CLADE: Cycle Loss Augmented Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images
arxiv(2023)
摘要
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.
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