Optimal combination of anti‐scatter grids and software correction for CBCT imaging

Purpose Cone beam computed tomography ( CBCT ) has been widely adopted in clinical practice for image‐guided radiotherapy. Soft tissue contrast and Hounsfield units are impaired to the presence of scattered radiation. In our previous work, we proposed a high selectivity anti‐scatter grid ( ASG ) as a possible solution to the problem. An alternative approach is the application of iterative scatter correction using deconvolution with scatter point spread function ( PSF ). The purpose of this work was to compare the performance of ASG s with different selectivity with and without the iterative and uniform scatter corrections in terms of CBCT image quality. A secondary objective of this study was to develop a novel measurement approach to measure the scatter point spread functions. Methods The scatter PSF was modeled as a sum of two bivariate Gaussian functions. The PSF parameters were estimated from a series of transmission measurements through polystyrene slabs of varying thickness with lead partial beam‐blocker for three different ASG designs ranging from low (5.6), medium (9), and high (11) selectivity. The scatter correction scheme is based on iterative convolution of the current estimate of the primary with the scatter PSF until the root mean square deviation ( RMSD ) of the measured projection and the sum of the estimate of primary and scatter falls below a predefined threshold. The image quality was evaluated with the CIRS CBCT Image Quality and Electron Density phantom in a head and neck and pelvis configuration and the CIRS Virtual Male Human Patient. The image quality was quantified by the contrast‐to‐noise ratio ( CNR ) relative to the uncorrected scans and the root mean square deviation of the average gray values for different regions with respect to the nominal Hounsfield units and the mean difference of the reconstructed HU between the planning CT and CBCT s of the virtual human phantom. Results For the head and neck phantom, the CNR increased with more advanced scatter correction algorithm and the ASG selectivity, reaching 3.9, 3.7, 3.5, and 3.1 for the high, medium, light, and with no grid configuration, respectively, combined with the iterative software correction. The same is true for the pelvis phantom with CNR improvement reaching 1.5 for the heavy and medium grid, 1.3 for the light grid, and 1.1 on its own. The HU RMSD for the head and neck phantom was 22 HU , 13 HU , 12 HU , and 6 HU for iterative correction without the grid, with the light grid, medium grid and the heavy grid, respectively. For same correction strategies, the values for the pelvis phantom where 170, 120, 34, and 27 HU . The average difference with the PCT of the virtual human phantom was 59 ± 48 HU and 63 ± 59 HU with scans reconstructed with the iterative correction and two higher selectivity grids. Visual inspection revealed similar trends for a head‐and‐neck and prostate cancer patient. Conclusions The best scatter mitigation strategy was found to be a combination of a grid with selectivity larger than 9, combined with iterative scatter estimation. None of the investigated grids required increasing the imaging dose. The PSF determined using proposed method leads to image quality improvements results for all but one of the investigated scenarios.