A Novel Method of Cone Beam CT Projection Binning Based on Image Registration

Abstract

Accurate sorting of beam projections is important in four-dimensional Cone Beam Computed Tomography (4D CBCT) to improve the quality of the reconstructed 4D CBCT image by removing motion-induced artifacts. We propose Image Registration-based Projection Binning (IRPB), a novel markerless binning method for 4D CBCT projections, which combines Intensity-based Feature Point Detection (IFPD) and Trajectory Tracking using Random sample consensus (TTR). IRPB extracts breathing motion and phases by analyzing tissue feature point trajectories. We conducted experiments with two phantom and six patient datasets including both regular and irregular respiration. In experiments, we compared the performance of the proposed IRPB, Amsterdam Shroud method (AS), Fourier Transform-based method (FT), and Local Intensity Feature Tracking (LIFT) method. The results showed that the average absolute phase shift of IRPB was 3.74 projections and 0.48 projections less than that of FT and LIFT, respectively. AS lost the most breathing cycles in the respiration extraction for the five patient datasets, so we could not compare the average absolute phase shift between IRPB and AS. Based on the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed 4D CBCT images, IRPB had 5.08 dB, 1.05 dB, and 2.90 dB larger PSNR than AS, FT, and LIFT, respectively. The average Structure SIMilarity Index (SSIM) of the 4D CBCT image reconstructed by IRPB, AS, and LIFT were 0.87, 0.74, 0.84, and 0.70, respectively. These results demonstrated that IRPB has superior performance to the other standard methods.

Planning CT‐guided robust and fast cone‐beam CT scatter correction using a local filtration technique

“…This mismatch is even more severe when only rigid registration used and would introduce locally over‐ or under‐estimated scatter. Deformable registration 37–39 may alleviate this error but shows limited performance when large anatomical changes take place between CT and CBCT images.…”

“…This mismatch is even more severe when only rigid registration used and would introduce locally overor under-estimated scatter. Deformable registration [37][38][39] may alleviate this error but shows limited performance when large anatomical changes take place between CT and CBCT images. Rather than improving the registration accuracy, we aim to promote the accuracy of scatter estimation when only rigid registration is performed between pCT and CBCT.…”

Low-dose cone-beam CT (LD-CBCT) reconstruction for image-guided radiation therapy (IGRT) by three-dimensional dual-dictionary learning

“…For objective evaluation, quantitative metrics peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were applied to our experiment results for numerical comparisons between SART-DDL and SART-TV [ 19 ], which was typical iterative reconstruction algorithm. The PSNR and SSIM are commonly used standard metrics for quantitative analysis in CBCT reconstruction previously [ 20 , 21 ]. In our study, the reference images were F-CBCT sets, which are widely adopted in present clinical applications and regarded as the basic standard.…”

Fluoroscopic 3D Image Generation from Patient-Specific PCA Motion Models Derived from 4D-CBCT Patient Datasets: A Feasibility Study

“…Several methods have been used to estimate respiratory motion corresponding to the raw CBCT projections. These methods include using external equipment, such as external markers or abdominal belts, internal implanted radiopaque fiducial markers, or marker-free pure image-based approaches [20][21][22][23][24][25][26][27]. On-board 4D-CBCT images are produced at the day of treatment delivery while the patient is in treatment position.…”