Real‐time CT image generation based on voxel‐by‐voxel modeling of internal deformation by utilizing the displacement of fiducial markers

Purpose To show the feasibility of real‐time CT image generation technique utilizing internal fiducial markers that facilitate the evaluation of internal deformation. Methods In the proposed method, a linear regression model that can derive internal deformation from the displacement of fiducial markers is built for each voxel in the training process before the treatment session. Marker displacement and internal deformation are derived from the four‐dimensional computed tomography (4DCT) dataset. In the treatment session, the three‐dimensional deformation vector field is derived according to the marker displacement, which is monitored by the real‐time imaging system. The whole CT image can be synthesized by deforming the reference CT image with a deformation vector field in real‐time. To show the feasibility of the technique, image synthesis accuracy and tumor localization accuracy were evaluated using the dataset generated by extended NURBS‐Based Cardiac‐Torso (XCAT) phantom and clinical 4DCT datasets from six patients, containing 10 CT datasets each. In the validation with XCAT phantom, motion range of the tumor in training data and validation data were about 10 and 15 mm, respectively, so as to simulate motion variation between 4DCT acquisition and treatment session. In the validation with patient 4DCT dataset, eight CT datasets from the 4DCT dataset were used in the training process. Two excluded inhale CT datasets can be regarded as the datasets with large deformations more than training dataset. CT images were generated for each respiratory phase using the corresponding marker displacement. Root mean squared error (RMSE), normalized RMSE (NRMSE), and structural similarity index measure (SSIM) between the original CT images and the synthesized CT images were evaluated as the quantitative indices of the accuracy of image synthesis. The accuracy of tumor localization was also evaluated. Results In the validation with XCAT phantom, the mean NRMSE, SSIM, and three‐dimensional tumor localization error were 7.5 ± 1.1%, 0.95 ± 0.02, and 0.4 ± 0.3 mm, respectively. In the validation with patient 4DCT dataset, the mean RMSE, NRMSE, SSIM, and three‐dimensional tumor localization error in six patients were 73.7 ± 19.6 HU, 9.2 ± 2.6%, 0.88 ± 0.04, and 0.8 ± 0.6 mm, respectively. These results suggest that the accuracy of the proposed technique is adequate when the respiratory motion is within the range of the training dataset. In the evaluation with a marker displ