论文标题
MRI图像通过使用神经ODE的学习优化重建
MRI Image Reconstruction via Learning Optimization Using Neural ODEs
论文作者
论文摘要
我们建议将MRI图像重建作为优化问题，并使用普通微分方程（ODE）建模优化轨迹作为动态过程。我们使用神经网络对ODE中的动力学进行建模，并用现成的（固定）求解器求解所需的ODE以获得重建的图像。我们将此模型扩展到了网络设计（学习的求解器）中。我们研究了基于三个ODE求解器的几个模型，并将模型与固定求解器和学习的求解器进行了比较。我们的模型获得了更好的重建结果，并且比其他流行的方法（例如UNET和级联CNN）更有效。我们通过使用神经ODE对连续优化动态进行建模，引入了一种解决MRI重建问题的新方法。
We propose to formulate MRI image reconstruction as an optimization problem and model the optimization trajectory as a dynamic process using ordinary differential equations (ODEs). We model the dynamics in ODE with a neural network and solve the desired ODE with the off-the-shelf (fixed) solver to obtain reconstructed images. We extend this model and incorporate the knowledge of off-the-shelf ODE solvers into the network design (learned solvers). We investigate several models based on three ODE solvers and compare models with fixed solvers and learned solvers. Our models achieve better reconstruction results and are more parameter efficient than other popular methods such as UNet and cascaded CNN. We introduce a new way of tackling the MRI reconstruction problem by modeling the continuous optimization dynamics using neural ODEs.
