Tissue volume and vesselness measure preserving nonrigid registration of lung CT images

In registration-based analyses of lung biomechanics and function, high quality registrations are essential to obtain meaningful results. Various criteria have been suggested to find the correspondence mappings between two lung images acquired at different levels of inflation. In this paper, we describe a new metric, the sum of squared vesselness measure difference (SSVMD), that utilizes the rich information of blood vessel locations and matches similar vesselness patterns in two images. Preserving both the lung tissue volume and the vesselness measure, a registration algorithm is developed to minimize the sum of squared tissue volume difference (SSTVD) and SSVMD together. We compare the registration accuracy using SSTVD + SSVMD with that using SSTVD alone by registering lung CT images of three normal human subjects. After adding the new SSVMD metric, the improvement of registration accuracy is observed by landmark error and fissure positioning error analyses. The average values of landmark error and fissure positioning error are reduced by about 30% and 25%, respectively. The mean landmark error is on the order of 1 mm. Statistical testing of landmark errors shows that there is a statistically significant difference between two methods with p values < 0.05 in all three subjects. Visual inspection shows there are obvious accuracy improvements in the lung regions near the thoracic cage after adding SSVMD.

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

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

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

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

[5]  Eric A. Hoffman,et al.  Registration-based regional lung mechanical analysis: retrospectively reconstructed dynamic imaging versus static breath-hold image acquisition , 2009, Medical Imaging.

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

[7]  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.

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

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

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

[11]  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..

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