Ultralow dose CT image reconstruction with pre-log shifted-Poisson model and texture-based MRF prior

Yuxiang Xing, Junyan Rong, Hongbin Lu, Hao Zhang, Zhengrong Liang


Published in:Fully3D 2017 Proceedings


ultralow dose CT, pre-log shifted Poisson, image reconstruction, texture, MRF
Computed tomography (CT) dosage is a big concern in clinic. Low-dose CT has attracted a lot of attention in this field. Lowering dose leads to difficulties in reconstruction because of high noise level. In this work we study an ultralow dose CT by using a pre-log shifted-Poisson statistical model and proposed an iterative reconstruction method by optimizing an objective function consisted of pre-log shifted Poisson likelihood and a texture-based MRF (Markov random field). The proposed method was tested on a numerical phantom with ultralow incident photons and electronic noise, as well as an artificial ultralow data simulated from a high-dose patient data. Our results demonstrated the good performance gained from the prelog shifted Poisson model and texture-based MRF prior.
Yuxiang Xing
Tsinghua University, China
Junyan Rong
Fourth Military Medical University, China
Hongbin Lu
Fourth Military Medical University, China
Hao Zhang
Johns Hopkins University, USA
Zhengrong Liang
Stony Brook University, USA
  1. B R Whiting, S Kingshighway, and S L Mo, “Signal statistics of X-ray Computed Tomography,” Proc. SPIE Medical Imaging, 2002, 4682: 53– 60.
  2. I A Elbakri and J A Fessler, “Efficient and accurate likelihood for iterative image reconstruction in x-ray computed tomography,” Proc. SPIE Medical Imaging, 2003, 5032: 1839–1850.
  3. G M Lasio, B R Whiting, and J F Williamson. "Statistical reconstruction for x-ray computed tomography using energy-integrating detectors,“ Physics in Medicine and Biology, 2007, 52(8): 2247.
  4. J Wang, H Lu, Z Liang, D Eremina, G Zhang, S Wang, J Chen, and J Manzione, “An Experimental Study on the Noise Properties of X-ray CT Sinogram Data in Radon Space,” Physics in Medicine and Biology, 2008, 53(12): 3327–3341.
  5. D L Snyder, C W Helstrom, and A D Lanterman, “Compensation for readout noise in CCD images,” Journal of Optical Society of America A, 1995, 12(2): 272–283.
  6. P J Rivière, “Penalized-likelihood sinogram smoothing for low-dose CT,” Medical Physics, 2005, 32(6): 1676–1683.
  7. J Wang, T Li, and L Xing. "Iterative image reconstruction for CBCT using edge-preserving prior," Medical physics, 2009, 36(1): 252–260.
  8. K J Little and P J Rivière, “Sinogram restoration in computed tomography with an edge-preserving penalty,” Medical Physics, 2015, 42: 1307–1320.
  9. B Nett, J Tang, B Aagaard-Kienitz, H Rowley, and G Chen, “Low radiation dose C-arm cone-beam CT based on prior image constrained compressed sensing (PICCS): Including compensation for image volume mismatch between multiple data acquisitions,” Proc. SPIE Medical Imaging, 2009, 7258: 725–803.
  10. J Ma, J Huang, Q Feng, H Zhang, H Lu, Z Liang, and W Chen, “Lowdose CT image restoration using previous normal-dose scan,” Medical Physics, 2011, 38: 5713–5731.
  11. L Ouyang, T Solberg, and J Wang, “Noise reduction in low-dose cone beam CT by incorporating prior volumetric image information,” Medical Physics, 2012, 39: 2569–2577
  12. H Zhang, H Han, Z Liang, Y Hu, Y Liu, W Moore, J Ma, and H Lu, “Extracting information from previous full-dose CT scan for knowledgebased Bayesian reconstruction of current low-dose CT images,” IEEE Transactions on Medical Imaging, 2016, 35(3): 860–70.
  13. G Chen, J Tang, and S Leng, "Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly under-sampled projection data sets," Medical Physics, 2008, 35: 660–663.
  14. L Shen and Y Xing. "Multi-energy CT acquisition and reconstruction with a stepped tube potential scan," Medical Physics, 2015, 42(1): 282– 296.
  15. M Yavuz and J A Fessler. "Statistical image reconstruction methods for randoms-precorrected PET scans," Medical Image Analysis, 1998, 2(4): 369–378.
  16. H Erdogan and J A Fessler. "Monotonic algorithms for transmission tomography," The 5th IEEE EMBS International Summer School, Biomedical Imaging, 2002.