Super-resolution reconstruction for lung four dimensional computed tomography images using multi-model Gaussian process regression_Journal of Biomedical Engineering

Authors：

FANG Shiting ^1,2 , ZHONG Tao ^1,2 , CHEN Jin ^1,2 ,  ZHANG Yu ^1,2

1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, P.R.China;
2. Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, P.R.China;

Corresponding author：

ZHANG Yu, Email: yuzhang@smu.edu.cn

Keywords：

lung four dimensional computed tomography; super-resolution reconstruction; Gaussian process regression

DOI：

10.7507/1001-5515.201704048

Video：

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Lung four dimensional computed tomography (4D-CT) can lead to accurate radiotherapy. However, for the safety of patients, the scan spacing of 4D-CT cannot be too small so that the inter-slice resolution of lung 4D-CT is low, and thus the coronal and sagittal images need to be interpolated to obtain high-resolution images. This paper presents a super-resolution reconstruction technique based on multi-model Gaussian process regression. We use the high-resolution transversal images and the corresponding low-resolution images as the training sets. The high-resolution pixels of the coronal and sagittal images can be predicted by constructing multiple Gaussian process regression models. The experimental results show that our method is superior to bicubic algorithm, projections onto convex sets, sparse coding, multi-phase similarity based method and Gaussian process regression method based on self-learning block in terms of the edge and detail recovery. The results demonstrate that the proposed method can effectively improve the quality of lung 4D-CT images, and potentially be applied to better image-guided radiation therapy of lung cancer.

Citation： FANG Shiting, ZHONG Tao, CHEN Jin, ZHANG Yu. Super-resolution reconstruction for lung four dimensional computed tomography images using multi-model Gaussian process regression. Journal of Biomedical Engineering, 2017, 34(6): 922-927. doi: 10.7507/1001-5515.201704048 Copy

1.	Khan F, Bell G, Antony J, et al. The use of 4DCT to reduce lung dose: a dosimetric analysis. Med Dosim, 2009, 34(4): 273-278.
2.	张书旭, 周凌宏, 陈光杰, 等. 4D-CT 重建及其在肺癌放疗中的应用研究进展. 中国辐射卫生, 2008, 17(3): 375-377.
3.	Yang J, Huang T. Image super-resolution: historical overview and future challenges, 2010: 20-34.
4.	Irani M, Peleg S. Motion analysis for image enhancement: resolution, occlusion, and transparency. J Vis Commun Image Represent, 1993, 4(4): 324-335.
5.	Yang Jianchao, Wright J, Huang T S, et al. Image super-resolution via sparse representation. IEEE Trans Image Process, 2010, 19(11): 2861-2873.
6.	Zhang Yu, Wu Xiuxiu, Yang Wei, et al. Super-resolution Reconstruction for 4D computed tomography of the lung via the projections onto convex sets approach. Med Phys, 2014, 41(11): 111917-111917.
7.	陈瑾, 张煜. 基于多相位图像相似性的肺 4D-CT 超分辨率重建, 计算机工程与应用, 2017, 53(15): 191-196.
8.	He He, Siu Wanchi. Single image super-resolution using Gaussian process regression//Computer Vision and Pattern Recognition, 2011 IEEE Conference on IEEE, 2011: 449-456.
9.	何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述. 控制与决策, 2013, 28(8): 1121-1129, 1137.
10.	Williams C K I, Rasmussen C E. Gaussian processes for machine learning. the MIT Press, 2006, 2(3): 4.
11.	Castillo R, Castillo E, Guerra R, et al. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys Med Biol, 2009, 54(7): 1849-1870.
12.	Vandemeulebroucke J, Rit S, Kybic J, et al. Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med Phys, 2011, 38(1): 166-178.
13.	Greenspan H, Oz G, Kiryati N, et al. MRI inter-slice Reconstruction using super-resolution. Magn Reson Imaging, 2002, 20(5): 437-446.

1. Khan F, Bell G, Antony J, et al. The use of 4DCT to reduce lung dose: a dosimetric analysis. Med Dosim, 2009, 34(4): 273-278.
2. 张书旭, 周凌宏, 陈光杰, 等. 4D-CT 重建及其在肺癌放疗中的应用研究进展. 中国辐射卫生, 2008, 17(3): 375-377.
3. Yang J, Huang T. Image super-resolution: historical overview and future challenges, 2010: 20-34.
4. Irani M, Peleg S. Motion analysis for image enhancement: resolution, occlusion, and transparency. J Vis Commun Image Represent, 1993, 4(4): 324-335.
5. Yang Jianchao, Wright J, Huang T S, et al. Image super-resolution via sparse representation. IEEE Trans Image Process, 2010, 19(11): 2861-2873.
6. Zhang Yu, Wu Xiuxiu, Yang Wei, et al. Super-resolution Reconstruction for 4D computed tomography of the lung via the projections onto convex sets approach. Med Phys, 2014, 41(11): 111917-111917.
7. 陈瑾, 张煜. 基于多相位图像相似性的肺 4D-CT 超分辨率重建, 计算机工程与应用, 2017, 53(15): 191-196.
8. He He, Siu Wanchi. Single image super-resolution using Gaussian process regression//Computer Vision and Pattern Recognition, 2011 IEEE Conference on IEEE, 2011: 449-456.
9. 何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述. 控制与决策, 2013, 28(8): 1121-1129, 1137.
10. Williams C K I, Rasmussen C E. Gaussian processes for machine learning. the MIT Press, 2006, 2(3): 4.
11. Castillo R, Castillo E, Guerra R, et al. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys Med Biol, 2009, 54(7): 1849-1870.
12. Vandemeulebroucke J, Rit S, Kybic J, et al. Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med Phys, 2011, 38(1): 166-178.
13. Greenspan H, Oz G, Kiryati N, et al. MRI inter-slice Reconstruction using super-resolution. Magn Reson Imaging, 2002, 20(5): 437-446.

Journal of Biomedical Engineering

Super-resolution reconstruction for lung four dimensional computed tomography images using multi-model Gaussian process regression

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