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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.08161 |
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| _version_ | 1866914622618992640 |
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| author | Olsen, Anders S. Navarro, Miriam L. Svarer, Claus Hinrich, Jesper L. Mørup, Morten Knudsen, Gitte M. |
| author_facet | Olsen, Anders S. Navarro, Miriam L. Svarer, Claus Hinrich, Jesper L. Mørup, Morten Knudsen, Gitte M. |
| contents | Dynamic neuroimaging data, such as emission tomography measurements of radiotracer transport in blood or cerebrospinal fluid, often exhibit diffusion-like properties. These introduce distance-dependent temporal delays, scale-differences, and stretching effects that limit the effectiveness of conventional linear modeling and decomposition methods. To address this, we present the shift- and stretch-invariant non-negative matrix factorization framework. Our approach estimates both integer and non-integer temporal shifts as well as temporal stretching, all implemented in the frequency domain, where shifts correspond to phase modifications, and where stretching is handled via zero-padding or truncation. The model is implemented in PyTorch (https://github.com/anders-s-olsen/shiftstretchNMF). We demonstrate on synthetic data and brain emission tomography data that the model is able to account for stretching to provide more detailed characterization of brain tissue structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08161 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Shift- and stretch-invariant non-negative matrix factorization with an application to brain tissue delineation in emission tomography data Olsen, Anders S. Navarro, Miriam L. Svarer, Claus Hinrich, Jesper L. Mørup, Morten Knudsen, Gitte M. Machine Learning Dynamic neuroimaging data, such as emission tomography measurements of radiotracer transport in blood or cerebrospinal fluid, often exhibit diffusion-like properties. These introduce distance-dependent temporal delays, scale-differences, and stretching effects that limit the effectiveness of conventional linear modeling and decomposition methods. To address this, we present the shift- and stretch-invariant non-negative matrix factorization framework. Our approach estimates both integer and non-integer temporal shifts as well as temporal stretching, all implemented in the frequency domain, where shifts correspond to phase modifications, and where stretching is handled via zero-padding or truncation. The model is implemented in PyTorch (https://github.com/anders-s-olsen/shiftstretchNMF). We demonstrate on synthetic data and brain emission tomography data that the model is able to account for stretching to provide more detailed characterization of brain tissue structure. |
| title | Shift- and stretch-invariant non-negative matrix factorization with an application to brain tissue delineation in emission tomography data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.08161 |