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.

翻译：三维成像在医学应用中十分普遍，然而，为缩短扫描时间，常需采集切片厚、空间分辨率低且具有各向异性的三维体数据。深度学习可通过超分辨率重建技术恢复高分辨率特征。遗憾的是，许多三维医学应用缺乏配对训练数据，因此我们提出一种新颖的无配对方法——CLADE（循环损失增强退化）。CLADE采用改进的CycleGAN架构，通过循环一致性梯度映射损失，从各向异性三维体数据中高分辨率平面的非重叠切片学习低分辨率维度的超分辨率重建。我们验证了CLADE在腹部MRI和腹部CT中的可行性，并证明其在图像质量上显著优于低分辨率体数据及现有最先进的自监督超分辨率方法SMORE（合成多方向分辨率增强）。定量PIQUE（基于感知的图像质量评估）评分与边缘锐度（ES —— 基于感兴趣边界像素强度最大梯度计算）显示，CLADE在MRI和CT中均表现更优。定性评价表明，CLADE的整体图像质量最佳，且感知ES值高于低分辨率体数据及SMORE。本文展示了CLADE在无需配对三维训练数据的情况下，对各向异性三维医学成像数据进行超分辨率重建的潜力。