School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
Center of AI Perception, AI Research Institute, Harbin Institute of Technology, Harbin 150001, China.
J Healthc Eng. 2021 Apr 9;2021:5594649. doi: 10.1155/2021/5594649. eCollection 2021.
Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.
磁共振成像（MRI）广泛应用于疾病的检测和诊断。高分辨率的 MR 图像有助于医生定位病灶并诊断疾病。然而，获取高分辨率的 MR 图像需要高强度磁场和长时间的扫描，这会给患者带来不适，并容易引入运动伪影，导致图像质量下降。因此，硬件成像的分辨率已经达到了极限。针对这种情况，提出了一种基于深度学习超分辨率的统一框架，将最先进的自然图像深度学习方法应用于 MRI 超分辨率。与传统的图像超分辨率方法相比，深度学习超分辨率方法具有更强的特征提取和表征能力，可以从大量样本数据中学习先验知识，具有更稳定、更优异的图像重建效果。我们提出了一种基于深度学习的 MRI 超分辨率的统一框架，该框架包含了目前具有最佳超分辨率效果的五种深度学习方法。此外，还构建了一个具有 ×2、×3 和 ×4 尺度的高低分辨率 MR 图像数据集，涵盖了颅骨、膝盖、乳房和头颈部的 4 个部分。实验结果表明，与传统方法相比，所提出的深度学习超分辨率统一框架对数据具有更好的重建效果，为深度学习超分辨率在 MR 图像中的应用提供了标准数据集和实验基准。
