An automated method for registration and perfusion analysis of pulmonary CT data for evaluating response to radiotherapy in patient with non-small cell lung cancer

Yu-Tzu Lee; Chi-Hsuan Tsou; Yeun-Chung Chang; Chung-Ming Chen

doi:10.1117/12.2006741

29 March 2013 An automated method for registration and perfusion analysis of pulmonary CT data for evaluating response to radiotherapy in patient with non-small cell lung cancer

Yu-Tzu Lee, Chi-Hsuan Tsou, Yeun-Chung Chang, Chung-Ming Chen

Author Affiliations +

Proceedings Volume 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging; 86721X (2013) https://doi.org/10.1117/12.2006741
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Perfusion computed tomography (CT) has been widely used to assess the response of lung cancer treatment. However, the respiratory motion has become the major obstacle to the pixel-based time-series analyses. To minimize the effect of respiratory motion and investigate the feasibility of perfusion CT for prediction of tumor response and prognosis of non-small cell lung cancer, an image registration framework is proposed by unifying a virtual 3D local rigid alignment and 3D global non-rigid alignment. The basic idea is to use the perfusion CT data and routine whole-lung CT data, respectively. To realize this idea, maximum intensity projection (MIP) of the time series perfusion CT images is first generated, followed by decomposing the MIP image into region of interest (ROI), which is located on a lung nodule. For the ROI, affine transformation model based on mutual information is performed to estimate the virtual three dimensional linear deformations. Following that, the 3D thin plate spline (TPS) is carried out to establish the pixel correspondence between the paired volumetric CT data. The control points for the TPS are global feature points chosen from the boundary of whole lung, which are automatically derived by using the iterative closest point (ICP) matching Algorithm. The proposed algorithm has been evaluated both qualitatively and quantitatively on real lung perfusion CT datasets. From the time-intensity curves and perfusion parameters, the experiment results suggest that the findings on perfusion CT images obtained after treatment may be considered as a significant predictor of lung cancer.

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Yu-Tzu Lee, Chi-Hsuan Tsou, Yeun-Chung Chang, and Chung-Ming Chen "An automated method for registration and perfusion analysis of pulmonary CT data for evaluating response to radiotherapy in patient with non-small cell lung cancer", Proc. SPIE 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging, 86721X (29 March 2013); https://doi.org/10.1117/12.2006741

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KEYWORDS

Computed tomography

Tumors

Lung cancer

Lung

3D image processing

Image registration

Radiotherapy

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