Preliminary Studies on Training and Fine-Tuning Deep Denoiser Neural Networks in Learned D-Amp for Undersampled Real Mr Measurements

Abstract

Recently, deep learning based MR image reconstructions have shown outstanding performance. While there have been many direct mapping based methods by deep neural networks without taking advantage of known physical model of medical imaging modality, some groups investigated combining conventional model-based image reconstruction (MBIR) and learning based method to enhance performance and computation speed of MBIR. Here, we investigated learned denoiser-based approximate message passing (LDAMP) with undersampled MR measurements. LDAMP yielded favorable performance over BM3D-based AMP even though ground truth images were noisy and deep denoisers were trained only for Gaussian noise, not for undersampling artifacts. We further investigated the feasibility of using Stein's unbiased risk estimator (SURE) to fine-tune deep denoisers with given undersampled MR measurement. Our proposed SURE based unsupervised fine-tuning method faithfully reconstructed images corresponding to the measurement and demonstrated the potential of enhancing the image quality of LDAMP results on real MRI dataset.