pmp Progress in Medical Physics Progress in Medical Physics 2508-4445 2508-4453 Korean Society of Medical Physics 10.14316/pmp.2016.27.2.105 pmp-27-105 Original Article Improvement of Analytic Reconstruction Algorithms Using a Sinogram Interpolation Method for Sparse-angular Sampling with a Photon-counting Detector KimDohyeon ∗ JoByungdu† ParkSu-Jin† KimHyemi† KimHee-Joung† Departments of Radiation Convergence Engineering, Yonsei University, Wonju, Korea Departments of Radiological Science, College of Health Science, Yonsei University, Wonju, Korea Correspondence: Hee-Joung Kim (hjk1@yonsei.ac.kr) Tel: 82-33-760-2983, Fax: 82-33-760-2562 92016 1992016 273105 110 07072016 21092016 22092016 Copyright © 2016 Korean Society of Medical Physics 2016 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

 Sinogram interpolation Computed tomography (CT) Image reconstruction Low-dose Photon-counting detector REFERENCESKalke M Siltanen S Sinogram interpolation method for sparse-angle tomography Appl Math 53 423441 2014 Brooks RA Weiss GH Talbert AJ A new approach to interpolation in computed tomography J Comput Assisted Tomogr 25 577585 1978 Kim D Park S-J Jo B Kim H Kim H-J Investigation of sparse-angle view in cone beam computed tomography (CBCT) reconstruction algorithm using a sinogram interpolation method IFMBE Proceedings 2015, Canada, pp. 74 Siltanen S Kolehmainen V Järvenpää S Statistical inversion for medical x-ray tomography with few radiographs: I. General theory Phys Med Biol 4810 14651490 2003 Lahart MJ Estimation of reconstructions in computed tomography J Opt Soc Am 7110 11551161 1981 Shikhaliev PM Xu T Molloi S Photon counting CT: Concept and initial results Med Phys 322 427436 2005 Lundqvist M Cederstrom B Chmill V Danielsson M Hasegawa B Evaluation of a photon counting X-ray imaging system IEEE Trans Nucl Sci 484 15301536 2001 Sarkar V Shi C Rassiah-Szegedi P The effect of a limited number of projections and reconstruction algorithms on the image quality of megavoltage digital tomosynthesis J Appl Clin Med Phys 103 155172 2009 Feldkamp LA Davis LC Kress JW Practical cone-beam algorithm J Opt Soc Am A 16 612619 1984 Shepp LA Vardi Y Maximum likelihood reconstruction for emission tomography IEEE Trans Med Imag 12 113122 1982 Dempster AP Laird NM Rubin DB Maximum likelihood from incomplete data via the EM algorithm J R Statist Soc B 391 138 1977 https://en.wikipedia.org/wiki/Signal-to-noise_ratio_(imaging) Bertram M Wiegert J Schäfer D Aach T and Rose G Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT IEEE Trans Med Imag 287 10111022 2009 Bertram M Rose G Schäfer D Wiegert J and Aach T Directional interpolation of sparsely sampled cone-beam CT sinogram data IEEE Int Symp Biomed Imag 1 928931 2004 Figures and Tables

Flowchart of the proposed sinogram interpolation method.

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

Illustration of the PMMA phantom containing four materials.

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

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).

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).

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).

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

Detailed acquisition parameters of the spectral CT system.

Source to detector distance (SID) 1,200 mm
Source to center of rotation distance 1,015 mm
Detector information  
Pixel pitch 0.8 mm
Detector length 51.2 mm
Number of pixels 64×1
Source information bin 1: 23∼33 keV
  bin 2: 34∼44 keV
Number of projections (angle interval) 36 projections (10o)
Reconstruction method 1. Reconstruction method –‘FBP’
  2. Filter –‘Ram-lak’