二维图像的正则坐标空间三维重建
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
摘要：
有限的捕获范围以及为基于优化的2D/3D图像配准方法提供高质量初始化的要求会显著降低3D图像重建和运动补偿流水线的性能。包含诸如胎儿宫内成像的显著对象运动的复杂临床成像场景使3D图像和体积重建过程复杂化。在本文中，我们提出了一种基于学习的图像配准方法，能够预测任意取向的2D图像切片的3D刚性变换，相对于一个学习的规范图集坐标系。该方法只利用图像切片的灰度信息进行配准和正则对准，不需要空间变换初始化。为了找到图像变换，我们利用卷积神经网络（CNN）架构来学习能够将2D图像切片映射到3D规范图谱空间的回归函数。我们广泛地评估了我们的方法的有效性，定量模拟磁共振成像（MRI），胎儿脑成像与合成运动，并进一步证明了定性的结果，真正的胎儿MRI数据，我们的方法被集成到一个完整的重建和运动补偿管道。我们基于学习的配准在模拟数据上实现了7 mm的平均空间预测误差，并为胎龄约20周的重度移动胎儿提供了质量改进的重建。我们的模型为2D/3D配准初始化问题提供了一个通用且计算效率高的解决方案，适用于实时场景。
英文摘要：
Limited capture range, and the requirement to provide high quality initialization for optimization-based 2D/3D image registration methods, can significantly degrade the performance of 3D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion such as fetal in-utero imaging, complicate the 3D image and volume reconstruction process. In this paper we present a learning based image registration method capable of predicting 3D rigid transformations of arbitrarily oriented 2D image slices, with respect to a learned canonical atlas co-ordinate system. Only image slice intensity information is used to perform registration and canonical alignment, no spatial transform initialization is required. To find image transformations we utilize a Convolutional Neural Network (CNN) architecture to learn the regression function capable of mapping 2D image slices to a 3D canonical atlas space. We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline. Our learning based registration achieves an average spatial prediction error of 7 mm on simulated data and produces qualitatively improved reconstructions for heavily moving fetuses with gestational ages of approximately 20 weeks. Our model provides a general and computationally efficient solution to the 2D/3D registration initialization problem and is suitable for real-time scenarios.
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