Improved Cone-Beam Reconstruction Using a Prior Breathing Motion Model

Abstract

<h3>Purpose/Objective(s)</h3> To demonstrate whether incorporating a pre-measured breathing motion model into algebraic cone-beam CT (CBCT) reconstruction could improve daily target imaging for radiotherapy delivery of lung tumors. <h3>Materials/Methods</h3> We employed our clinic's 5DCT simulation technique to generate the breathing motion model. 5DCT provides a voxel-by-voxel representation of breathing motion as a function of breathing amplitude and rate, and the motion model was used to characterize the breathing motion during CBCT acquisition. We acquired CBCT projections before treatment and simultaneously acquired a breathing amplitude signal using a respiratory bellows. The 5DCT reference image was registered to the CBCT geometry using a rigid registration of the spine. To enable the use of the 5DCT motion model on the CBCT data, a correspondence between the simulation and CBCT surrogates needed to be made. The breathing surrogates of the simulation were correlated with the CBCT using the diaphragm shadow in the CBCT projections and the 5DCT reference image deformed using the motion model. By adjusting the 5DCT model breathing amplitude, the diaphragm shadow moved until the CBCT projection and 5DCT diaphragm shadows overlapped, yielding the correspondence between the two surrogates such that the 5DCT motion model could be directly applied to the CBCT reconstruction. To reconstruct the CBCT image, we modified the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) by introducing the aligned and scaled 5DCT motion model parameters. The modified MC-SART compared the raw projections to corresponding model-generated projections with various levels of binning. To quantify the 5DCT model's ability to describe the breathing motion during the CBCT, we reconstructed the CBCT dividing the breathing amplitude into 1, 2, 4 and 8 bins. We examined the corresponding sharpness of the diaphragm to see if the diaphragm boundary continuously sharpened with increasing bins. <h3>Results</h3> We found that the diaphragm appeared most blurred when only 1 amplitude bin was used, appearing sharper as bins were added. At 8 bins, the diaphragm boundary was only 2 voxels, consistent with typical breath-hold CT reconstruction. This demonstrated that the motion model from the CT simulation could quantitatively track breathing motion during the CBCT. However, while the diaphragm appeared sharper, the reconstructed images with increasing bins also had poorer image contrast. <h3>Conclusion</h3> We have shown that using a previously determined breathing motion model with algebraic reconstruction can improve image sharpness. We will expand the current study to more patients and fractions and develop techniques to improve image contrast. However, since the purpose of this approach will be to localize lung tumors and characterize their motion at the treatment table, the image contrast may be sufficient. We will also compare our approach to published 4DCT approaches to determine if ours better manages irregular breathing.