학술논문
Undersampled Dynamic X-Ray Tomography With Dimension Reduction Kalman Filter
Document Type
Periodical
Author
Source
IEEE Transactions on Computational Imaging IEEE Trans. Comput. Imaging Computational Imaging, IEEE Transactions on. 5(3):492-501 Sep, 2019
Subject
Language
ISSN
2573-0436
2333-9403
2334-0118
2333-9403
2334-0118
Abstract
In this paper, we propose a prior-based dimension reduction Kalman filter for undersampled dynamic X-ray tomography. With this method, the X-ray reconstructions are parameterized by a low-dimensional basis. Thus, the proposed method is computationally very light, and extremely robust as all the computations can be done explicitly. With real and simulated measurement data, we show that the method provides accurate reconstructions even with very limited number of angular directions.