TY - JOUR
T1 - Nonlinear sinogram smoothing for low-dose X-ray CT
AU - Li, Tianfang
AU - Li, Xiang
AU - Wang, Jing
AU - Wen, Junhai
AU - Lu, Hongbing
AU - Hsieh, Jiang
AU - Liang, Zhengrong
N1 - Funding Information:
Manuscript received November 15, 2003; revised March 18, 2004. This work was supported in part by the NIH National Heart, Lung and Blood Institute by Grant HL54166 and the NIH National Cancer Institute by Grant CA82402. T. Li and J. Wang are with the Departments of Physics and Astronomy and Radiology, State University of New York, Stony Brook, NY 11790 USA (e-mail: tfli@mil.sunysb.edu). X. Li was with the Department of Radiology, State University of New York, Stony Brook, NY 11790 USA. He is now with the Department of Radiation Oncology, Columbia University, New York, NY 10032 USA. H. Lu was with the Department of Radiology, State University of New York, Stony Brook, NY 11790 USA. She is now with the Department of Computer Application, the Fourth General Medical College, Xi’An, China. J. Wen is with the Department of Radiology, State University of New York, Stony Brook, NY 11794 USA. J. Hsieh is with the Applied Science Lab, GE Medical Systems, Milwaukee, WI 53201 USA. Z. Liang is with the Departments of Radiology and Computer Science, State University of New York, Stony Brook, NY 11794 USA. Digital Object Identifier 10.1109/TNS.2004.834824
PY - 2004/10
Y1 - 2004/10
N2 - When excessive quantum noise is present in extremely low dose X-ray CT imaging, statistical properties of the data has to be considered to achieve a satisfactory image reconstruction. Statistical iterative reconstruction with accurate modeling of the noise, rather than a filtered back-projection (FBP) with low-pass filtering, is one way to deal with the problem. Estimating a noise-free sinogram to satisfy the FBP reconstruction for the Radon transform is another way. The benefits of the latter include a higher computation efficiency, more uniform spatial resolution in the reconstructed image, and less modification of the current machine configurations. In a clinic X-ray CT system, the acquired raw data must be calibrated, in addition to the logarithmic transform, to achieve the high diagnostic image quality. The calibrated projection data or sinogram no longer follow a compound Poisson distribution in general, but are close to a Gaussian distribution with signal-dependent variance. In this paper, we first investigated a relatively accurate statistical model for the sinogram data, based on several phantom experiments. Then we developed a penalized likelihood method to smooth the sinogram, which led to a set of nonlinear equations that can be solved by iterated conditional mode (ICM) algorithm within a reasonable computing time. The method was applied to several experimental datasets acquired at 120 kVp, 10 mA/20 mA/50 mA protocols with a GE HiSpeed multi-slice detector CT scanner and demonstrated a significant noise suppression without noticeable sacrifice of the spatial resolution.
AB - When excessive quantum noise is present in extremely low dose X-ray CT imaging, statistical properties of the data has to be considered to achieve a satisfactory image reconstruction. Statistical iterative reconstruction with accurate modeling of the noise, rather than a filtered back-projection (FBP) with low-pass filtering, is one way to deal with the problem. Estimating a noise-free sinogram to satisfy the FBP reconstruction for the Radon transform is another way. The benefits of the latter include a higher computation efficiency, more uniform spatial resolution in the reconstructed image, and less modification of the current machine configurations. In a clinic X-ray CT system, the acquired raw data must be calibrated, in addition to the logarithmic transform, to achieve the high diagnostic image quality. The calibrated projection data or sinogram no longer follow a compound Poisson distribution in general, but are close to a Gaussian distribution with signal-dependent variance. In this paper, we first investigated a relatively accurate statistical model for the sinogram data, based on several phantom experiments. Then we developed a penalized likelihood method to smooth the sinogram, which led to a set of nonlinear equations that can be solved by iterated conditional mode (ICM) algorithm within a reasonable computing time. The method was applied to several experimental datasets acquired at 120 kVp, 10 mA/20 mA/50 mA protocols with a GE HiSpeed multi-slice detector CT scanner and demonstrated a significant noise suppression without noticeable sacrifice of the spatial resolution.
KW - Iterated conditional mode
KW - Low dose
KW - Penalized weighted least square
KW - Sinogram smoothing
KW - X-ray computed tomography (CT)
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U2 - 10.1109/TNS.2004.834824
DO - 10.1109/TNS.2004.834824
M3 - Article
AN - SCOPUS:8344272167
SN - 0018-9499
VL - 51
SP - 2505
EP - 2513
JO - IEEE Transactions on Nuclear Science
JF - IEEE Transactions on Nuclear Science
IS - 5 II
ER -