Larsson Karin, Hein Dennis, Huang Ruihan, Collin Daniel, Scotti Andrea, Fredenberg Erik, Andersson Jonas, Persson Mats
KTH Royal Institute of Technology, Department of Physics, Stockholm, Sweden.
Karolinska University Hospital, MedTechLabs, BioClinicum, Solna, Sweden.
J Med Imaging (Bellingham). 2024 Dec;11(Suppl 1):S12809. doi: 10.1117/1.JMI.11.S1.S12809. Epub 2024 Nov 20.
Proton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise. We assessed the potential of photon-counting computed tomography (PCCT) for improving SPR estimation by training a deep neural network on a domain transform from PCCT images to SPR maps.
The XCAT phantom was used to simulate PCCT images of the head with CatSim, as well as to compute corresponding ground truth SPR maps. The tube current was set to 260 mA, tube voltage to 120 kV, and number of view angles to 4000. The CT images and SPR maps were used as input and labels for training a U-Net.
Prediction of SPR with the network yielded average root mean square errors (RMSE) of 0.26% to 0.41%, which was an improvement on the RMSE for methods based on physical modeling developed for single-energy CT at 0.40% to 1.30% and dual-energy CT at 0.41% to 3.00%, performed on the simulated PCCT data.
These early results show promise for using a combination of PCCT and deep learning for estimating SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.
由于质子具有独特的布拉格峰，质子放射治疗能够在保护周围非癌组织的同时，实现对肿瘤的精确剂量输送。将高剂量区域与肿瘤对齐需要准确估计患者组织的质子阻止本领比（SPR），通常从计算机断层扫描（CT）图像数据中得出。CT的光子计数探测器已显示出优于能量积分探测器的优势，例如改进的定量成像、更高的空间分辨率以及电子噪声过滤。我们通过在从PCCT图像到SPR图的域变换上训练深度神经网络，评估了光子计数计算机断层扫描（PCCT）在改善SPR估计方面的潜力。
使用XCAT体模通过CatSim模拟头部的PCCT图像，并计算相应的真实SPR图。管电流设置为260 mA，管电压设置为120 kV，视角数量设置为4000。CT图像和SPR图用作训练U-Net的输入和标签。
用该网络预测SPR产生的平均均方根误差（RMSE）为0.26%至0.41%，这优于基于为单能CT开发的物理模型的方法（RMSE为0.40%至1.30%）以及双能CT（RMSE为0.41%至3.00%），这些方法是在模拟的PCCT数据上执行的。
这些早期结果表明，将PCCT和深度学习结合用于估计SPR具有前景，进而证明了在质子治疗中降低射束范围不确定性的潜力。
