Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Phys Med Biol. 2020 Apr 28;65(9):095002. doi: 10.1088/1361-6560/ab7d54.
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.
在室成像（In-room imaging）是自适应质子治疗的前提。为了进行患者位置验证，常进行机载锥形束计算机断层扫描（CBCT）成像，这可以实现每日剂量重建和计划自适应决策。然而，图像质量的不足会影响剂量计算的准确性，因此有必要对 CBCT 进行校正。本研究比较了三种校正 CBCT 并生成适用于质子剂量计算的合成 CT（sCT）的方法。使用来自 33 名头颈部癌症患者的 CBCT、计划 CT 和重复 CT（rCT）来比较深度学习卷积神经网络（DCNN）、形变图像配准（DIR）和基于解析图像的校正方法（AIC）生成 sCT 的效果。通过与同一天的 rCT 进行比较，使用平均绝对误差（MAE）、平均误差（ME）、Dice 相似系数（DSC）、结构不均匀性（SNU）和信号/对比噪声比（SNR/CNR）作为指标来评估 sCT 的图像质量。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 产生的 MAE 和 ME 最低（分别为 37/2 HU 和-8/1 HU），DSC 最高（0.96）。而 DIR 和 AIC 生成的图像 MAE 为 44/77 HU，ME 为-8/1 HU，DSC 为 0.94/0.90。伽马和范围偏移分析表明，基于 DCNN 和 DIR 的 sCT 之间几乎没有剂量学差异。AIC 生成的 sCT 图像质量较低，影响了剂量学准确性，导致通过率降低和范围偏移增加。患者特异性差异突出了每种方法的优缺点。对于这组患者，基于 DCNN 的方法生成的 sCT 具有最高的图像质量。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 方法导致的图像质量较低，剂量计算准确性降低。
