CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images

Three-dimensional (3D) imaging is popular in medical applications, however, anisotropic 3D volumes with thick, low-spatial-resolution slices are often acquired to reduce scan times. Deep learning (DL) offers a solution to recover high-resolution features through super-resolution reconstruction (SRR). Unfortunately, paired training data is unavailable in many 3D medical applications and therefore we propose a novel unpaired approach; CLADE (Cycle Loss Augmented Degradation Enhancement). CLADE uses a modified CycleGAN architecture with a cycle-consistent gradient mapping loss, to learn SRR of the low-resolution dimension, from disjoint patches of the high-resolution plane within the anisotropic 3D volume data itself. We show the feasibility of CLADE in abdominal MRI and abdominal CT and demonstrate significant improvements in CLADE image quality over low-resolution volumes and state-of-the-art self-supervised SRR; SMORE (Synthetic Multi-Orientation Resolution Enhancement). Quantitative PIQUE (qualitative perception-based image quality evaluator) scores and quantitative edge sharpness (ES - calculated as the maximum gradient of pixel intensities over a border of interest), showed superior performance for CLADE in both MRI and CT. Qualitatively CLADE had the best overall image quality and highest perceptual ES over the low-resolution volumes and SMORE. This paper demonstrates the potential of using CLADE for super-resolution reconstruction of anisotropic 3D medical imaging data without the need for paired 3D training data

Deep-learning methods for non-linear transonic flow-field prediction

It is envisaged that the next generation of ultra-high bypass ratio engines will use compact aero-engine nacelles. The design and optimisation process of these new configurations have been typically driven by numerical simulations, which can have a large computational cost. Few studies have considered the nacelle design process with low order models. Typically these low order methods are based on regression functions to predict the nacelle drag characteristics. However, it is also useful to develop methods for flow-field prediction that can be used at the preliminary design stages. This paper investigates an approach for the rapid assessment of transonic flow-fields based on convolutional neural networks (CNN) for 2D axisymmetric aeroengine nacelles. The process is coupled with a Sobel filter for edge detection to enhance the accuracy in the prediction of the shock wave location. Relative to a baseline CNN built with guidelines from the open literature, the proposed method has a 75% reduction in the mean square error for Mach number prediction. Overall, the presented method enables the fast prediction of the flow characteristics around civil aero-engine nacelles.Rolls-Royce pl