论文标题
无监督的超分辨率：从低分辨率各向异性示例中创建高分辨率的医疗图像
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples
论文作者
论文摘要
尽管在临床实践中需要高分辨率的各向同性3D医学图像，但它们的获取并不总是可行的。取而代之的是，使用常规插值方法将较低的分辨率图像提升到更高分辨率。在临床环境中，基于精致的基于学习的超分辨率方法通常无法获得，因为这种方法需要使用高分辨率各向同性示例的训练。为了解决这个问题，我们提出了一种基于学习的超分辨率方法，该方法可以仅使用各向异性图像，即没有高分辨率地面真相数据进行培训。该方法利用了在各向异性图像上训练的自动编码器生成的潜在空间，以增加低分辨率图像中的空间分辨率。使用自动化心脏诊断挑战（ACDC）的100次公开心脏Cine MR扫描对该方法进行了训练和评估。定量结果表明，所提出的方法的性能优于常规插值方法。此外，定性结果表明，特别优质的心脏结构是高质量合成的。该方法有可能应用于其他解剖和模态，并且可以轻松地应用于任何3D各向异性医学图像数据集。
Although high resolution isotropic 3D medical images are desired in clinical practice, their acquisition is not always feasible. Instead, lower resolution images are upsampled to higher resolution using conventional interpolation methods. Sophisticated learning-based super-resolution approaches are frequently unavailable in clinical setting, because such methods require training with high-resolution isotropic examples. To address this issue, we propose a learning-based super-resolution approach that can be trained using solely anisotropic images, i.e. without high-resolution ground truth data. The method exploits the latent space, generated by autoencoders trained on anisotropic images, to increase spatial resolution in low-resolution images. The method was trained and evaluated using 100 publicly available cardiac cine MR scans from the Automated Cardiac Diagnosis Challenge (ACDC). The quantitative results show that the proposed method performs better than conventional interpolation methods. Furthermore, the qualitative results indicate that especially finer cardiac structures are synthesized with high quality. The method has the potential to be applied to other anatomies and modalities and can be easily applied to any 3D anisotropic medical image dataset.
