In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper dose calculation accuracy and make corrections of CBCTs a necessity. This study compared three methods to correct CBCTs and create synthetic CTs that are suitable for proton dose calculations. CBCTs, planning CTs and repeated CTs (rCT) from 33 H&N cancer patients were used to compare a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction method (AIC) for synthetic CT (sCT) generation. Image quality of sCTs was evaluated by comparison with a same-day rCT, using mean absolute error (MAE), mean error (ME), Dice similarity coefficient (DSC), structural non-uniformity (SNU) and signal/contrast-to-noise ratios (SNR/CNR) as metrics. Dosimetric accuracy was investigated in an intracranial setting by performing gamma analysis and calculating range shifts. Neural network-based sCTs resulted in the lowest MAE and ME (37/2 HU) and the highest DSC (0.96). While DIR and AIC generated images with a MAE of 44/77 HU, a ME of -8/1 HU and a DSC of 0.94/0.90. Gamma and range shift analysis showed almost no dosimetric difference between DCNN and DIR based sCTs. The lower image quality of AIC based sCTs affected dosimetric accuracy and resulted in lower pass ratios and higher range shifts. Patient-specific differences highlighted the advantages and disadvantages of each method. For the set of patients, the DCNN created synthetic CTs with the highest image quality. Accurate proton dose calculations were achieved by both DCNN and DIR based sCTs. The AIC method resulted in lower image quality and dose calculation accuracy was reduced compared to the other methods.

中文翻译：

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比较适用于质子治疗中质子剂量计算的基于CBCT的合成CT方法。

室内成像是自适应质子治疗的前提。常规使用的机载锥形束计算机断层扫描（CBCT）成像可用于患者位置验证，可以实现每日剂量重建和计划适应性决策。但是，图像质量不足会影响剂量计算的准确性，并且必须校正CBCT。这项研究比较了三种校正CBCT和创建适合质子剂量计算的合成CT的方法。使用33名H＆N癌症患者的CBCT，计划的CT和重复CT（rCT）比较深层卷积神经网络（DCNN），可变形图像配准（DIR）和基于分析图像的合成CT（sCT）校正方法（ACT） ）代。sCT的图像质量是通过与当日rCT进行比较来评估的，使用平均绝对误差（MAE），平均误差（ME），骰子相似系数（DSC），结构不均匀度（SNU）和信噪比/噪声比（SNR / CNR）作为度量。通过执行伽马分析和计算范围偏移，在颅内环境中研究了剂量学准确性。基于神经网络的sCT导致最低的MAE和ME（37/2 HU）和最高的DSC（0.96）。DIR和AIC生成的图像的MAE为44/77 HU，ME为-8/1 HU，DSC为0.94 / 0.90。伽马和距离平移分析显示，基于DCNN和DIR的sCT之间几乎没有剂量学差异。基于AIC的sCT的较低图像质量会影响剂量精度，并导致较低的通过率和较大的范围偏移。特定于患者的差异突出了每种方法的优缺点。对于这组患者，DCNN创建了具有最高图像质量的合成CT。通过基于DCNN和DIR的sCT均可实现准确的质子剂量计算。与其他方法相比，AIC方法导致图像质量降低，并且剂量计算精度降低。