ArticleBMC medical imaging2017
Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity.
Article in BMC medical imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Attention over vulnerable brain regions associating cerebral palsy disorder and biological markers.Journal of advanced research · 2025Article
- Trends in brain MRI and CP association using deep learning.La Radiologia medica · 2024Article
- Mouse brain MR super-resolution using a deep learning network trained with optical imaging data.Frontiers in radiology · 2023Article
- An automatic restoration framework based on GPU-accelerated collateral filtering in brain MR images.BMC medical imaging · 2019Article
- Automatic brain tissue segmentation based on graph filter.BMC medical imaging · 2018Article
- Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform.BMC medical imaging · 2018Article
- Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
8 authors.
Funding
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Abstract
backgroundLow-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution.
methodsIn this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart.
resultsThe statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments.
conclusionExperimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast. Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weights.
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