Patient-specific synthetic magnetic resonance imaging generation from cone beam computed tomography for image guidance in liver stereotactic body radiation therapy.
Patient-specific synthetic magnetic resonance imaging generation from cone beam computed tomography for image guidance in liver stereotactic body radiation therapy.
复制标题
患者特异性的合成磁共振成像从锥形束计算机断层扫描中产生，用于肝脏立体定向身体放射疗法的图像引导。
DOI：
10.1002/pro6.1163
复制
发表时间：
2022-06
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
尽管圆锥束计算机断层扫描（CBCT）很流行，但它的软组织对比效果很差，这给肝脏肿瘤的定位带来了挑战。我们提出了一种针对患者的深度学习模型，从CBCT生成合成磁共振成像（MRI），以提高肿瘤定位。一个关键的创新是使用患者特定的CBCT-MRI图像对来训练深度学习模型，以从CBCT生成合成MRI。具体而言，将患者计划CT变形注册到先前的MRI上，然后使用模拟投影和Feldkamp， Davis和Kress重建来模拟CBCT。这些CBCT-MRI图像使用平移和旋转增强，以生成足够的患者特定训练数据。开发并训练了基于u - net的深度学习模型，以从肝脏中的CBCT生成合成MRI，然后在不同的CBCT数据集上进行测试。合成MRI与真实MRI进行定量评估。合成MRI显示极好的软组织对比，肿瘤清晰可见。合成MRI的峰值信噪比、均方误差和结构相似度指数平均分别达到28.01、0.025和0.929，优于CBCT图像。模型的性能在所有三名患者中都是一致的。我们的研究证明了一种患者特异性模型的可行性，该模型可以从CBCT中生成用于肝肿瘤定位的合成MRI，从而为传统LINACs临床的MRI指导提供了可能。
Despite its prevalence, cone beam computed tomography (CBCT) has poor soft-tissue contrast, making it challenging to localize liver tumors. We propose a patient-specific deep learning model to generate synthetic magnetic resonance imaging (MRI) from CBCT to improve tumor localization. A key innovation is using patient-specific CBCT-MRI image pairs to train a deep learning model to generate synthetic MRI from CBCT. Specifically, patient planning CT was deformably registered to prior MRI, and then used to simulate CBCT with simulated projections and Feldkamp, Davis, and Kress reconstruction. These CBCT-MRI images were augmented using translations and rotations to generate enough patient-specific training data. A U-Net-based deep learning model was developed and trained to generate synthetic MRI from CBCT in the liver, and then tested on a different CBCT dataset. Synthetic MRIs were quantitatively evaluated against ground-truth MRI. The synthetic MRI demonstrated superb soft-tissue contrast with clear tumor visualization. On average, the synthetic MRI achieved 28.01, 0.025, and 0.929 for peak signal-to-noise ratio, mean square error, and structural similarity index, respectively, outperforming CBCT images. The model performance was consistent across all three patients tested. Our study demonstrated the feasibility of a patient-specific model to generate synthetic MRI from CBCT for liver tumor localization, opening up a potential to democratize MRI guidance in clinics with conventional LINACs.