Online 4-D CT estimation for patient-specific respiratory motion based on real-time breathing signals

Authors:
Tiancheng He;Zhong Xue;Weixin Xie;Stephen T. C. Wong
Affiliations:
The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, TX and Intellig ...;The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, TX;Intelligent Information Institute, Shenzhen University, Shenzhen, China;The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, TX
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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Abstract

In image-guided lung intervention, the electromagnetic (EM) tracked needle can be visualized in a pre-procedural CT by registering the EM tracking and the CT coordinate systems. However, there exist discrepancies between the static pre-procedural CT and the patient due to respiratory motion. This paper proposes an online 4-D CT estimation approach to patient-specific respiratory motion compensation. First, the motion patterns between 4-D CT data and respiratory signals such as fiducials from a number of patients are trained in a template space after image registration. These motion patterns can be used to estimate the patient-specific serial CTs from a static 3-D CT and the real-time respiratory signals of that patient, who do not generally take 4-D CTs. Specifically, the respiratory lung field motion vectors are projected onto the Kernel Principal Component Analysis (K-PCA) space, and a motion estimation model is constructed to estimate the lung field motion from the fiducial motion using the ridge regression method based on the least squares support vector machine (LS-SVM). The algorithm can be performed onsite prior to the intervention to generate the serial CT images according to the respiratory signals in advance, and the estimated CTs can be visualized in real-time during the intervention. In experiments, we evaluated the algorithm using leave-one-out strategy on 30 4-D CT data, and the results showed that the average errors of the lung field surfaces are 1.63mm.