High-resolution coded-aperture design for compressive X-ray tomography using low resolution detectors
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High-resolution coded-aperture design for compressive X-ray tomography using low resolution detectors. / Mojica, Edson; Pertuz, Said; Arguello, Henry.
In: Optics Communications, Vol. 404, 2017, p. 103-109.Research output: Contribution to journal › Article › Scientific › peer-review
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TY - JOUR
T1 - High-resolution coded-aperture design for compressive X-ray tomography using low resolution detectors
AU - Mojica, Edson
AU - Pertuz, Said
AU - Arguello, Henry
PY - 2017
Y1 - 2017
N2 - One of the main challenges in Computed Tomography (CT) is obtaining accurate reconstructions of the imaged object while keeping a low radiation dose in the acquisition process. In order to solve this problem, several researchers have proposed the use of compressed sensing for reducing the amount of measurements required to perform CT. This paper tackles the problem of designing high-resolution coded apertures for compressed sensing computed tomography. In contrast to previous approaches, we aim at designing apertures to be used with low-resolution detectors in order to achieve super-resolution. The proposed method iteratively improves random coded apertures using a gradient descent algorithm subject to constraints in the coherence and homogeneity of the compressive sensing matrix induced by the coded aperture. Experiments with different test sets show consistent results for different transmittances, number of shots and super-resolution factors.
AB - One of the main challenges in Computed Tomography (CT) is obtaining accurate reconstructions of the imaged object while keeping a low radiation dose in the acquisition process. In order to solve this problem, several researchers have proposed the use of compressed sensing for reducing the amount of measurements required to perform CT. This paper tackles the problem of designing high-resolution coded apertures for compressed sensing computed tomography. In contrast to previous approaches, we aim at designing apertures to be used with low-resolution detectors in order to achieve super-resolution. The proposed method iteratively improves random coded apertures using a gradient descent algorithm subject to constraints in the coherence and homogeneity of the compressive sensing matrix induced by the coded aperture. Experiments with different test sets show consistent results for different transmittances, number of shots and super-resolution factors.
KW - Coded apertures
KW - Compressive sensing
KW - Computed tomography
KW - Super-resolution
U2 - 10.1016/j.optcom.2017.06.053
DO - 10.1016/j.optcom.2017.06.053
M3 - Article
VL - 404
SP - 103
EP - 109
JO - Optics Communications
JF - Optics Communications
SN - 0030-4018
ER -