Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.
Department of Radiation Oncology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Med Phys. 2021 Nov;48(11):6832-6843. doi: 10.1002/mp.15299. Epub 2021 Oct 26.
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).
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).
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 to , and HU for adipose and from to , , 3 HU for muscle, respectively. After correction, the spatial non-uniformity (SNU) of CBCT corrected with MG-RR, MG-DR and LF-RR was , , and HU for adipose, and , , and 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.
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.
锥形束 CT（CBCT）已广泛应用于图像引导放疗中。计划 CT（pCT）辅助 CBCT 散射校正可以进一步提高图像质量，并将 CBCT 应用扩展到剂量计算和自适应计划中。然而，现有的基于 pCT 的方法需要在 pCT 和 CBCT 之间进行精确的配准，这导致在存在大的解剖差异时，成像性能有限且计算成本增加。在这项工作中，我们提出了一种使用局部滤波技术和 pCT 与 CBCT 之间的刚性配准（LF-RR）的稳健快速 CBCT 散射校正方法。
首先，将 pCT 与 CBCT 刚性配准，然后对配准后的 pCT 进行正向投影，以创建无散射的投影。通过从测量的 CBCT 投影中减去无散射的投影来获得原始散射信号。基于频率和强度阈值标准，从原始散射信号中选择可靠的散射信号，并通过局部滤波技术进一步进行全局散射估计滤波。最后，使用 FDK 算法通过从原始 CBCT 投影中减去散射估计值来生成投影，从而使用重建的散射校正后的 CBCT。通过与基于中值和高斯滤波器的另一种 pCT 散射校正方法（MG 方法）的比较来评估 LF-RR 方法。
该方法首先在人体骨盆模型上进行了验证，即使在 pCT 上故意创建解剖学不匹配的情况下，该方法也能很好地去除散射。还对 4 个临床 CBCT 图像进行了定量分析。与未经校正的 CBCT 相比，用刚性配准的 MG（MG-RR）、用变形配准的 MG（MG-DR）和 LF-RR 校正后的 CBCT 中脂肪的 CT 值误差从 减少到 、 ，肌肉的 CT 值误差从 减少到 、 、 ，分别为 HU。校正后，用 MG-RR、MG-DR 和 LF-RR 校正后的 CBCT 的空间不均匀性（SNU）分别为脂肪 、 、 ，肌肉 、 、 ，分别为 HU。同时，肌肉和脂肪之间的对比噪声比（CNR）分别提高了 2.70、2.89 和 2.56 倍。使用 LF-RR 方法，可在 10 秒内完成 656 个投影的散射校正，在 2 分钟内获得校正后的容积图像（200 个切片）。
我们开发了一种快速稳健的基于 pCT 的 CBCT 散射校正方法，该方法利用局部滤波技术提高了散射估计的准确性，并能抵抗 pCT 与 CBCT 配准的不确定性。体模和患者研究均表明，该方法在成像准确性和计算效率方面具有优势，表明其具有广阔的临床应用前景。
