JSTSP Volume 14 Issue 6

In magnetic resonance imaging (MRI), several images can be obtained using different imaging settings (e.g. T1, T2, DWI, and Flair). These images have similar anatomical structures but are with different contrasts, which provide a wealth of information for diagnosis.

Recently, deep neural network (DNN) based methods for low-dose CT have been investigated to achieve excellent performance in both image quality and computational speed. However, almost all methods using DNNs for low-dose CT require clean ground truth data with full radiation dose to train the DNNs. In this work, we attempt to train DNNs for low-dose CT reconstructions with reduced tube current by investigating unsupervised training of DNNs for denoising sensor measurements or sinograms without full-dose ground truth images.

Regularization by denoising (RED) is an image reconstruction framework that uses an image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED with learned denoisers corresponding to pre-trained convolutional neural nets (CNNs). In this work, we propose to broaden the current denoiser-centric view of RED by considering priors corresponding to networks trained for more general artifact-removal.