Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors

High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware and processing constraints. Recently, deep learning methods have been shown to produce compelling state-of-the-art results for image enhancement/s...

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Bibliographic Details
Published in:IEEE transactions on image processing 2020-01, Vol.29, p.1368-1383
Main Authors: Cherukuri, Venkateswararao, Guo, Tiantong, Schiff, Steven J., Monga, Vishal
Format: Article
Language:English
Subjects:
Brain
Deep learning
Feedback
Image enhancement
Image quality
Image resolution
Interpolation
Laplace equations
low-rank
Machine learning
Magnetic resonance imaging
Mathematical analysis
priors
Sharpness
Spatial resolution
Training
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Summary:High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware and processing constraints. Recently, deep learning methods have been shown to produce compelling state-of-the-art results for image enhancement/super-resolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image super-resolution (SR). Our contributions are then incorporating these priors in an analytically tractable fashion as well as towards a novel prior guided network architecture that accomplishes the super-resolution task. This is particularly challenging for the low rank prior since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. As a key extension, we modify the fixed feedback (Laplacian) layer by learning a new set of training data driven filters that are optimized for enhanced sharpness. Experiments performed on publicly available MR brain image databases and comparisons against existing state-of-the-art methods show that the proposed prior guided network offers significant practical gains in terms of improved SNR/image quality measures. Because our priors are on output images, the proposed method is versatile and can be combined with a wide variety of existing network architectures to further enhance their performance.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2019.2942510