Paper Info
Title
Coded Aperture Optimization in X-Ray Tomography via Sparse Principal Component Analysis
Abstract
Coded aperture X-ray computed tomography (CAXCT) systems reconstruct high quality images of the inner structure of an object from a few coded illumination measurements. Since the computed tomography (CT) system matrix is highly structured, random coded apertures lead to lower quality image reconstructions. In this paper, the noisy forward models of CAXCT in both Gaussian noise and Poisson noise are formulated and analyzed. In addition, a coded aperture optimization approach based on sparse principal component analysis is proposed to maximize the information sensed by a set of fan-beam projections. The complexity of the proposed optimization method is on the same order of magnitude as that of state-of-the-art methods but provide superior image quality. Computational experiments using simulated datasets and real datasets show gains up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 4.3 dB with SNR = 25 dB in the reconstruction image quality compared with that attained by random coded apertures.
Year
DOI
Venue
2020
10.1109/TCI.2019.2919228
IEEE Transactions on Computational Imaging
Keywords
Field
DocType
Apertures,X-ray imaging,Computed tomography,Image reconstruction,Detectors,Attenuation
Aperture,Computer vision,Coded aperture,Image quality,Tomography,Artificial intelligence,Order of magnitude,Shot noise,Gaussian noise,Mathematics,Principal component analysis
Journal
Volume
ISSN
Citations 
6
2573-0436
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Tianyi Mao100.34
Angela P. Cuadros212.05
Xu Ma302.70
Weiji He401.01
Qian Chen538785.48
Gonzalo R. Arce61061134.94