Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies Article Swipe
Karthik Gopinath
,
Douglas N. Greve
,
Sudeshna Das
,
S. R. Arnold
,
Colin Magdamo
,
Juan Eugenio Iglesias
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.01827
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.01827
Surface analysis of the cortex is ubiquitous in human neuroimaging with MRI, e.g., for cortical registration, parcellation, or thickness estimation. The convoluted cortical geometry requires isotropic scans (e.g., 1mm MPRAGEs) and good gray-white matter contrast for 3D reconstruction. This precludes the analysis of most brain MRI scans acquired for clinical purposes. Analyzing such scans would enable neuroimaging studies with sample sizes that cannot be achieved with current research datasets, particularly for underrepresented populations and rare diseases. Here we present the first method for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and pulse sequence. The methods has a learning component and a classical optimization module. The former uses domain randomization to train a CNN that predicts an implicit representation of the white matter and pial surfaces (a signed distance function) at 1mm isotropic resolution, independently of the pulse sequence and resolution of the input. The latter uses geometry processing to place the surfaces while accurately satisfying topological and geometric constraints, thus enabling subsequent parcellation and thickness estimation with existing methods. We present results on 5mm axial FLAIR scans from ADNI and on a highly heterogeneous clinical dataset with 5,000 scans. Code and data are publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.01827
- https://arxiv.org/pdf/2305.01827
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4368755066
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4368755066Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.01827Digital Object Identifier
- Title
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Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studiesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-02Full publication date if available
- Authors
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Karthik Gopinath, Douglas N. Greve, Sudeshna Das, S. R. Arnold, Colin Magdamo, Juan Eugenio IglesiasList of authors in order
- Landing page
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https://arxiv.org/abs/2305.01827Publisher landing page
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https://arxiv.org/pdf/2305.01827Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2305.01827Direct OA link when available
- Concepts
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Neuroimaging, Fluid-attenuated inversion recovery, White matter, Artificial intelligence, Computer science, Pattern recognition (psychology), Computer vision, Magnetic resonance imaging, Neuroscience, Medicine, Psychology, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.component | 106 |
| abstract_inverted_index.datasets, | 68 |
| abstract_inverted_index.diseases. | 75 |
| abstract_inverted_index.function) | 136 |
| abstract_inverted_index.geometric | 165 |
| abstract_inverted_index.isotropic | 25, 139 |
| abstract_inverted_index.precludes | 39 |
| abstract_inverted_index.purposes. | 50 |
| abstract_inverted_index.sequence. | 100 |
| abstract_inverted_index.thickness | 18, 88, 172 |
| abstract_inverted_index.accurately | 161 |
| abstract_inverted_index.convoluted | 21 |
| abstract_inverted_index.estimation | 89, 173 |
| abstract_inverted_index.gray-white | 32 |
| abstract_inverted_index.processing | 155 |
| abstract_inverted_index.resolution | 97, 147 |
| abstract_inverted_index.satisfying | 162 |
| abstract_inverted_index.subsequent | 169 |
| abstract_inverted_index.ubiquitous | 6 |
| abstract_inverted_index.estimation. | 19 |
| abstract_inverted_index.populations | 72 |
| abstract_inverted_index.resolution, | 140 |
| abstract_inverted_index.topological | 163 |
| abstract_inverted_index.constraints, | 166 |
| abstract_inverted_index.neuroimaging | 9, 56 |
| abstract_inverted_index.optimization | 110 |
| abstract_inverted_index.parcellation | 170 |
| abstract_inverted_index.particularly | 69 |
| abstract_inverted_index.heterogeneous | 191 |
| abstract_inverted_index.independently | 141 |
| abstract_inverted_index.parcellation, | 16, 86 |
| abstract_inverted_index.randomization | 116 |
| abstract_inverted_index.registration, | 15, 85 |
| abstract_inverted_index.representation | 125 |
| abstract_inverted_index.reconstruction, | 84 |
| abstract_inverted_index.reconstruction. | 37 |
| abstract_inverted_index.underrepresented | 71 |
| abstract_inverted_index.https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical | 204 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |