Analysis of evolving processes in pulmonary nodules using a sequence of three-dimensional thoracic images

This paper presents a method to analyze volume evolutions of pulmonary nodules for discrimination between malignant and benign nodules. Our method consists of four steps; The 3D rigid registration of the two successive 3D thoracic CT images, the 3D affine registration of the two successive region-of-interest (ROI) images, non rigid registration between local volumetric ROIs, and analysis of the local displacement field between successive temporal images. In preliminary study, the method was applied to the successive 3D thoracic images of two pulmonary lesions including a metastasis malignant case and an inflammatory benign to quantify the evolving process in the pulmonary nodules and surrounding structure. The time intervals between successive 3D thoracic images for the benign and malignant cases were 120 and 30 days, respectively. From the display of the displacement fields and the contrasted image by the vector field operator based on the Jacobian, it was observed that the benign case reduced in the volume and the surrounding structure was involved into the nodule in the evolution process. It was also observed that the malignant case expanded in the volume. These experimental results indicate that our method is a promising tool to quantify how the lesions evolve their volume and surrounding structures.

[1]  William H. Press,et al.  Numerical recipes in C , 2002 .

[2]  Li Fan,et al.  3D warping and registration from lung images , 1999, Medical Imaging.

[3]  R. Deriche.  Recursively Implementing the Gaussian and its Derivatives , 1993 .

[4]  Hervé Delingette,et al.  Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis , 1999, IPMI.

[5]  N. Müller,et al.  Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation. , 1991, Radiology.

[6]  Noboru Niki,et al.  Lapse observation algorithm for lung cancer detection using 3D thoracic helical CT images , 1998, Medical Imaging.

[7]  M. Kaneko,et al.  Pulmonary organs analysis for differential diagnosis based on thoracic thin-section CT images , 1997, 1997 IEEE Nuclear Science Symposium Conference Record.

[8]  D. Cavouras,et al.  Image analysis methods for solitary pulmonary nodule characterization by computed tomography. , 1992, European journal of radiology.

[9]  R Takaki,et al.  [Simulations of bronchial displacement owing to solitary pulmonary nodules]. , 1999, Nihon Igaku Hoshasen Gakkai zasshi. Nippon acta radiologica.

[10]  K. Mori,et al.  Small nodular lesions in the lung periphery: new approach to diagnosis with CT. , 1990, Radiology.

[11]  Noboru Niki,et al.  Quantitative surface characterization of pulmonary nodules based on thin-section CT images , 1997 .

[12]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[13]  D L Hill,et al.  Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. , 1997, Medical physics.

[14]  栗山 啓子,et al.  CT-pathologic correlation in small peripheral lung cancers , 1988 .

[15]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[16]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[17]  Takayuki Ito,et al.  Image correspondences by warping functions and its applications , 2002, Systems and Computers in Japan.

[18]  J Konishi,et al.  Solitary pulmonary nodule: preliminary study of evaluation with incremental dynamic CT. , 1995, Radiology.

[19]  E A Zerhouni,et al.  CT of the solitary pulmonary nodule. , 1980, AJR. American journal of roentgenology.

[20]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[21]  S J Swensen,et al.  Solitary pulmonary nodule: CT evaluation of enhancement with iodinated contrast material--a preliminary report. , 1992, Radiology.

[22]  H. Ohmatsu,et al.  Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. , 1996, Radiology.

[23]  Rajiv Gupta,et al.  Small pulmonary nodules: evaluation with repeat CT--preliminary experience. , 1999, Radiology.

[24]  J. Thirion,et al.  Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences , 1999, IEEE Transactions on Medical Imaging.

[25]  Noboru Niki,et al.  Tracking interval changes of pulmonary nodules using a sequence of three-dimensional thoracic images , 2000, Medical Imaging: Image Processing.