Establishing a normative atlas of the human lung: computing the average transformation and atlas construction.

RATIONALE AND OBJECTIVES To establish the range of normal for quantitative computed tomography (CT)-based measures of lung structure and function, we seek to develop methods for matching pulmonary structures across individuals and establishing a normative human lung atlas. MATERIALS AND METHODS In our previous work, we have presented a three-dimensional (3D) image registration method suitable for pulmonary atlas construction based on CT datasets. The method has been applied to a population of normative lungs in multiple experiments and, in each instance, has resulted in significant reductions in registration errors. This study is a continuation to our previous work by presenting a method for synthesizing a computerized human lung atlas from previously registered and matched 3D pulmonary CT datasets from a population of normative subjects. Our method consists of defining the origin of the atlas coordinate system; defining the nomenclature and labels for anatomical structures within the atlas system; computing the average transformation based on the displacement fields to register individual subject to the common template subject; constructing the atlas by deforming the template with the average transformation; and calculating shape variations within the population. RESULTS The feasibility of pulmonary atlas construction was evaluated using CT datasets from 20 normal volunteers. Substantial reductions in shape variability were demonstrated. In addition, the constructed atlas depends only slightly on a specific subject being selected as the template. These results indicate the framework is a robust and valid method for pulmonary atlas construction based on CT scans. The atlas consists of a grayscale CT dataset of the template, a labeled mask dataset of the template (ie, lungs, lobes, and lobar fissures are labeled with different gray levels), a data set representing the population's average shape, datasets representing the population's shape variations (ie, the magnitude of standard deviation), a data structure to contain the labels and coordinates of major airway branchpoints, and the labels of the mask dataset, and a reference coordinate system for each lung. CONCLUSION A computerized human lung atlas representing by the average shape of a population of twenty normal subjects was constructed and visualized. The atlas provides a basis for establishing regional ranges of normative values for structural and functional measures of the human lung. In the future, we plan to use the computerized human lung atlas to help detect and quantify early signs of lung pathology.

[1]  Eric A Hoffman,et al.  Subsecond multisection CT of regional pulmonary ventilation. , 2002, Academic radiology.

[2]  Gary E. Christensen,et al.  Consistent landmark and intensity-based image registration , 2002, IEEE Transactions on Medical Imaging.

[3]  Ruzena Bajcsy,et al.  Multiresolution elastic matching , 1989, Comput. Vis. Graph. Image Process..

[4]  Paul M. Thompson,et al.  A framework for computational anatomy , 2002 .

[5]  B. Simon,et al.  Distribution of pulmonary ventilation using Xe-enhanced computed tomography in prone and supine dogs. , 2001, Journal of applied physiology.

[6]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[7]  J. Chu,et al.  CT and PET lung image registration and fusion in radiotherapy treatment planning using the chamfer-matching method. , 1999, International journal of radiation oncology, biology, physics.

[8]  Gary E. Christensen,et al.  Validation of probabilistic anatomical shape atlases , 2000, Medical Imaging: Image Processing.

[9]  D. Louis Collins,et al.  Application of Information Technology: A Four-Dimensional Probabilistic Atlas of the Human Brain , 2001, J. Am. Medical Informatics Assoc..

[10]  Michael I. Miller,et al.  Volumetric transformation of brain anatomy , 1997, IEEE Transactions on Medical Imaging.

[11]  Eric A. Hoffman,et al.  Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images , 2001, IEEE Transactions on Medical Imaging.

[12]  Michael F. McNitt-Gray,et al.  Method for segmenting chest CT image data using an anatomical model: preliminary results , 1997, IEEE Transactions on Medical Imaging.

[13]  E. Hoffman,et al.  Advances in physiologic lung assessment via electron beam computed tomography , 1998, Proceedings Pacific Medical Technology Symposium-PACMEDTek. Transcending Time, Distance and Structural Barriers (Cat. No.98EX211).

[14]  A. Khotanzad,et al.  A physics-based coordinate transformation for 3-D image matching , 1997, IEEE Transactions on Medical Imaging.

[15]  Gary E. Christensen,et al.  Synthesizing average 3D anatomical shapes using deformable templates , 1999, Medical Imaging.

[16]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Eric A. Hoffman,et al.  3D intersubject warping and registration of pulmonary CT images for a human lung model , 2002, SPIE Medical Imaging.

[18]  E. Hoffman,et al.  Utilization of 3-D elastic transformation in the registration of chest X-ray CT and whole body PET , 1996 .

[19]  Milan Sonka,et al.  Segmentation of intrathoracic airway trees: a fuzzy logic approach , 1998, IEEE Transactions on Medical Imaging.

[20]  Eric A. Hoffman,et al.  CT-based assessment of regional pulmonary blood flow parameters: an update , 1999, Medical Imaging.

[21]  Milan Sonka,et al.  Integrated system for CT-based assessment of parenchymal lung disease , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[22]  U. Grenander,et al.  Statistical methods in computational anatomy , 1997, Statistical methods in medical research.

[23]  Karl J. Friston,et al.  Incorporating Prior Knowledge into Image Registration , 1997, NeuroImage.

[24]  J Hildebrandt,et al.  Determination of regional ventilation and perfusion in the lung using xenon and computed tomography. , 2001, Journal of applied physiology.

[25]  D. Louis Collins,et al.  Automated 3D nonlinear deformation procedure for determination of gross morphometric variability in human brain , 1994, Other Conferences.

[26]  Joseph M. Reinhardt,et al.  Detection of lung lobar fissures using fuzzy logic , 1999, Medical Imaging.

[27]  Eric A. Hoffman,et al.  Advances in physiologic lung assessment via electron beam computed tomography (EBCT) , 1999, Optics & Photonics.

[28]  E. Hoffman,et al.  Assessment of the pulmonary structure-function relationship and clinical outcomes measures: quantitative volumetric CT of the lung. , 1997, Academic radiology.

[29]  S. Levin.  Lectu re Notes in Biomathematics , 1983 .

[30]  Gary E. Christensen,et al.  Evaluating template bias when synthesizing population averages , 2001, SPIE Medical Imaging.

[31]  H Kitaoka,et al.  A three-dimensional model of the human airway tree. , 1999, Journal of applied physiology.

[32]  Lawrence B. Wolff,et al.  Analysis of the Pulmonary Vascular Tree Using Differential Geometry Based Vector Fields , 1997, Comput. Vis. Image Underst..

[33]  Milan Sonka,et al.  Segmentation, Skeletonization, and Branchpoint Matching - A Fully Automated Quantitative Evaluation of Human Intrathoracic Airway Trees , 2002, MICCAI.

[34]  Fred L. Bookstein,et al.  Morphometric Tools for Landmark Data. , 1998 .

[35]  Gary E. Christensen,et al.  Consistent image registration , 2001, IEEE Transactions on Medical Imaging.

[36]  Geoffrey McLennan,et al.  Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images. , 2003, Academic radiology.

[37]  Joseph M. Reinhardt,et al.  3D pulmonary CT image registration with a standard lung atlas , 2000, Medical Imaging.

[38]  E. Hoffman,et al.  Pulmonary CT image registration and warping for tracking tissue deformation during the respiratory cycle through 3D consistent image registration. , 2008, Medical physics.

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