Accelerated MRI with Un-trained Neural Networks

Mohammad Zalbagi Darestani, Reinhard Heckel

Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems. Typically, CNNs are trained on large amounts of training images. Recently, however, un-trained CNNs such as the Deep Image Prior and Deep Decoder have achieved excellent performance for image reconstruction problems such as denoising and inpainting, \emph{without using any training data}. Motivated by this development, we address the reconstruction problem arising in accelerated MRI with un-trained neural networks. We propose a highly-optimized un-trained recovery approach based on a variation of the Deep Decoder. We show that the resulting method significantly outperforms conventional un-trained methods such as total-variation norm minimization, as well as naive applications of un-trained networks. Most importantly, we achieve on-par performance with a standard trained baseline, the U-net, on the FastMRI dataset, a dataset for benchmarking deep learning based reconstruction methods. While state-of-the-art trained methods still outperform our un-trained method, our work demonstrates that current trained methods only achieve a minor performance gain over un-trained methods, at the cost of a loss in robustness to out-of-distribution examples. Therefore, un-trained neural networks are a serious competitor to trained ones for medical imaging.

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