficient numerical reconstruction algorithms,image quality,image sequences,memory requirement reduction,motion-tracking,nodal values estimation,simulated gated cardiac-perfusion SPECT images,tomographic image reconstruction
Iterative reconstruction,Single-photon emission computed tomography,Computer vision,Tomographic image reconstruction,Content adaptive,Mesh model,Computer science,Image quality,Adaptive filter,Artificial intelligence,Computation
Conference
Volume
ISSN
Citations 
1
1522-4880
8
PageRank 
References 
Authors
0.74
0
3
Name
Order
Citations
PageRank
Jovan G. Brankov18212.09
Yongyi Yang21409140.74
Miles N. Wernick359561.13