Feasibility study of sparse-angular sampling and sinogram interpolation in material decomposition with a photon-counting detector

Dohyeon Kim; Byungdu Jo; Su-Jin Park; Hyemi Kim; Hee-Joung Kim

doi:10.1117/12.2216634

22 March 2016 Feasibility study of sparse-angular sampling and sinogram interpolation in material decomposition with a photon-counting detector

Dohyeon Kim, Byungdu Jo, Su-Jin Park, Hyemi Kim, Hee-Joung Kim

Author Affiliations +

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97835O (2016) https://doi.org/10.1117/12.2216634
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Spectral computed tomography (SCT) is a promising technique for obtaining enhanced image with contrast agent and distinguishing different materials. We focused on developing the analytic reconstruction algorithm in material decomposition technique with lower radiation exposure and shorter acquisition time. Sparse-angular sampling can reduce patient dose and scanning time for obtaining the reconstruction images. In this study, the sinogram interpolation method was used to improve the quality of material decomposed images in sparse angular sampling. A prototype of spectral CT system with 64 pixels CZT-based photon counting detector was used. The source-to-detector distance and the source-tocenter of rotation distance were 1200 and 1015 mm, respectively. The x-ray spectrum at 90 kVp with a tube current of 110 μA was used. Two energy bins (23-33 keV and 34-44 keV) were set to obtain the two images for decomposed iodine and calcification. We used PMMA phantom and its height and radius were 50 mm and 17.5 mm, respectively. The phantom contained 4 materials including iodine, gadolinium, calcification, and liquid state lipid. We evaluated the signal to noise ratio (SNR) of materials to examine the significance of sinogram interpolation method. The decomposed iodine and calcification images were obtained by projection based subtraction method using two energy bins with 36 projection data. The SNR in decomposed images were improved by using sinogram interpolation method. And these results indicated that the signal of decomposed material was increased and the noise of decomposed material was reduced. In conclusion, the sinogram interpolation method can be used in material decomposition method with sparse-angular sampling.

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Dohyeon Kim, Byungdu Jo, Su-Jin Park, Hyemi Kim, and Hee-Joung Kim "Feasibility study of sparse-angular sampling and sinogram interpolation in material decomposition with a photon-counting detector", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97835O (22 March 2016); https://doi.org/10.1117/12.2216634

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KEYWORDS

Signal to noise ratio

Reconstruction algorithms

Iodine

Image enhancement

Sensors

Photodetectors

Polymethylmethacrylate

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