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

Abstract

Purpose Cone‐beam CT (CBCT) has been widely utilized in image‐guided radiotherapy. Planning CT (pCT)‐aided CBCT scatter correction could further enhance image quality and extend CBCT application to dose calculation and adaptive planning. Nevertheless, existing pCT‐based approaches demand accurate registration between pCT and CBCT, leading to limited imaging performance and increased computational cost when large anatomical discrepancies exist. In this work, we proposed a robust and fast CBCT scatter correction method using local filtration technique and rigid registration between pCT and CBCT (LF‐RR). Methods First of all, the pCT was rigidly registered with CBCT, then forward projection was performed on registered pCT to create scatter‐free projections. The raw scatter signals were obtained via subtracting the scatter‐free projections from the measured CBCT projections. Based on frequency and intensity threshold criteria, reliable scatter signals were selected from the raw scatter signals, and further filtered for global scatter estimation via local filtration technique. Finally, corrected CBCT was reconstructed with the projections generated by subtracting the scatter estimation from the raw CBCT projections using FDK algorithm. The LF‐RR method was evaluated via comparison with another pCT‐based scatter correction method based on Median and Gaussian filters (MG method). Results Proposed method was first validated on an anthropomorphic pelvis phantom, and showed satisfied performance on scatter removal even when anatomical mismatches were intentionally created on pCT. The quantitative analysis was further performed on four clinical CBCT images. Compared with the uncorrected CBCT, CBCT corrected by MG with rigid registration (MG‐RR), MG with deformable registration (MG‐DR), and LF‐RR reduced the CT number error from 79±35 to 25±18,17±13 and 7±3 HU for adipose and from 115±61 to 36±22,30±24, 7±3 HU for muscle, respectively. After correction, the spatial non‐uniformity (SNU) of CBCT corrected with MG‐RR, MG‐DR and LF‐RR was 51±13,60±21, and 21±9 HU for adipose, and 50±22,57±41, and 25±6 HU for muscle, respectively. Meanwhile, the contrast‐to‐noise ratio (CNR) between muscle and adipose was increased by a factor of 2.70, 2.89 and 2.56, respectively. Using the LF‐RR method, the scatter correction of 656 projections can be finished within 10 s and the corrected volumetric images (200 slices) can be obtained within 2 min. Conclusion We developed a fast and robust pCT‐based CBCT scatter correction method which exploits the local‐filtration technique to promote the accuracy of scatter estimation and is resistant to pCT‐to‐CBCT registration uncertainties. Both phantom and patient studies showed the superiority of the proposed correction in imaging accuracy and computational efficiency, indicating promisingfuture clinical application.

A practical and robust method for beam blocker-based cone beam CT scatter correction

“…The threshold for the scatter signal gradient, G , max is used to eliminate the relatively high frequency signal, and the threshold for the scatter intensity, S , max is to keep only the signal in the internal region. The value determination rules, which have been verified in our previous work (Cui et al 2021), were adapted in this work. G max value is such determined that the total pixels in [0 G max ] interval account for 60% of all pixels.…”

“…One way to model the CBCT scatter is to borrow the prior information from a patient's previous CT. This type of method performs well only when a decent registration between the current CBCT and the prior CT can be established , Shi et al 2017, Cui et al 2019, Cui et al 2021. Recent researches have shown the great potential of deep learning scatter correction methods, but their performance is heavily relied on the quality and quantity of the training data (Hansen et al 2018, Kida et al 2018, Maier et al 2018, Jiang et al 2019, Roser et al 2021, Maul et al 2022.…”

A correlated sampling‐based Monte Carlo simulation for fast CBCT iterative scatter correction

“…First, it is independent of prior information like planning CT, which can avoid the registration problems. Because the planning CT and CBCT are not acquired at the same time, it is difficult to conduct an accurate registration due to the day‐to‐day deformation of the patient's anatomy 40,41 . Second, efficient MC simulation is realized by introducing correlated sampling, making it suitable for clinical use.…”

“…Because the planning CT and CBCT are not acquired at the same time, it is difficult to conduct an accurate registration due to the day-to-day deformation of the patient's anatomy. 40,41 Second, efficient MC simulation is realized by introducing correlated sampling, making it suitable for clinical use. The proposed method was shown to improve the image quality in simulated studies, phantom, and clinical studies.…”

A scatter correction method of CBCT via CycleGAN and forward projection algorithm

“…Above scatter correction methods mainly via direction measurement or theoretical calculation to estimate scatter, considering the characteristic of existing planning CT in radiation therapy, there are also methods estimate scatter signals via planning CT forward projection and then subtracted it from raw projections. [12][13][14][15] Nui et al 12 via rigid registration of the planning CT with CBCT, and then forward projection process be used in planning CT to acquire primary projections, so the scatter signals can be estimated easily and be smoothed. To further decrease the estimated scatter errors, they also used some improvement technologies such as deformable registration, gas pocket matching in this method.…”