Abstract
PurposeTo incorporate the bilateral filtering into the Deformable Vector Field (DVF) based 4D-CBCT reconstruction for realizing a fully automatic sliding motion compensated 4D-CBCT.Materials and MethodsInitially, a motion compensated simultaneous algebraic reconstruction technique (mSART) is used to generate a high quality reference phase (e.g. 0% phase) by using all phase projections together with the initial 4D-DVFs. The initial 4D-DVF were generated via Demons registration between 0% phase and each other phase image. The 4D-DVF will then kept updating by matching the forward projection of the deformed high quality 0% phase with the measured projection of the target phase. The loss function during this optimization contains an projection intensity difference matching criterion plus a DVF smoothing constrain term. We introduce a bilateral filtering kernel into the DVF constrain term to estimate the sliding motion automatically. The bilateral filtering kernel contains three sub-kernels: 1) an spatial domain Guassian kernel; 2) an image intensity domain Guassian kernel; and 3) a DVF domain Guassian kernel. By choosing suitable kernel variances, the sliding motion can be extracted. A non-linear conjugate gradient optimizer was used. We validated the algorithm on a non-uniform rotational B-spline based cardiac-torso (NCAT) phantom and four anonymous patient data. For quantification, we used: 1) the Root-Mean-Square-Error (RMSE) together with the Maximum-Error (MaxE); 2) the Dice coefficient of the extracted lung contour from the final reconstructed images and 3) the relative reconstruction error (RE) to evaluate the algorithm's performance.ResultsFor NCAT phantom, the motion trajectory's RMSE/MaxE are 0.796/1.02 mm for bilateral filtering reconstruction; and 2.704/4.08 mm for original reconstruction. For patient pilot study, the 4D-Dice coefficient obtained with bilateral filtering are consistently higher than that without bilateral filtering. Meantime several image content such as the rib position, the heart edge definition, the fibrous structures all has been better corrected with bilateral filtering.ConclusionWe developed a bilateral filtering based fully automatic sliding motion compensated 4D-CBCT scheme. Both digital phantom and initial patient pilot studies confirmed the improved motion estimation and image reconstruction ability. It can be used as a 4D-CBCT image guidance tool for lung SBRT treatment.
Highlights
In image-guided radiation therapy (IGRT), 3D Cone Beam CT (CBCT) is extensively applied to check patient positioning before a radiation beam is delivered
The vein length has been corrected by these two algorithms compared with the FIGURE 10 | sv sensitivity analysis for texture smoothing. (A) Bilateral filtering based reconstruction with to 2 mm; (B) Bilateral filtering based reconstruction with to 3 mm; (C) Bilateral filtering based reconstruction with to 4 mm; (D) Bilateral filtering based reconstruction with to 5 mm; (E) Bilateral filtering based reconstruction without Deformable Vector Field (DVF) domain kernel; (F) Original simultaneous reconstruction; (G) Patient Reference; (H) Adjacent reference slice from patient reference; (I) Adjacent reference slice that has been smoothed
As the sliding motion mainly occurs at the interface between the lung and the chest wall, we only focus on this zoomed local region of interest to determine the DVF differences
Summary
In image-guided radiation therapy (IGRT), 3D Cone Beam CT (CBCT) is extensively applied to check patient positioning before a radiation beam is delivered. At the SBRT treatment stage for lung cancer cases, the patient usually will be performed with a 3D-CBCT to check positioning before SBRT beam on. The Deformable Vector Field (DVF)-based 4D-CBCT image reconstruction algorithm has shown an advantage for high-quality 4D-CBCT reconstruction [11,12,13,14]. Most of those methods assume the lung moves along an uniform path and ignored the lung's non-average local motion (e.g. sliding motion). Its clinical translation is hindered due to its ineluctable requirement of lung boundary half automatic segmentation
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