Improving Intensity-Based Lung CT Registration Accuracy Utilizing Vascular Information

Accurate pulmonary image registration is a challenging problem when the lungs have a deformation with large distance. In this work, we present a nonrigid volumetric registration algorithm to track lung motion between a pair of intrasubject CT images acquired at different inflation levels and introduce a new vesselness similarity cost that improves intensity-only registration. Volumetric CT datasets from six human subjects were used in this study. The performance of four intensity-only registration algorithms was compared with and without adding the vesselness similarity cost function. Matching accuracy was evaluated using landmarks, vessel tree, and fissure planes. The Jacobian determinant of the transformation was used to reveal the deformation pattern of local parenchymal tissue. The average matching error for intensity-only registration methods was on the order of 1 mm at landmarks and 1.5 mm on fissure planes. After adding the vesselness preserving cost function, the landmark and fissure positioning errors decreased approximately by 25% and 30%, respectively. The vesselness cost function effectively helped improve the registration accuracy in regions near thoracic cage and near the diaphragm for all the intensity-only registration algorithms tested and also helped produce more consistent and more reliable patterns of regional tissue deformation.

[1]  Tinsu Pan,et al.  Dynamic ventilation imaging from four-dimensional computed tomography , 2006, Physics in medicine and biology.

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

[3]  Milan Sonka,et al.  Automated segmentation of pulmonary vascular tree from 3D CT images , 2004, SPIE Medical Imaging.

[4]  Kai Ding,et al.  4DCT-based measurement of changes in pulmonary function following a course of radiation therapy. , 2010, Medical physics.

[5]  Michael I. Miller,et al.  Deformable templates using large deformation kinematics , 1996, IEEE Trans. Image Process..

[6]  James M Balter,et al.  Mutual information based CT registration of the lung at exhale and inhale breathing states using thin-plate splines. , 2004, Medical physics.

[7]  Gary E. Christensen,et al.  Tissue volume and vesselness measure preserving nonrigid registration of lung CT images , 2010, Medical Imaging.

[8]  Josien P. W. Pluim,et al.  Semi-automatic Reference Standard Construction for Quantitative Evaluation of Lung CT Registration , 2008, MICCAI.

[9]  Terry S. Yoo,et al.  Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis , 2004 .

[10]  E L Ritman,et al.  Effect of body orientation on regional lung expansion in dog and sloth. , 1985, Journal of applied physiology.

[11]  Brian B. Avants,et al.  Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge , 2011, IEEE Transactions on Medical Imaging.

[12]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[13]  Marleen de Bruijne,et al.  Mass Preserving Image Registration: Results of Evaluation of Methods for Pulmonary Image Registration 2010 Challenge , 2010 .

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

[15]  Cristian Lorenz,et al.  Evaluation of 4D-CT Lung Registration , 2009, MICCAI.

[16]  Michael A. Unser,et al.  Spline pyramids for intermodal image registration using mutual information , 1997, Optics & Photonics.

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

[18]  E. Hoffman,et al.  Mass preserving nonrigid registration of CT lung images using cubic B-spline. , 2009, Medical physics.

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

[20]  G. Christensen,et al.  Regularized Nonrigid Registration of Lung CT I m ages by Preserving Tissue Volu m e and Vesselness Measure , 2010 .

[21]  Stanley Durrleman,et al.  Curve- and surface-based registration of lung CT images via currents , 2009 .

[22]  Eric A. Hoffman,et al.  Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation , 2008, Medical Image Anal..

[23]  Marleen de Bruijne,et al.  Weight Preserving Image Registration for Monitoring Disease Progression in Lung CT , 2008, MICCAI.

[24]  M. Unser,et al.  Spline Pyramids for Inter-Modal Image Registration Using Mutual Information , 1997 .

[25]  Seungyong Lee,et al.  Injectivity Conditions of 2D and 3D Uniform Cubic B-Spline Functions , 2000, Graph. Model..

[26]  Gary E. Christensen,et al.  Transitive Inverse-Consistent Manifold Registration , 2005, IPMI.

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

[28]  Vladimir Pekar,et al.  Assessment of a model-based deformable image registration approach for radiation therapy planning. , 2007, International journal of radiation oncology, biology, physics.

[29]  V. Boldea,et al.  Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans. , 2006, Medical physics.

[30]  R. Rabbitt,et al.  3D brain mapping using a deformable neuroanatomy. , 1994, Physics in medicine and biology.

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

[32]  Tunc Geveci,et al.  Advanced Calculus , 2014, Nature.

[33]  G. Christensen,et al.  Intensity-and-Landmark-Driven , Inverse Consistent , B-Spline Registration and Analysis for Lung Imagery , 2009 .

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

[35]  Eric A. Hoffman,et al.  Regional lung ventilation from volumetric CT scans using image warping functions , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[36]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[37]  Eric A. Hoffman,et al.  Evaluation of Lobar Biomechanics during Respiration Using Image Registration , 2009, MICCAI.

[38]  Chia-Ling Tsai,et al.  An Uncertainty-Driven Hybrid of Intensity-Based and Feature-Based Registration with Application to Retinal and Lung CT Images , 2004, MICCAI.

[39]  Marleen de Bruijne,et al.  Lung CT registration combining intensity, curves and surfaces , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[40]  Joan Alexis Glaunès,et al.  Surface Matching via Currents , 2005, IPMI.

[41]  Paul Suetens,et al.  3D lung registration using splineMIRIT and Robust Tree Registration (RTR) , 2010 .

[42]  Eric A. Hoffman,et al.  Lung Lobar Slippage Assessed with the Aid of Image Registration , 2010, MICCAI.

[43]  Jürgen Weese,et al.  A comparison of similarity measures for use in 2-D-3-D medical image registration , 1998, IEEE Transactions on Medical Imaging.

[44]  Kai Ding,et al.  A cubic B-spline-based hybrid registration of lung CT images for a dynamic airway geometric model with large deformation , 2011, Physics in medicine and biology.

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

[46]  James C. Gee,et al.  Towards a model of lung biomechanics: pulmonary kinematics via registration of serial lung images , 2005, Medical Image Anal..

[47]  Wei Lu,et al.  Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry. , 2007, Medical physics.

[48]  Michael Unser,et al.  Fast parametric elastic image registration , 2003, IEEE Trans. Image Process..

[49]  J. Kybic.  ELASTIC IMAGE REGISTRATION USING PARAMETRIC DEFORMATION MODELS , 2001 .

[50]  Joseph M. Reinhardt,et al.  Anatomy-Guided Lung Lobe Segmentation in X-Ray CT Images , 2009, IEEE Transactions on Medical Imaging.

[51]  K. Murphy,et al.  Evaluation of Methods for Pulmonary Image Registration : The EMPIRE 10 Study , 2010 .

[52]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[53]  Gary E. Christensen.  Inverse consistent registration with object boundary constraints , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[54]  David J. Hawkes,et al.  Voxel similarity measures for 3-D serial MR brain image registration , 1999, IEEE Transactions on Medical Imaging.

[55]  Gary E. Christensen,et al.  Unifying Vascular Information in Intensity-Based Nonrigid Lung CT Registration , 2010, WBIR.

[56]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.