Development of spectral X-ray computer tomography (CT) equipped with photon counting detector has been recently attracting great research interest. This work aims to improve the quality of spectral X-ray CT image. Maximum a posteriori (MAP) expectation-maximization (EM) algorithm is applied for reconstructing image-based weighting spectral X-ray CT images. A spectral X-ray CT system based on the cadmium zinc telluride photon counting detector and a fat cylinder phantom were simulated. Comparing with the commonly used filtered back projection (FBP) method, the proposed method reduced noise in the final weighting images at 2, 4, 6 and 9 energy bins up to 85.2%, 87.5%, 86.7% and 85%, respectively. CNR improvement ranged from 6.53 to 7.77. Compared with the prior image constrained compressed sensing (PICCS) method, the proposed method could reduce noise in the final weighting images by 36.5%, 44.6%, 27.3% and 18% at 2, 4, 6 and 9 energy bins, respectively, and improve the contrast-to-noise ratio (CNR) by 1.17 to 1.81. The simulation study also showed that comparing with the FBP and PICCS algorithms, image-based weighting imaging using MAP-EM statistical algorithm yielded significant improvement of the CNR and reduced the noise of the final weighting image.
SossinA., RebuffelV., TabaryJ., LétangJ.M., FreudN., VergerL., A novel scatter separation method for multi-energy x-ray imaging, Physics in Medicine & Biology61(12) (2016), 4711–4728.
2.
LeeS.W., ChoiY.N., ChoH.M., LeeY.J., RyuH.J., KimH.J., A Monte Carlo simulation study of the effect of energy windows in computed tomography images based on an energy-resolved photon counting detector, Physics in
Medicine & Biology57(15) (2012), 4931–4949.
3.
SchmidtT.G., CT energy weighting in the presence of scatter and limited energy resolution, Medical Physics37(3) (2010), 1056–1067.
4.
SchmidtT.G., Optimal “image-based” weighting for energy-resolved CT, Medical Physics36(7) (2009), 3018–3027.
5.
BerglundJ., JohanssonH., MaackH.I., FredenbergE., Energy weighting improves the image quality of spectral mammograms: Implementation on a photon-counting mammography system,, SPIE Medical Imaging9033 (2014), 90331E1–7.
6.
LeeS.W., ChoiY.N., LeeY.J., KimH.J., The effect of energy weighting on x-ray imaging based on photon counting detector: A Monte Carlo simulation, Proceeding of SPIE-The International Society for Optical Engineering (2012), 8313193.
7.
RupcichF., GilatschmidtT., Experimental study of optimal energy weighting in energy-resolved CT using a CZT detector,, Medical Imaging 2013: Physics of Medical Imaging 86681X8668 (2013), 86681X1–8.
8.
LeeY., LeeS., KimH.J., Comparison of spectral CT imaging methods based a photon-counting detector: Experimental study, Nuclear Inst & Methods in Physics Research A815 (2016), 68–74.
9.
RenL., ZhengB., LiuH., Tutorial on X-ray photon counting detector characterization, Journal of X-ray Science and Technology26(1) (2018), 1–28.
10.
ZieglerA., KöhlerT., ProksaR., Noise and resolution in images reconstructed with FBP and OSC algorithms for CT, Medical Physics34(2) (2007), 585–598.
11.
YuZ., LengS., LiZ., McColloughC.H., Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography, Physics in Medicine & Biology61(18) (2016), 6707–6732.
12.
SukovicP., ClinthorneN.H., Penalized weighted least-squares image reconstruction for dual energy X-ray transmission tomography, Medical Imaging IEEE Transactions on19(11) (2000), 1075–1081.
Jianhua1M., HuaZ., YangG., HuangJ., ZhengrongL., QianjingF., WufanC., Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior, Physics in Medicine & Biology57(22) (2012), 7519–7542.
15.
HelinT., BurgerM., Maximum a posteriori probability estimates in infinite-dimensional Bayesian inverse problems, Inverse Problems31(8) (2015), 1–23.
16.
LazaroD., BuvatI., LoudosG., StrulD., SantinG., GiokarisN., DonnarieixD., MaigneL., SpanoudakiV., StyliarisS., StaelensS., BretonV., Validation of the GATE Monte Carlo simulation platform for modelling a CsI(Tl) scintillation camera dedicated to small-animal imaging, Physics in Medicine & Biology49(2) (2004), 271–285.
17.
PunnooseJ., XuJ., SisniegaA., ZbijewskiW., SiewedsenJ.H., Technical Note:spektr3.0—A computational tool for x-ray spectrum modeling and analysis, , Medical Physcis43(8)(2016), 4711–4717.
18.
CaldeiraL., ScheinsJ.J., AlmeidaP., SeabraJ., HerzogH., Maximum a Posteriori Reconstruction using PRESTO and PET/MR data acquired Simultaneously with the 3TMR-BrainPET, Nuclear Science Symposium Conference Record612(3) (2010), 2879–2884.
19.
ChenY., Ma JJ., FengQ., LuoL. and ShiP., Nonlocal Prior Bayesian Tomographic Reconstruction, Journal of Mathematical Imaging & Vision30(2) (2008), 133–146.
20.
GreenP.J., Bayesian reconstructions from emission tomography data using a modified EM algorithm, IEEE Transactions Medical Imaging9(1) (1990), 84–93.
21.
LalushD.S., TsuiB.W., Simulation evaluation of Gibbs prior distributions for use in maximum a posteriori SPECT reconstructions, IEEE Transactions Medical Imaging11(2) (1992), 267–275.
22.
LauzierP.T., ChenG.H., Characterization of statistical prior image constrained compressed sensing. I. Applications to time-resolved contrast-enhanced CT, Medical Physics39(10) (2012), 5930–5948.
23.
LauzierP.T., ChenG.H., Characterization of statistical prior image constrained compressed sensing (PICCS): II. Application to dose reduction, Medical Physics39(1) (2012), , 66–80.
24.
LauzierP.T., TangJ., ChenG.H., Prior image constrained compressed sensing: Implementation and performance evaluation, Medical Physics39(1) (2012), 66–80.
25.
WangX., MeierD., TaguchiK., WagenaarD.J., PattB.E., FreyE.C., Material separation in x-ray CT with energy resolved photon-counting detectors, Medical Physics38(3) (2011), 1534–1546.
26.
ShuaiL., YuL., JiaW., FletcherJ.G., MistrettaC.A., McColloughC.H., Noise reduction in spectral CT: Reducing dose and breaking the trade-off between image noise and energy bin selection, Medical Physics38(9) (2011), 4946–4957.
