Ice-Tide: Implicit Cryo-ET Imaging and Deformation Estimation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910743636475904 |
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| author | Debarnot, Valentin Kishore, Vinith Righetto, Ricardo D. Dokmanić, Ivan |
| author_facet | Debarnot, Valentin Kishore, Vinith Righetto, Ricardo D. Dokmanić, Ivan |
| contents | We introduce ICE-TIDE, a method for cryogenic electron tomography (cryo-ET) that simultaneously aligns observations and reconstructs a high-resolution volume. The alignment of tilt series in cryo-ET is a major problem limiting the resolution of reconstructions. ICE-TIDE relies on an efficient coordinate-based implicit neural representation of the volume which enables it to directly parameterize deformations and align the projections. Furthermore, the implicit network acts as an effective regularizer, allowing for high-quality reconstruction at low signal-to-noise ratios as well as partially restoring the missing wedge information. We compare the performance of ICE-TIDE to existing approaches on realistic simulated volumes where the significant gains in resolution and accuracy of recovering deformations can be precisely evaluated. Finally, we demonstrate ICE-TIDE's ability to perform on experimental data sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02182 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ice-Tide: Implicit Cryo-ET Imaging and Deformation Estimation Debarnot, Valentin Kishore, Vinith Righetto, Ricardo D. Dokmanić, Ivan Image and Video Processing We introduce ICE-TIDE, a method for cryogenic electron tomography (cryo-ET) that simultaneously aligns observations and reconstructs a high-resolution volume. The alignment of tilt series in cryo-ET is a major problem limiting the resolution of reconstructions. ICE-TIDE relies on an efficient coordinate-based implicit neural representation of the volume which enables it to directly parameterize deformations and align the projections. Furthermore, the implicit network acts as an effective regularizer, allowing for high-quality reconstruction at low signal-to-noise ratios as well as partially restoring the missing wedge information. We compare the performance of ICE-TIDE to existing approaches on realistic simulated volumes where the significant gains in resolution and accuracy of recovering deformations can be precisely evaluated. Finally, we demonstrate ICE-TIDE's ability to perform on experimental data sets. |
| title | Ice-Tide: Implicit Cryo-ET Imaging and Deformation Estimation |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2403.02182 |