Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.
Reconstruction of three-dimensional tomographic patient models for radiation dose modulation in CT from two scout views using deep learning.
复制标题
使用深度学习从两种侦察观点中重建了CT中的三维层析成像患者模型。
作者：
在X射线计算机层析成像(CT)中，断层病人模型是辐射剂量调制的关键。目前，双视侦察图像(也称为地形图)被用来估计具有相对均匀衰减系数的患者模型。这些患者模型没有考虑到人类受试者的详细解剖变化，因此可能会限制新兴CT技术中视野内或器官特定剂量调制的准确性。这项工作的目的是展示3D断层扫描患者模型可以使用深度学习策略从两个视角的童子军图像生成，并且重建的3D患者模型确实能够在随后的CT扫描中准确地开出注量场调制的或特定于器官的剂量传递处方。回顾性收集了4214例CT检查的CT图像和相应的双视图球探图像。收集的数据用于训练称为ScoutCT-Net的深度神经网络结构，以从两个视角的侦察图像生成3D断层扫描衰减模型。使用来自212名患者的55,136张图像的队列对训练后的网络进行了验证。为了评估重建的3D患者模型的准确性，使用ScoutCT-Net 3D患者模型生成放射传输计划，并将其与基于真实CT图像(金标准)的计划进行比较，以用于通量场调制CT和器官特异性CT。用蒙特卡罗模拟估算了辐射剂量分布，并用伽玛分析方法进行了定量估算。将调制的剂量分布与最先进的管电流调制方案进行了比较。还比较了基于ScoutCT-Net患者模型的剂量调制方案在总体图像外观、噪声大小和噪声均匀度方面对通用CT采集和特定器官采集的影响。结果表明：(1)端到端训练的ScoutCT-Net可以用于生成3D患者衰减模型，并具有经验泛化能力。2)在实际CT采集前，3D患者模型可用于准确估计标准螺旋CT辐射剂量的空间分布；与金标准剂量分布相比，基于ScoutCT-Net的剂量图中95.0%的体素具有可接受的伽玛值，其距离为5 mm，剂量差为10%。3)与管电流调制CT相比，3D患者模型还能够精确地处方通量场调制CT，从而在患者全身产生更均匀的噪声分布。4)ScoutCT-Net 3D患者模型能够在给定的辐射剂量约束下，精确地规定特定器官的CT，以提高给定身体感兴趣区域的图像质量。ScoutCT-Net从两个视角的侦察图像生成的3D断层扫描衰减模型可用于为特定成像任务提供高精度的注量场调制CT扫描或特定器官CT扫描，以实现减少辐射剂量或改善图像质量的总体目标。
A tomographic patient model is essential for radiation dose modulation in x-ray computed tomography (CT). Currently, two-view scout images (also known as topograms) are used to estimate patient models with relatively uniform attenuation coefficients. These patient models do not account for the detailed anatomical variations of human subjects, and thus may limit the accuracy of intra-view or organ-specific dose modulations in emerging CT technologies. The purpose of this work was to show that 3D tomographic patient models can be generated from two-view scout images using deep learning strategies, and the reconstructed 3D patient models indeed enable accurate prescriptions of fluence-field modulated or organ-specific dose delivery in the subsequent CT scans. CT images and the corresponding two-view scout images were retrospectively collected from 4,214 individual CT exams. The collected data were curated for the training of a deep neural network architecture termed ScoutCT-NET to generate 3D tomographic attenuation models from two-view scout images. The trained network was validated using a cohort of 55,136 images from 212 individual patients. To evaluate the accuracy of the reconstructed 3D patient models, radiation delivery plans were generated using ScoutCT-NET 3D patient models and compared with plans prescribed based on true CT images (gold-standard) for both fluence-field modulated CT and organ-specific CT. Radiation dose distributions were estimated using Monte Carlo simulations and were quantitatively evaluated using the Gamma analysis method. Modulated dose profiles were compared against state-of-the-art tube current modulation schemes. Impacts of ScoutCT-NET patient model-based dose modulation schemes on universal-purpose CT acquisitions and organ-specific acquisitions were also compared in terms of overall image appearance, noise magnitude, and noise uniformity. The results demonstrate that (1) The end-to-end trained ScoutCT-NET can be used to generate 3D patient attenuation models and demonstrate empirical generalizability. 2) The 3D patient models can be used to accurately estimate the spatial distribution of radiation dose delivered by standard helical CTs prior to the actual CT acquisition; compared to the gold-standard dose distribution, 95.0% of the voxels in the ScoutCT-NET based dose maps have acceptable gamma values for 5 mm distance-to-agreement and 10% dose difference. 3) The 3D patient models also enabled accurate prescription of fluence-field modulated CT to generate a more uniform noise distribution across the patient body compared to tube current modulated CT. 4) ScoutCT-NET 3D patient models enabled accurate prescription of organ-specific CT to boost image quality for a given body region-of-interest under a given radiation dose constraint. 3D tomographic attenuation models generated by ScoutCT-NET from two-view scout images can be used to prescribe fluence-field modulated or organ-specific CT scans with high accuracy for the overall objective of radiation dose reduction or image quality improvement for a given imaging task.
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DOI：
10.1109/tpami.2020.3012955
发表时间：
2023-04
影响因子：
23.6
作者：
Chun IY;Huang Z;Lim H;Fessler JA
通讯作者：
Fessler JA
影响因子：
3.8
作者：
Hsieh, Scott S.;Fleischmann, Dominik;Pelc, Norbert J.
通讯作者：
Pelc, Norbert J.
影响因子：
5
作者：
Christner, Jodie A.;Zavaletta, Vanessa A.;McCollough, Cynthia H.
通讯作者：
McCollough, Cynthia H.
影响因子：
3.8
作者：
Gomez-Cardona D;Hayes JW;Zhang R;Li K;Cruz-Bastida JP;Chen GH
通讯作者：
Chen GH
影响因子：
--
作者：
Lubner, Meghan G.;Pickhardt, Perry J.;Kim, David H.;Tang, Jie;del Rio, Alejandro Munoz;Chen, Guang-Hong
通讯作者：
Chen, Guang-Hong