ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2022
[Multimodality-based super-resolution reconstruction for routine brain magnetic resonance images].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- [Whole-brain parcellation for macaque brain magnetic resonance images based on attention mechanism and multi-modality feature fusion].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
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5 authors.
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Abstract
objectiveTo 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.
methodsBased 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.
resultsOur 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.
conclusionThe 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.
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