Bootsma G J, Verhaegen F, Jaffray D A
Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada.
Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht 6201 BN, The Netherthlands and Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec H3G 1A4, Canada.
Med Phys. 2015 Jan;42(1):54-68. doi: 10.1118/1.4903260.
X-ray scatter is a significant impediment to image quality improvements in cone-beam CT (CBCT). The authors present and demonstrate a novel scatter correction algorithm using a scatter estimation method that simultaneously combines multiple Monte Carlo (MC) CBCT simulations through the use of a concurrently evaluated fitting function, referred to as concurrent MC fitting (CMCF).
The CMCF method uses concurrently run MC CBCT scatter projection simulations that are a subset of the projection angles used in the projection set, P, to be corrected. The scattered photons reaching the detector in each MC simulation are simultaneously aggregated by an algorithm which computes the scatter detector response, SMC. SMC is fit to a function, SF, and if the fit of SF is within a specified goodness of fit (GOF), the simulations are terminated. The fit, SF, is then used to interpolate the scatter distribution over all pixel locations for every projection angle in the set P. The CMCF algorithm was tested using a frequency limited sum of sines and cosines as the fitting function on both simulated and measured data. The simulated data consisted of an anthropomorphic head and a pelvis phantom created from CT data, simulated with and without the use of a compensator. The measured data were a pelvis scan of a phantom and patient taken on an Elekta Synergy platform. The simulated data were used to evaluate various GOF metrics as well as determine a suitable fitness value. The simulated data were also used to quantitatively evaluate the image quality improvements provided by the CMCF method. A qualitative analysis was performed on the measured data by comparing the CMCF scatter corrected reconstruction to the original uncorrected and corrected by a constant scatter correction reconstruction, as well as a reconstruction created using a set of projections taken with a small cone angle.
Pearson's correlation, r, proved to be a suitable GOF metric with strong correlation with the actual error of the scatter fit, SF. Fitting the scatter distribution to a limited sum of sine and cosine functions using a low-pass filtered fast Fourier transform provided a computationally efficient and accurate fit. The CMCF algorithm reduces the number of photon histories required by over four orders of magnitude. The simulated experiments showed that using a compensator reduced the computational time by a factor between 1.5 and 1.75. The scatter estimates for the simulated and measured data were computed between 35-93 s and 114-122 s, respectively, using 16 Intel Xeon cores (3.0 GHz). The CMCF scatter correction improved the contrast-to-noise ratio by 10%-50% and reduced the reconstruction error to under 3% for the simulated phantoms.
The novel CMCF algorithm significantly reduces the computation time required to estimate the scatter distribution by reducing the statistical noise in the MC scatter estimate and limiting the number of projection angles that must be simulated. Using the scatter estimate provided by the CMCF algorithm to correct both simulated and real projection data showed improved reconstruction image quality.
在锥束CT（CBCT）中，X射线散射是提高图像质量的一个重大障碍。作者提出并演示了一种新颖的散射校正算法，该算法使用一种散射估计方法，通过使用同时评估的拟合函数（称为并发蒙特卡罗拟合（CMCF）），同时结合多个蒙特卡罗（MC）CBCT模拟。
CMCF方法使用同时运行的MC CBCT散射投影模拟，这些模拟是要校正的投影集P中使用的投影角度的一个子集。在每个MC模拟中到达探测器的散射光子由一种算法同时汇总，该算法计算散射探测器响应SMC。SMC拟合到一个函数SF，如果SF的拟合在指定的拟合优度（GOF）内，则模拟终止。然后，拟合函数SF用于在集合P中的每个投影角度的所有像素位置上插值散射分布。使用频率受限的正弦和余弦之和作为拟合函数，在模拟数据和测量数据上对CMCF算法进行了测试。模拟数据包括一个从CT数据创建的人体头部和骨盆模型，分别在使用和不使用补偿器的情况下进行模拟。测量数据是在Elekta Synergy平台上对一个模型和患者进行的骨盆扫描。模拟数据用于评估各种GOF指标以及确定合适的拟合值。模拟数据还用于定量评估CMCF方法提供的图像质量改善。通过将CMCF散射校正重建与原始未校正、通过常数散射校正重建校正的以及使用小锥角拍摄的一组投影创建的重建进行比较，对测量数据进行了定性分析。
Pearson相关性r被证明是一个合适的GOF指标，与散射拟合SF的实际误差有很强的相关性。使用低通滤波快速傅里叶变换将散射分布拟合到有限的正弦和余弦函数之和，提供了一种计算效率高且准确的拟合。CMCF算法将所需的光子历史数量减少了四个数量级以上。模拟实验表明，使用补偿器可将计算时间减少1.5至1.75倍。使用16个英特尔至强核心（3.0 GHz），分别在35 - 93秒和114 - 122秒之间计算了模拟数据和测量数据的散射估计。CMCF散射校正将模拟模型的对比度噪声比提高了10% - 50%，并将重建误差降低到3%以下。
新颖的CMCF算法通过减少MC散射估计中的统计噪声并限制必须模拟的投影角度数量，显著减少了估计散射分布所需的计算时间。使用CMCF算法提供的散射估计来校正模拟和实际投影数据，显示出重建图像质量得到了改善。
