양전자 방출 단층 촬영을 위한 3차원 최대사후 영상재구성에서 평활 파라미터의 적응형 미세 조정
Adaptive Fine-Tuning of Smoothing Parameters in 3-D MAP Image Reconstruction for Positron Emission Tomography
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초록
The maximum a posteriori (MAP) approach has played an important role in developing model-based iter- ative reconstruction methods for positron emission tomography (PET). In this approach, the objective function con- sists of the likelihood and prior terms, with the balance between these two terms controlled by a positive smoothing parameter. When the prior term takes a quadratic form as a regularizer, the objective function can be easily optimized using the method of iterated conditional modes (ICM). However, the quadratic regularizer has the disadvantage of being unable to preserve the edges of the reconstructed image. In this study, we propose a new method for enhancing edges even with a quadratic regularizer within the ICM framework by adaptively fine-tuning the smoothing param- eter. To adjust the smoothing parameter, we use a method that transforms the histogram, obtained from the pixel- wise standard deviation of the estimated image in the previous iteration, into a mapping function for assigning an optimal value of the smoothing parameter for each pixel. The simulation results demonstrate that the proposed method improves the reconstruction accuracy in terms of the percentage error as well as the bias calculated from independent noise trials by efficiently enhancing the edges.
Abstract
The maximum a posteriori (MAP) approach has played an important role in developing model-based iter- ative reconstruction methods for positron emission tomography (PET). In this approach, the objective function con- sists of the likelihood and prior terms, with the balance between these two terms controlled by a positive smoothing parameter. When the prior term takes a quadratic form as a regularizer, the objective function can be easily optimized using the method of iterated conditional modes (ICM). However, the quadratic regularizer has the disadvantage of being unable to preserve the edges of the reconstructed image. In this study, we propose a new method for enhancing edges even with a quadratic regularizer within the ICM framework by adaptively fine-tuning the smoothing param- eter. To adjust the smoothing parameter, we use a method that transforms the histogram, obtained from the pixel- wise standard deviation of the estimated image in the previous iteration, into a mapping function for assigning an optimal value of the smoothing parameter for each pixel. The simulation results demonstrate that the proposed method improves the reconstruction accuracy in terms of the percentage error as well as the bias calculated from independent noise trials by efficiently enhancing the edges.
참고문헌 (21)
- Cherry SR, "Physics in Nuclear Medicine" (2012), Saunders
- Ollinger JH, "Positron emission tomography", IEEE Signal Process. Mag, 14, pp.43~55 (1997)
- Qi J, "Iterative reconstruction techniques in emission computed tomography", Phys. Med. Biol, 51, pp.541~578 (2006)
- Lewitt RM, "Overview of methods for image reconstruction from projections in emission computed tomography", Proc. IEEE, 91, pp.1588~1611 (2003)
- Reader AJ, "Advances in PET image reconstruction", PET Clin, 2, pp.173~190 (2007)
- Rosenfeld A, "Digital Picture Processing" (1976), Academic Press
- Lange K, "EM reconstruction algorithms for emission and transmission tomography", J. Comput. Assist. Tomogr, 8(2), pp.306~316 (1984)
- Wilson DW, "Noise properties of filtered-backprojection and ML-EM reconstructed emission tomographic images", IEEE Trans. Nucl. Sci, 40(4), pp.1198~1203 (1993)
- Barrett HH, "Noise properties of the EM algorithm. I", Theory. Phys. Med. Biol, 39(5), pp.833~846 (1994)
- Lee S-J, "Bayesian image reconstruction in SPECT using higher order mechanical models as priors", IEEE Trans. Medical Imaging, 14(4), pp.669~680 (1995)
- Lee S-J, "Performance comparison of convex-nonquadratic priors for Bayesian tomographic reconstruction", Journal of Electronic Imaging, 9(3), pp.242~250 (2000)
- Geman S, "Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images", IEEE Trans. Patt. Anal. Mach. Intel, 6(6), pp.721~741 (1984)
- Ramani S, "Regularization parameter selection for nonlinear iterative image restoration and MRI reconstruction using GCV and sure-based methods", IEEE Trans. Image Process, 21, pp.3659~3672 (2012)
- Zhu X, "Automatic parameter selection for denoising algorithms using a no-reference measure of image content", IEEE Trans. Image Process, 19, pp.3116~3132 (2010)
- Liang H, "Comparison-based image quality assessment for selecting image restoration parameters", IEEE Trans. Image Process, 25, pp.5118~5130 (2016)
- Lee S-J, "The thin plate as a regularizer in Bayesian SPECT reconstruction", IEEE Trans. Nuclear Science, 44(3), pp.1381~1387 (1997)
- Shen C, "Intelligent parameter tuning in optimization-based iterative CT reconstruction via deep reinforcement learning", IEEE Trans. Med. Imaging, 37, pp.1430~1439 (2018)
- Lee J, "Smoothing-parameter tuning for regularized PET image reconstruction using deep learning", Proc. SPIE Medical Imaging, pp.124632C1~124632C6 (2023)
- Lee SJ, "Training set approach to smoothing parameter estimation for spline-regularized tomographic reconstruction", Opt. Eng, 48(2), pp.027003~ (2009)
- Hudson HM, "Accelerated image reconstruction using ordered subsets of projection data", IEEE Trans. Med. Imag, 13(4), pp.601~609 (1994)
- Reader AJ, "Deep learning for PET image reconstruction", IEEE Trans. Rad. Plasma Med. Sci, 5(1), pp.1~25 (2021)