Three-dimensional (3D) imaging is extremely popular in medical imaging as it enables diagnosis and disease monitoring through complete anatomical coverage. Computed Tomography or Magnetic Resonance Imaging (MRI) techniques are commonly used, however, anisotropic volumes with thick slices are often acquired to reduce scan times. Deep learning (DL) can be used to recover high-resolution features in the low-resolution dimension through super-resolution reconstruction (SRR). However, this often relies on paired training data which is unavailable in many medical applications. We describe a novel approach that only requires native anisotropic 3D medical images for training. This method relies on the observation that small 2D patches extracted from a 3D volume contain similar visual features, irrespective of their orientation. Therefore, it is possible to leverage disjoint patches from the high-resolution plane, to learn SRR in the low-resolution plane. Our proposed unpaired approach uses a modified CycleGAN architecture with a cycle-consistent gradient mapping loss: Cycle Loss Augmented Degradation Enhancement (CLADE). We show the feasibility of CLADE in an exemplar application; anisotropic 3D abdominal MRI data. We demonstrate superior quantitative image quality with CLADE over supervised learning and conventional CycleGAN architectures. CLADE also shows improvements over anisotopic volumes in terms of qualitative image ranking and quantitative edge sharpness and signal-to-noise ratio. This paper demonstrates the potential of using CLADE for super-resolution reconstruction of anisotropic 3D medical imaging data without the need for paired training data.

翻译：三维（3D）成像因能通过完整解剖覆盖实现诊断与疾病监测，在医学成像领域极为普及。尽管计算机断层扫描或磁共振成像技术已广泛使用，但为缩短扫描时间，常需采集具有厚层厚度的各向异性体数据。深度学习可通过超分辨率重建提取低分辨率维度中的高分辨率特征。然而，该方法通常依赖配对训练数据，这在许多医学应用中难以获取。本文提出一种仅需原生各向异性三维医学图像进行训练的新方法。该方法基于以下观察：从三维体数据中提取的二维小图像块，无论其方向如何，均包含相似的视觉特征。因此，可利用高分辨率平面中不重叠的图像块，学习低分辨率平面的超分辨率重建。我们提出的非配对方法采用改进的CycleGAN架构，结合了循环一致性梯度映射损失：循环损失增强退化补偿。通过各向异性三维腹部MRI数据的典型应用，验证了CLADE的可行性。研究表明，CLADE在定量图像质量上优于监督学习及传统CycleGAN架构。在定性图像排序、定量边缘锐度及信噪比方面，CLADE相较于各向异性体数据也表现出显著提升。本文证明了CLADE在无需配对训练数据的情况下，实现各向异性三维医学成像数据超分辨率重建的潜力。