ArticleCerebral cortex (New York, N.Y. : 1991)2021
Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution.
Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Deep-learning-accelerated T1-MPRAGE MRI for quantification and visual grading of cerebral volume in memory loss patients.Radiology advances · 2025Article
- Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Quantifying axonal features of human superficial white matter from three-dimensional multibeam serial electron microscopy data assisted by deep learning.NeuroImage · 2025Article
- Multisequence 3-T Image Synthesis from 64-mT Low-Field-Strength MRI Using Generative Adversarial Networks in Multiple Sclerosis.Radiology · 2025Observational
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- Machine learning-based reconstruction of 2D MRI for quantitative morphometry in epilepsy.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- In vivo evidence for cell body loss in cortical lesions in people with multiple sclerosis.Annals of clinical and translational neurology · 2025Article
- Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement.Magma (New York, N.Y.) · 2024Article
- High-resolution 7T fMRI reveals the visual zone of the human claustrum.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Rapid microscopic 3D-diffusion tensor imaging fiber-tracking of mouse brain in vivo by super resolution reconstruction: validation on MAP6-KO mouse model.Magma (New York, N.Y.) · 2023Article
- Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat).Medical image analysis · 2023Article
- SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry.Science advances · 2023Article
- [Multimodality-based super-resolution reconstruction for routine brain magnetic resonance images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- Upstream Machine Learning in Radiology.Radiologic clinics of North America · 2021Review
- MRI with ultrahigh field strength and high-performance gradients: challenges and opportunities for clinical neuroimaging at 7 T and beyond.European radiology experimental · 2021Review
- Article
- Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising.NeuroImage · 2021Article
- Multishot Diffusion-Weighted MRI of the Breast With Multiplexed Sensitivity Encoding (MUSE) and Shot Locally Low-Rank (Shot-LLR) Reconstructions.Journal of magnetic resonance imaging : JMRI · 2021Article
- MRSaiFE: An AI-based Approach Towards the Real-Time Prediction of Specific Absorption Rate.IEEE access : practical innovations, open solutions · 2021Article
- Noise2Average: An iterative residual learning strategy for image denoising without clean data.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
13 authors.
Funding
Abstract
Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.
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