Synthetic CT Generation Based on T2 Weighted MRI of Nasopharyngeal Carcinoma (NPC) Using a Deep Convolutional Neural Network (DCNN)
Synthetic CT Generation Based on T2 Weighted MRI of Nasopharyngeal Carcinoma (NPC) Using a Deep Convolutional Neural Network (DCNN)
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DOI：
10.3389/fonc.2019.01333
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发表时间：
2019-11-29
影响因子：
4.7
通讯作者：
Deng, Weiwei
中科院分区：
文献类型：
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作者：
Wang, Yuenan;Liu, Chenbin;Deng, Weiwei
目的：将磁共振成像(MRI)应用于放射治疗(RT)，由于其优越的软组织对比度，可以准确地描绘靶点以及评估治疗反应的功能信息，因此引起了人们的兴趣。在鼻咽癌的放射治疗中，基于MRI的放射治疗计划具有很大的潜力，可以在减少对周围正常组织毒性的同时增加肿瘤的剂量。我们的研究旨在使用深度学习算法从T2加权MRI生成合成CT。方法：对33例鼻咽癌患者进行回顾性研究，并获得当地IRB批准。所有患者均在同一周内进行了临床CT模拟检查和1.5T MRI检查。在CT/MRI图像配准之前，我们必须使用直方图匹配方法将两种不同的模式归一化到相似的强度等级。然后使用基于强度的配准工具箱lasitix(版本4.9)对CT和T2加权MRI进行刚性和变形配准。以23例鼻咽癌患者的图像为训练集，提出了一种具有23层卷积的U网深度学习算法来生成合成CT(SCT)。其余10例鼻咽癌患者作为测试集(占全部数据集的1/3)。计算平均绝对误差(MAE)和平均误差(ME)，以评价真实CT和SCT在骨、软组织和全身的HU差异。结果：提出的U-Net算法能够在鼻咽癌患者的T2加权磁共振基础上建立SCT，平均每个患者需要7个S。与真实CT相比，所有受检者SCT的MAE软组织为97+/-13HU，全身为131+/-24HU，骨为357+/-44HU。软组织为-48+/-10HU，全身为-6+/-13HU，骨为247+/-44HU。除鼻腔内软组织与骨界面处及部分细微结构因变形配准不完善导致重建不准确外，其余大部分软组织和骨区均得到准确重建。1例患者在具有细微骨结构的鼻窦区的PTV区域使用真CT和SCT显示的剂量分布几乎没有差异。结论：在鼻咽癌患者中使用深度学习算法生成高质量的基于T2加权MRI的SCT图像是可行的，这可能对未来仅使用MRI的治疗计划具有很大的临床潜力。
Purpose: There is an emerging interest of applying magnetic resonance imaging (MRI) to radiotherapy (RT) due to its superior soft tissue contrast for accurate target delineation as well as functional information for evaluating treatment response. MRI-based RT planning has great potential to enable dose escalation to tumors while reducing toxicities to surrounding normal tissues in RT treatments of nasopharyngeal carcinoma (NPC). Our study aims to generate synthetic CT from T2-weighted MRI using a deep learning algorithm. Methods: Thirty-three NPC patients were retrospectively selected for this study with local IRB's approval. All patients underwent clinical CT simulation and 1.5T MRI within the same week in our hospital. Prior to CT/MRI image registration, we had to normalize two different modalities to a similar intensity scale using the histogram matching method. Then CT and T2 weighted MRI were rigidly and deformably registered using intensity-based registration toolbox elastix (version 4.9). A U-net deep learning algorithm with 23 convolutional layers was developed to generate synthetic CT (sCT) using 23 NPC patients' images as the training set. The rest 10 NPC patients were used as the test set (1/3 of all datasets). Mean absolute error (MAE) and mean error (ME) were calculated to evaluate HU differences between true CT and sCT in bone, soft tissue and overall region. Results: The proposed U-net algorithm was able to create sCT based on T2-weighted MRI in NPC patients, which took 7 s per patient on average. Compared to true CT, MAE of sCT in all tested patients was 97 +/- 13 Hounsfield Unit (HU) in soft tissue, 131 +/- 24 HU in overall region, and 357 +/- 44 HU in bone, respectively. ME was -48 +/- 10 HU in soft tissue, -6 +/- 13 HU in overall region, and 247 +/- 44 HU in bone, respectively. The majority soft tissue and bone region was reconstructed accurately except the interface between soft tissue and bone and some delicate structures in nasal cavity, where the inaccuracy was induced by imperfect deformable registration. One patient example was shown with almost no difference in dose distribution using true CT vs. sCT in the PTV regions in the sinus area with fine bone structures. Conclusion: Our study indicates that it is feasible to generate high quality sCT images based on T2-weighted MRI using the deep learning algorithm in patients with nasopharyngeal carcinoma, which may have great clinical potential for MRI-only treatment planning in the future.