CT image construction of a totally deflated lung using deformable model extrapolation

A novel technique is proposed to construct CT image of a totally deflated lung from a free-breathing 4D-CT image sequence acquired preoperatively. Such a constructed CT image is very useful in performing tumor ablative procedures such as lung brachytherapy. Tumor ablative procedures are frequently performed while the lung is totally deflated. Deflating the lung during such procedures renders preoperative images ineffective for targeting the tumor. Furthermore, the problem cannot be solved using intraoperative ultrasound (U.S.) images because U.S. images are very sensitive to small residual amount of air remaining in the deflated lung. One possible solution to address these issues is to register high quality preoperative CT images of the deflated lung with their corresponding low quality intraoperative U.S. images. However, given that such preoperative images correspond to an inflated lung, such CT images need to be processed to construct CT images pertaining to the lung's deflated state.

To obtain the CT images of deflated lung, we present a novel image construction technique using extrapolated deformable registration to predict the deformation the lung undergoes during full deflation. The proposed construction technique involves estimating the lung's air volume in each preoperative image automatically in order to track the respiration phase of each 4D-CT image throughout a respiratory cycle; i.e., the technique does not need any external marker to form a respiratory signal in the process of curve fitting and extrapolation. The extrapolated deformation field is then applied on a preoperative reference image in order to construct the totally deflated lung's CT image. The technique was evaluated experimentally using ex vivo porcine lung.

The ex vivo lung experiments led to very encouraging results. In comparison with the CT image of the deflated lung we acquired for the purpose of validation, the constructed CT image was very similar. The intensity mean absolute difference between these two images was calculated to be at 1%. Tumor center as well as a number of anatomical fiducial markers were traced in different corresponding slices of the two images. The average misalignment obtained for the constructed CT image was (0.64, 0.39, 0.11) mm, which indicates a very desirable accuracy for lung brachytherapy applications.

The image construction accuracy obtained in this research is suitable for intraoperative tasks; e.g., tumor localization and fusing with real time navigation data in lung brachytherapy. These applications involve image registration with intraoperative U.S. images in order to enhance their poor quality. The proposed technique is also useful for preoperative tasks such as planning of lung brachytherapy treatment.