Evaluation of a 4D cone-beam CT reconstruction approach using a simulation framework

Current image-guided navigation systems for thoracic abdominal interventions utilize three dimensional (3D) images acquired at breath-hold. As a result they can only provide guidance at a specific point in the respiratory cycle. The intervention is thus performed in a gated manner, with the physician advancing only when the patient is at the same respiratory cycle in which the 3D image was acquired. To enable a more continuous workflow we propose to use 4D image data. We describe an approach to constructing a set of 4D images from a diagnostic CT acquired at breath-hold and a set of intraoperative cone-beam CT (CBCT) projection images acquired while the patient is freely breathing. Our approach is based on an initial reconstruction of a gated 4D CBCT data set. The 3D CBCT images for each respiratory phase are then non-rigidly registered to the diagnostic CT data. Finally the diagnostic CT is deformed based on the registration results, providing a 4D data set with sufficient quality for navigation purposes. In this work we evaluate the proposed reconstruction approach using a simulation framework. A 3D CBCT dataset of an anthropomorphic phantom is deformed using internal motion data acquired from an animal model to create a ground truth 4D CBCT image. Simulated projection images are then created from the 4D image and the known CBCT scan parameters. Finally, the original 3D CBCT and the simulated X-ray images are used as input to our reconstruction method. The resulting 4D data set is then compared to the known ground truth by normalized cross correlation (NCC). We show that the deformed diagnostic CTs are of better quality than the gated reconstructions with a mean NCC value of 0.94 versus a mean 0.81 for the reconstructions.