Prog Med Phys.  2016 Sep;27(3):105-110. 10.14316/pmp.2016.27.3.105.

Improvement of Analytic Reconstruction Algorithms Using a Sinogram Interpolation Method for Sparse-angular Sampling with a Photon-counting Detector

• Kim D
• Jo B
• Park SJ
• Kim H
• Kim HJ

• KMID: 2376539
• DOI: http://doi.org/10.14316/pmp.2016.27.3.105

Abstract

Sparse angular sampling has been studied recently owing to its potential to decrease the radiation exposure from computed tomography (CT). In this study, we investigated the analytic reconstruction algorithm in sparse angular sampling using the sinogram interpolation method for improving image quality and computation speed. A prototype of the spectral CT system, which has a 64-pixel Cadmium Zinc Telluride (CZT)-based photon-counting detector, was used. The source-to-detector distance and the source-to-center of rotation distance were 1,200 and 1,015 mm, respectively. Two energy bins (23~33 keV and 34~44 keV) were set to obtain two reconstruction images. We used a PMMA phantom with height and radius of 50.0 mm and 17.5 mm, respectively. The phantom contained iodine, gadolinium, calcification, and lipid. The Feld-kamp-Davis-Kress (FDK) with the sinogram interpolation method and Maximum Likelihood Expectation Maximization (MLEM) algorithm were used to reconstruct the images. We evaluated the signal-to-noise ratio (SNR) of the materials. The SNRs of iodine, calcification, and liquid lipid were increased by 167.03%, 157.93%, and 41.77%, respectively, with the 23~33 keV energy bin using the sinogram interpolation method. The SNRs of iodine, calcification, and liquid state lipid were also increased by 107.01%, 13.58%, and 27.39%, respectively, with the 34~44 keV energy bin using the sinogram interpolation method. Although the FDK algorithm with the sinogram interpolation did not produce better results than the MLEM algorithm, it did result in comparable image quality to that of the MLEM algorithm. We believe that the sinogram interpolation method can be applied in various reconstruction studies using the analytic reconstruction algorithm. Therefore, the sinogram interpolation method can improve the image quality in sparse-angular sampling and be applied to CT applications.

Keyword

Sinogram interpolation; Computed tomography (CT); Image reconstruction; Low-dose; Photon-counting detector

MeSH Terms

Cadmium
Gadolinium
Image Processing, Computer-Assisted
Iodine
Methods*
Polymethyl Methacrylate
Radiation Exposure
Radius
Signal-To-Noise Ratio
Zinc
Cadmium
Gadolinium
Iodine
Polymethyl Methacrylate
Zinc

Figure

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  Fig. 1 Flowchart of the proposed sinogram interpolation method.

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  Fig. 2 Spectral CT system with CZT-based photon-counting detector.

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  Fig. 3 Illustration of the PMMA phantom containing four materials.

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  Fig. 4 Restored PMMA phantom sinogram. (a) Without interpolation in 36 views, (b) With interpolation in 36 angular views.

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  Fig. 5 The reconstruction images of the PMMA phantom with the FDK and MLEM algorithms. (a∼c) Non-enhanced iodine image (23∼33 keV): (a) FDK without the sinogram interpolation method, (b) FDK with the sinogram interpolation method, (c) MLEM (10 iterations). (d∼e) Enhanced iodine image (34∼44 keV): (d) FDK without the sinogram interpolation method, (e) FDK with the sinogram interpolation method, (f) MLEM (10 iterations).

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  Fig. 6 The SNRs in the reconstruction images by FDK with the sinogram interpolation method, FDK without the sinogram interpolation method, and MLEM (10 iterations) (23∼33 keV).

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  Fig. 7 The SNRs in reconstruction images by FDK with the sinogram interpolation method, FDK without the sinogram interpolation method, and MLEM (10 iterations) (34∼44 keV).

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  Fig. 8 Time consumption for reconstructing images by FDK with the sinogram interpolation method, FDK without the sinogram interpolation method, and MLEM (10 iterations).

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