A Spatiotemporal Illumination Model for 3D Image Fusion in Optical Coherence Tomography
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916131384590336 |
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| author | Ploner, Stefan Won, Jungeun Schottenhamml, Julia Girgis, Jessica Lam, Kenneth Waheed, Nadia Fujimoto, James Maier, Andreas |
| author_facet | Ploner, Stefan Won, Jungeun Schottenhamml, Julia Girgis, Jessica Lam, Kenneth Waheed, Nadia Fujimoto, James Maier, Andreas |
| contents | Optical coherence tomography (OCT) is a non-invasive, micrometer-scale imaging modality that has become a clinical standard in ophthalmology. By raster-scanning the retina, sequential cross-sectional image slices are acquired to generate volumetric data. In-vivo imaging suffers from discontinuities between slices that show up as motion and illumination artifacts. We present a new illumination model that exploits continuity in orthogonally raster-scanned volume data. Our novel spatiotemporal parametrization adheres to illumination continuity both temporally, along the imaged slices, as well as spatially, in the transverse directions. Yet, our formulation does not make inter-slice assumptions, which could have discontinuities. This is the first optimization of a 3D inverse model in an image reconstruction context in OCT. Evaluation in 68 volumes from eyes with pathology showed reduction of illumination artifacts in 88\% of the data, and only 6\% showed moderate residual illumination artifacts. The method enables the use of forward-warped motion corrected data, which is more accurate, and enables supersampling and advanced 3D image reconstruction in OCT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12114 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Spatiotemporal Illumination Model for 3D Image Fusion in Optical Coherence Tomography Ploner, Stefan Won, Jungeun Schottenhamml, Julia Girgis, Jessica Lam, Kenneth Waheed, Nadia Fujimoto, James Maier, Andreas Image and Video Processing Computer Vision and Pattern Recognition Optical coherence tomography (OCT) is a non-invasive, micrometer-scale imaging modality that has become a clinical standard in ophthalmology. By raster-scanning the retina, sequential cross-sectional image slices are acquired to generate volumetric data. In-vivo imaging suffers from discontinuities between slices that show up as motion and illumination artifacts. We present a new illumination model that exploits continuity in orthogonally raster-scanned volume data. Our novel spatiotemporal parametrization adheres to illumination continuity both temporally, along the imaged slices, as well as spatially, in the transverse directions. Yet, our formulation does not make inter-slice assumptions, which could have discontinuities. This is the first optimization of a 3D inverse model in an image reconstruction context in OCT. Evaluation in 68 volumes from eyes with pathology showed reduction of illumination artifacts in 88\% of the data, and only 6\% showed moderate residual illumination artifacts. The method enables the use of forward-warped motion corrected data, which is more accurate, and enables supersampling and advanced 3D image reconstruction in OCT. |
| title | A Spatiotemporal Illumination Model for 3D Image Fusion in Optical Coherence Tomography |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.12114 |