A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.
A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.
复制标题
一种深度学习方法，用于从对比增强 CT 中去除对比度以进行质子剂量计算。
DOI：
10.1002/acm2.14266
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发表时间：
2024
影响因子：
2.1
通讯作者：
Yang,Deshan
中科院分区：
文献类型：
--
作者：
Wang,Xu;Hao,Yao;Duan,Ye;Yang,Deshan
目的质子剂量计算通常需要非对比增强CT(NCECT)，而增强CT(CECT)常用于肿瘤和器官的显示。这两个CT之间可能的组织运动增加了剂量学的不确定性，特别是对于胸部和腹部移动的肿瘤。在这里，我们报告了一种深度学习方法，可以直接从CECT生成NCECT。该方法避免了NCECT扫描，减少了CT模拟时间和成像剂量，减少了不同CT扫描之间因组织运动引起的不确定性。该网络接收来自CECT图像的3D图像作为输入，并生成相应的去除对比度的NCECT图像块。对20例患者的腹部CECT和NCECT图像对进行形变配准，并用从配准图像对中提取的8000个图像块对对模型进行训练和测试。使用临床质子患者的CT和他们的治疗计划来评估使用生成的NCECT进行质子剂量计算的剂量学影响。结果我们的方法获得的余弦相似分数为0.988，MSE值为0.002。对5名质子患者在CECT和NCECT上计算的临床质子剂量计划进行了定量比较，发现在束流路径的远端有显著的剂量差异。PTV和GTV的V100%分别变化3.5%和5.5%。生成的和扫描的NCECT之间的平均HU差值为∼4.72，而CECT和扫描的NCECT之间的差值为∼64.52，平均HU差值减少了∼93%。这种方法可用于质子剂量计算，以减少组织运动在CECT和NCECT之间造成的不确定度。
PurposeNon‐Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties, especially for moving tumors in the thorax and abdomen. Here we report a deep‐learning approach to generate NCECT directly from CECT. This method could be useful to avoid the NCECT scan, reduce CT simulation time and imaging dose, and decrease the uncertainties caused by tissue motion between otherwise two different CT scans.MethodsA deep network was developed to convert CECT to NCECT. The network receives a 3D image from CECT images as input and generates a corresponding contrast‐removed NCECT image patch. Abdominal CECT and NCECT image pairs of 20 patients were deformably registered and 8000 image patch pairs extracted from the registered image pairs were utilized to train and test the model. CTs of clinical proton patients and their treatment plans were employed to evaluate the dosimetric impact of using the generated NCECT for proton dose calculation.ResultsOur approach achieved a Cosine Similarity score of 0.988 and an MSE value of 0.002. A quantitative comparison of clinical proton dose plans computed on the CECT and the generated NCECT for five proton patients revealed significant dose differences at the distal of beam paths. V100% of PTV and GTV changed by 3.5% and 5.5%, respectively. The mean HU difference for all five patients between the generated and the scanned NCECTs was ∼4.72, whereas the difference between CECT and the scanned NCECT was ∼64.52, indicating a ∼93% reduction in mean HU difference.ConclusionsA deep learning approach was developed to generate NCECTs from CECTs. This approach could be useful for the proton dose calculation to reduce uncertainties caused by tissue motion between CECT and NCECT.