Cao Z, Liu G, Zhang Z, Shi F, Zhang Y
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2022 Jul 20;42(7):1019-1025. doi: 10.12122/j.issn.1673-4254.2022.07.08.
To propose a multi-modality-based super-resolution synthesis model for reconstruction of routine brain magnetic resonance images (MRI) with a low resolution and a high thickness into high-resolution images.
Based on real paired low-high resolution MRI data (2D T1, 2D T2 FLAIR and 3D T1), a structure-constrained image mapping network was used to extract important features from the images with different modalities including the whole T1 and subcortical regions of T2 FLAIR to reconstruct T1 images with higher resolutions. The gray scale intensity and structural similarities between the super-resolution images and high-resolution images were used to enhance the reconstruction performance. We used the anatomical information acquired from segment maps of the super-resolution T1 image and the ground truth by a segmentation tool as a significant constraint for adaptive learning of the intrinsic tissue structure characteristics of the brain to improve the reconstruction performance of the model.
Our method showed the performance on the testing dataset than other methods with an average PSNR of 33.11 and SSIM of 0.996. The anatomical structure of the brain including the sulcus, gyrus, and subcortex were all reconstructed clearly using the proposed method, which also greatly enhanced the precision of MSCSR for brain volume measurement.
The proposed MSCSR model shows excellent performance for reconstructing super-resolution brain MR images based on the information of brain tissue structure and multimodality MR images.
提出一种基于多模态的超分辨率合成模型，用于将低分辨率、厚层的常规脑磁共振成像（MRI）重建为高分辨率图像。
基于真实的低分辨率与高分辨率MRI配对数据（二维T1、二维T2液体衰减反转恢复序列和三维T1），使用结构约束图像映射网络从不同模态的图像中提取重要特征，包括整个T1图像和T2液体衰减反转恢复序列的皮质下区域，以重建更高分辨率的T1图像。利用超分辨率图像与高分辨率图像之间的灰度强度和结构相似性来提高重建性能。我们将通过分割工具从超分辨率T1图像的分割图和真实图像中获取的解剖学信息作为自适应学习脑内组织结构特征的重要约束条件，以提高模型的重建性能。
我们的方法在测试数据集上表现优于其他方法，平均峰值信噪比为33.11，结构相似性指数为0.996。使用所提出的方法可以清晰地重建包括脑沟、脑回和皮质下区域在内的脑解剖结构，这也大大提高了多尺度上下文敏感超分辨率重建（MSCSR）在脑体积测量方面的精度。
所提出的MSCSR模型基于脑组织结构信息和多模态MR图像，在重建超分辨率脑MR图像方面表现出优异的性能。
