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. To improve CBCT imaging for quantitative use, we recently proposed a correction method using planning CT (pCT) as the prior knowledge. Though showing promising phantom results, our method needs more clinical validations especially when patients have large organ deformations. In this abstract, we extend our method from bench to bedside by including several new components. Methods: Our algorithm estimates the primary signals of CBCT projections via forward projection on the pCT image, and then obtains the low‐frequency errors in CBCT raw projections by subtracting the estimated primaries 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 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 applied to reduce these effects. Results: The proposed method is evaluated on four prostate patients. Using the pCT image as the ground truth, the overall mean CT number error is reduced from over 300 to below 16 HU in the selected ROIs, and the spatial non‐uniformity 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. the NIH under the grant number 1R21EB012700‐01A1.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging