DeepOrganNet：基于深度变形网络的单视图投影三维/四维肺模型的实时重建和可视化
DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network
摘要：
本文介绍了一种基于深度神经网络的方法，DeepOrganNet，用于实时的从单视图医学图像生成和可视化高保真3D/4D器官几何模型。传统的3D/4D医学图像重建需要近数百个投影，这花费了难以忍受的计算时间，并向人类受试者提供了不期望的高成像/辐射剂量。此外，它总是需要进一步的臭名昭著的过程，以提取准确的三维器官模型随后。据我们所知，目前还没有一种方法直接和明确地重建多个三维器官网格从一个单一的二维医学灰度图像的飞行。给定单视图2D医学图像，例如，3D/4D-CT投影或X射线图像，我们的端到端DeepOrganNet框架可以通过基于三变量张量积变形技术从多个模板中学习平滑变形场，利用从输入2D图像中提取的信息潜在描述符，有效地重建具有各种几何形状的3D/4D肺部模型。该方法可以保证生成高质量和高保真度的三维/四维肺部模型的流形网格。这项工作的主要贡献是从2D单视图投影准确地重建3D器官形状，显着提高手术时间，以允许即时可视化，并显着降低人类受试者的成像剂量。通过使用来自合成体模和真实患者数据集的大量3D和4D示例，对实验结果进行评估，并与传统重建方法和深度学习的最新技术进行比较。该方法生成10K顶点的器官网格仅需几毫秒时间，在实时图像引导放射治疗（IGRT）中具有很大的应用潜力。
英文摘要：
This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize high-fidelity 3D / 4D organ geometric models from single-view medical image in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to extract the accurate 3D organ models subsequently. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples from synthetic phantom and real patient datasets. The proposed method only needs several milliseconds to generate organ meshes with 10K vertices, which has a great potential to be used in real-time image guided radiation therapy (IGRT).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
