Medical image computing and computer-assisted interventionLung Deformation Estimation and Four-dimensional CT Lung Reconstruction
Section snippets
Methods
Our 4D CT lung reconstruction method is illustrated in Fig 1. First, a reference 3D CT volume is obtained under breath hold. Next, a set of continuous CT scans is taken at every table position to obtain a series of two-dimensional (2D) images while the patient is breathing freely. The 2D image series at every table position covers at least one complete respiratory cycle. Using deformable registration, each 2D image is registered to the reference volume to estimate the displacement field of the
Results
Three experiments were conducted to validate the algorithm. These experiments were based on a synthetic 4D CT, a swine study, and a clinical 4D CT, respectively.
Discussion
This article presents a new methodology to reconstruct a 4D lung image from a set of 2D CT scans and a reference CT volume. The temporal resolution of the method is high and the reconstruction provides good-quality images. Based on a synthetic CT data set, the average registration error was less than 5% of the average lung deformation. Results from a swine study also showed that better image quality can be obtained using the algorithm instead of the image sorting method.
The algorithm is
Acknowledgment
The authors gratefully thank Frank Sauer, PhD, Ali Khamene, PhD, and Christophe Chefd’hotel, PhD, at Siemens Corporate Research for providing the lung dataset. The dataset was originally obtained by the EMC in Rotterdam. The authors also thank David Lindisch, RT, for his assistance with the experiments at Georgetown University.
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Supported by U.S. Army grants DAMD17-99-1-9022 and W81XWH-04-1-0078, NSF Engineering Research Center 9731478.