UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations

Lucas Mahler, Julius Steiglechner, Benjamin Bender, Tobias Lindig, Dana Ramadan +10 more
6/3/2024
cs.CV

No official code found. Best implementation: MPI-Neuroinformatics/UltraCortex (4 stars, last commit 2025-04-22).

Abstract

The UltraCortex repository (https://www.ultracortex.org) houses magnetic resonance imaging data of the human brain obtained at an ultra-high field strength of 9.4 T. It contains 86 structural MR images with spatial resolutions ranging from 0.6 to 0.8 mm. Additionally, the repository includes segmentations of 12 brains into gray and white matter compartments. These segmentations have been independently validated by two expert neuroradiologists, thus establishing them as a reliable gold standard. This resource provides researchers with access to high-quality brain imaging data and validated segmentations, facilitating neuroimaging studies and advancing our understanding of brain structure and function. Existing repositories do not accommodate field strengths beyond 7 T, nor do they offer validated segmentations, underscoring the significance of this new resource.

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Code Implementations(1)

MPI-Neuroinformatics/UltraCortexCommunity100%

UltraCortex is a Python-based toolkit for calculating and visualizing various metrics for MRI images following the BIDS (Brain Imaging Data Structure) format. The toolkit includes metrics such as Entropy Focus Criterion (EFC), Anatomical Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR), and Coefficient of Joint Variation (CJV).

42PythonApr 17, 20241 years ago
datasetsmedical-imagingmetrics

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Cite this paper

@article{mahler2024ultracortex,
  title  = {UltraCortex: Submillimeter Ultra-High Field 9.4 T Brain MR Image Collection and Manual Cortical Segmentations},
  author = {Lucas Mahler and Julius Steiglechner and Benjamin Bender and Tobias Lindig and Dana Ramadan and Jonas Bause and Florian Birk and Rahel Heule and Edyta Charyasz and Michael Erb and Vinod Jangir Kumar and Gisela E Hagberg and Pascal Martin and Gabriele Lohmann and Klaus Scheffler},
  year   = {2024},
  eprint = {2406.18571},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.18571v3}
}

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