Deep learning-based single image super-resolution for low-field MR brain images.

Low-field MRI scanners are significantly less expensive than their high-field counterparts, which gives them the potential to make MRI technology more accessible all around the world. In general, images acquired using low-field MRI scanners tend to be of a relatively low resolution, as signal-to-noise ratios are lower. The aim of this work is to improve the resolution of these images. To this end, we present a deep learning-based approach to transform low-resolution low-field MR images into high-resolution ones. A convolutional neural network was trained to carry out single image super-resolution reconstruction using pairs of noisy low-resolution images and their noise-free high-resolution counterparts, which were obtained from the publicly available NYU fastMRI database. This network was subsequently applied to noisy images acquired using a low-field MRI scanner. The trained convolutional network yielded sharp super-resolution images in which most of the high-frequency components were recovered. In conclusion, we showed that a deep learning-based approach has great potential when it comes to increasing the resolution of low-field MR images.

Practical improvements in the design of high permittivity pads for dielectric shimming in neuroimaging at 7T.

Improvements are proposed for practical design and use of high permittivity materials in high field neuroimaging in three different areas: (i) a simple formula to predict the permittivity of tri-component aqueous-based perovskite suspensions with relative permittivities between 110 and 300, (ii) characterization of addition of a hydroxyethyl-cellulose gelling agent to improve the long-term stability and material properties of "dielectric pads", and (iii) investigation of the integration of, for example, headphones into the dielectric pads to increase patient comfort within tightly-fitting receive coil arrays.