论文标题
在线性反问题的深度学习中克服测量不一致：医学成像中的应用
Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging
论文作者
论文摘要
目前，深神经网络（DNN）的显着性能使它们成为解决线性反相反问题的首选方法。它们已应用于超级溶解和恢复图像，以及重建MR和CT图像。在这些应用程序中，DNN通过通过训练数据，测量图像和输入图像之间的地图进行查找，通过查找前向操作员进行反转。然后可以预期该地图仍然对测试数据有效。但是，该框架在测试过程中引入了测量不一致。我们表明，在医学成像或防御等领域可能至关重要的这种不一致与概括误差密切相关。然后，我们提出了一个框架，该框架通过优化算法进行后处理DNN的输出，从而实现测量一致性。 MR图像上的实验表明，通过我们的方法执行测量一致性可能会导致重建性能的巨大提高。
The remarkable performance of deep neural networks (DNNs) currently makes them the method of choice for solving linear inverse problems. They have been applied to super-resolve and restore images, as well as to reconstruct MR and CT images. In these applications, DNNs invert a forward operator by finding, via training data, a map between the measurements and the input images. It is then expected that the map is still valid for the test data. This framework, however, introduces measurement inconsistency during testing. We show that such inconsistency, which can be critical in domains like medical imaging or defense, is intimately related to the generalization error. We then propose a framework that post-processes the output of DNNs with an optimization algorithm that enforces measurement consistency. Experiments on MR images show that enforcing measurement consistency via our method can lead to large gains in reconstruction performance.
