Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

Maximilian Seitzer, Guang-Zhong Yang, Jo Schlemper, Ozan Oktay, Tobias Würfl, Vincent Christlein, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Daniel Rueckert, Andreas Maier

Abstract:

Deep learning approaches have shown promising performance
for compressed sensing-based Magnetic Resonance Imaging. While deep
neural networks trained with mean squared error (MSE) loss functions
can achieve high peak signal to noise ratio, the reconstructed images are
often blurry and lack sharp details, especially for higher undersampling
rates. Recently, adversarial and perceptual loss functions have been shown
to achieve more visually appealing results. However, it remains an open
question how to (1) optimally combine these loss functions with the
MSE loss function and (2) evaluate such a perceptual enhancement. In
this work, we propose a hybrid method, in which a visual refinement
component is learnt on top of an MSE loss-based reconstruction network.
In addition, we introduce a semantic interpretability score, measuring the
visibility of the region of interest in both ground truth and reconstructed
images, which allows us to objectively quantify the usefulness of the
image quality for image post-processing and analysis. Applied on a large
cardiac MRI dataset simulated with 8-fold undersampling, we demonstrate
significant improvements (p <0.01) over the state-of-the-art in both a
human observer study and the semantic interpretability score.