Lung deformation estimation and four-dimensional CT lung reconstruction

Abstract

Rationale and objectives: Four-dimensional (4D) computed tomography (CT) can be used in radiation treatment planning to account for respiratory motion. Current 4D CT techniques have limitations in either spatial or temporal resolution. In addition, most of these techniques rely on auxiliary surrogates to relate the time of the CT scan to the patient's respiratory phase. We propose a 4D CT method for lung applications to overcome these problems.

Materials and methods: A set of axial scans are taken at multiple table positions to obtain a series of two-dimensional images while the patient is breathing freely. Each two-dimensional image is registered to a reference CT volume. The deformation of the image with respect to the volume is used to synchronize the image with the respiratory cycle assuming that there is no phase variation along the craniocaudal direction. The reconstructed 4D dataset is a series of deformable transformations of the reference volume.

Results: A synthetic 4D dataset showed that the registration error is less than 5% of the image deformation. A swine study showed that the algorithm can generate better image quality than the image sorting method. A respiratory-gated 4D dataset showed that the algorithm's result is consistent with the ground truth.

Conclusion: The algorithm can reconstruct good quality 4D images without external surrogates even if the CT scans are acquired under irregular respiratory motion. The algorithm may allow for reduced radiation dose to the patient with a limited loss of image quality. Although the phase variation exists along the craniocaudal direction, the 4D reconstruction is reasonably accurate.