Department of Mathematics, National University of Singapore, Singapore. Electronic address: https://github.com/jiulongliu/Deep-Joint-Indirect-Registration-and-Reconstruction.
Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK.
Med Image Anal. 2021 Feb;68:101930. doi: 10.1016/j.media.2020.101930. Epub 2020 Dec 5.
Indirect image registration is a promising technique to improve image reconstruction quality by providing a shape prior for the reconstruction task. In this paper, we propose a novel hybrid method that seeks to reconstruct high quality images from few measurements whilst requiring low computational cost. With this purpose, our framework intertwines indirect registration and reconstruction tasks is a single functional. It is based on two major novelties. Firstly, we introduce a model based on deep nets to solve the indirect registration problem, in which the inversion and registration mappings are recurrently connected through a fixed-point interaction based sparse optimisation. Secondly, we introduce specific inversion blocks, that use the explicit physical forward operator, to map the acquired measurements to the image reconstruction. We also introduce registration blocks based deep nets to predict the registration parameters and warp transformation accurately and efficiently. We demonstrate, through extensive numerical and visual experiments, that our framework outperforms significantly classic reconstruction schemes and other bi-task method; this in terms of both image quality and computational time. Finally, we show generalisation capabilities of our approach by demonstrating their performance on fast Magnetic Resonance Imaging (MRI), sparse view computed tomography (CT) and low dose CT with measurements much below the Nyquist limit.
间接图像配准是一种很有前途的技术，可以通过为重建任务提供形状先验来提高图像重建质量。在本文中，我们提出了一种新颖的混合方法，旨在从少量测量中重建高质量的图像，同时要求计算成本低。为此，我们的框架将间接注册和重建任务交织在一个单一的功能中。它基于两个主要的创新点。首先，我们引入了一种基于深度网络的模型来解决间接配准问题，其中反演和配准映射通过基于固定点的稀疏优化的递归连接。其次，我们引入了特定的反转块，使用显式的物理前向算子，将采集到的测量值映射到图像重建中。我们还引入了基于深度网络的配准块，以准确有效地预测配准参数和变换。通过大量的数值和可视化实验，我们证明了我们的框架在图像质量和计算时间方面都明显优于经典的重建方案和其他双任务方法。最后，我们通过在快速磁共振成像（MRI）、稀疏视图计算机断层扫描（CT）和低剂量 CT 中展示低于奈奎斯特极限的测量值，展示了我们方法的泛化能力。
