Alignment of CT lung volumes with an optical flow method.

RATIONALE AND OBJECTIVES This study was performed to evaluate an optical flow method for registering serial computed tomographic (CT) images of lung volumes to assist physicians in visualizing and assessing changes between CT scans. MATERIALS AND METHODS The optical flow method is a coarse-to-fine model-based motion estimation technique for estimating first a global parametric transformation and then local deformations. Five serial pairs of CT images of lung volumes that were misaligned because of patient positioning, respiration, and/or different fields of view were used to test the method. RESULTS Lung volumes depicted on the serial paired images initially were correlated at only 28%-68% because of misalignment. With use of the optical flow method, the serial images were aligned to at least 95% correlation. CONCLUSION The optical flow method enables a direct comparison of serial CT images of lung volumes for the assessment of nodules or functional changes in the lung.

[1]  Y. Kawata,et al.  Computer-aided diagnosis for pulmonary nodules based on helical CT images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[2]  Eric A. Hoffman,et al.  Evaluation and application of 3D lung warping and registration model using HRCT images , 2001, SPIE Medical Imaging.

[3]  S. Armato,et al.  Automated detection of lung nodules in CT scans: preliminary results. , 2001, Medical physics.

[4]  K. Doi,et al.  Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multiple-template matching technique. , 2001, Medical physics.

[5]  Leon Axel,et al.  Validation of an optical flow method for tag displacement estimation , 1999, IEEE Transactions on Medical Imaging.

[6]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[7]  Manuel G. Penedo,et al.  Computer-aided diagnosis: a neural-network-based approach to lung nodule detection , 1998, IEEE Transactions on Medical Imaging.

[8]  Donald J. Gerson 24th AIPR Workshop on Tools and Techniques for Modeling and Simulation , 1996 .

[9]  Yan Li,et al.  Feature extraction and recognition of harbor contour , 2001, International Symposium on Multispectral Image Processing and Pattern Recognition.

[10]  Li Fan,et al.  Integrated approach to 3D warping and registration from lung images , 1999, Optics & Photonics.

[11]  Margrit Betke,et al.  Chest CT: automated nodule detection and assessment of change over time--preliminary experience. , 2001, Radiology.

[12]  Richard H. Moore,et al.  Detecting lesions in magnetic resonance breast scans , 1996, Other Conferences.