使用精确学习推导神经网络架构：并行到扇形束转换
Deriving Neural Network Architectures using Precision Learning: Parallel-to-fan beam Conversion
摘要：
在本文中，我们推导出一个神经网络架构的基础上的分析制定的平行扇束转换问题的概念，精度学习。该网络允许以数据驱动的方式学习这种转换中的未知运算符，避免插值和潜在的分辨率损失。已知算子的积分导致少量的可训练参数，这些参数只能从合成数据中估计。在混合MRI/X射线成像的背景下评价该概念，其中需要将平行射束MRI投影转换为扇束X射线投影。所提出的方法相比，传统的重组方法。结果表明，所提出的方法优于逐射线插值，并且能够使用相同数量的平行射束输入投影提供更清晰的图像，这对于介入应用至关重要。我们相信，这种方法为深度学习、信号处理、物理学和传统模式识别的进一步工作奠定了基础。
英文摘要：
In this paper, we derive a neural network architecture based on an analytical formulation of the parallel-to-fan beam conversion problem following the concept of precision learning. The network allows to learn the unknown operators in this conversion in a data-driven manner avoiding interpolation and potential loss of resolution. Integration of known operators results in a small number of trainable parameters that can be estimated from synthetic data only. The concept is evaluated in the context of Hybrid MRI/X-ray imaging where transformation of the parallel-beam MRI projections to fan-beam X-ray projections is required. The proposed method is compared to a traditional rebinning method. The results demonstrate that the proposed method is superior to ray-by-ray interpolation and is able to deliver sharper images using the same amount of parallel-beam input projections which is crucial for interventional applications. We believe that this approach forms a basis for further work uniting deep learning, signal processing, physics, and traditional pattern recognition.
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。
