Quantitative cone‐beam CT imaging in radiation therapy using planning CT as a prior: First patient studies

Abstract

Purpose: Quantitative cone-beam CT (CBCT) imaging is on increasing demand for high-performance image guided radiation therapy (IGRT). However, the current CBCT has poor image qualities mainly due to scatter contamination. Its current clinical application is therefore limited to patient setup based on only bony structures. To improve CBCT imaging for quantitative use, we recently proposed a correction method using planning CT (pCT) as the prior knowledge. Promising phantom results have been obtained on a tabletop CBCT system, using a correction scheme with rigid registration and without iterations. More challenges arise in clinical implementations of our method, especially because patients have large organ deformation in different scans. In this paper, we propose an improved framework to extend our method from bench to bedside by including several new components. Methods: The basic principle of our correction algorithm is to estimate the primary signals of CBCT projections via forward projection on the pCT image, and then to obtain the low-frequency errors in CBCT raw projections by subtracting the estimated primary signals and low-pass filtering. We improve the algorithm by using deformable registration to minimize the geometry difference between the pCT and the CBCT images. Since the registration performance relies on the accuracy of the CBCT image, we design an optional iterative scheme to update the CBCT image used in the registration. Large correction errors result from the mismatched objects in the pCT and the CBCT scans. Another optional step of gas pocket and couch matching is added into the framework to reduce these effects. Results: The proposed method is evaluated on four prostate patients, of which two cases are presented in detail to investigate the method performance for a large variety of patient geometry in clinical practice. The first patient has small anatomical changes from the planning to the treatment room. Our algorithm works well even without the optional iterations and the gas pocket and couch matching. The image correction on the second patient is more challenging due to the effects of gas pockets and attenuating couch. The improved framework with all new components is used to fully evaluate the correction performance. The enhanced image quality has been evaluated using mean CT number and spatial nonuniformity (SNU) error as well as contrast improvement factor. If the pCT image is considered as the ground truth, on the four patients, the overall mean CT number error is reduced from over 300 HU to below 16 HU in the selected regions of interest (ROIs), and the SNU error is suppressed from over 18% to below 2%. The average soft-tissue contrast is improved by an average factor of 2.6. Conclusions: We further improve our pCT-based CBCT correction algorithm for clinical use. Superior correction performance has been demonstrated on four patient studies. By providing quantitative CBCT images, our approach significantly increases the accuracy of advanced CBCT-based clinical applications for IGRT....

Proton dose calculation on scatter‐corrected CBCT image: Feasibility study for adaptive proton therapy

“…Similar validation studies had been already performed for photon dose calculation. 18,20 However, we believe it necessary for this to be independently conducted since the residual HU errors after the scatter correction would have different effects on proton dose calculations compared to photon cases. Moreover, this validation cannot be done in real patient images, as there would be no ground truth of CT ref to compare with daily CBCT images unless CT and CBCT images are acquired at the same patient setup as pointed out by Landry et al 12 Recently, Landry et al 12,13 investigated the feasibility of proton dose calculations on a virtual CT image using deformable image registration between plan CT and daily CBCT images.…”

“…Unlike the simple uniform scatter correction, a priori CTbased correction involves some preprocessing techniques such 20 we also validated them for some complex cases in our experimental framework. This validation aimed to prove that the CBCT ap is still feasible when there are obvious anatomical changes at some time points between CT and CBCT scans.…”

“…For 20 was applied to account for highcontrast geometry mismatching in the rectal cavities. Briefly, the procedure consists of the following steps: cavity segmentation using intensity threshold for both CT ref and CBCT raw , filling cavities with surrounding soft-tissue intensity values, DIR, and creating new cavities on the deformed CT using the segmentation mask from CBCT raw .…”

Image quality and absorbed dose comparison of single‐ and dual‐source cone‐beam computed tomography

“…Figure 7 shows axial images of the pelvic region obtained with SCBCT and DCBCT. A CBCT correction algorithm for clinical use is necessary to improve the DCBCT image quality 21. We will further investigate the performance of DCBCT in other imaging regions (e.g., thorax and abdomen).…”

Accelerated barrier optimization compressed sensing (ABOCS) reconstruction for cone-beam CT: Phantom studies

“…Note that, the user-defined parameter ε is interpreted as the variance of the difference between the predicted and the raw projections. After effective data correction for scatter and beam-hardening effects, [11][12][13][14][15][16][17][18] most of the projection errors in CT scans are from Poisson statistics of the incident photons, except for very lowdose imaging cases. [19][20][21] ε can therefore be readily estimated from the measured projections.…”