Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914361600114688 |
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| author | Efimov, Timofey Venkatakrishnan, Singanallur Hossain, Maliha Duba-Sullivan, Haley Ziabari, Amirkoushyar |
| author_facet | Efimov, Timofey Venkatakrishnan, Singanallur Hossain, Maliha Duba-Sullivan, Haley Ziabari, Amirkoushyar |
| contents | Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to collect large amounts of measurements even for a single scan, leading to sparse data sets from which it is challenging to obtain high quality reconstructions even with diffusion models. One strategy to mitigate this challenge is to leverage a complementary, easily available imaging modality; however, such approaches typically require retraining the diffusion model with large datasets. In this work, we propose incorporating an additional modality without retraining the diffusion prior, enabling accelerated imaging of costly modalities. We further examine the impact of imperfect side modalities on cross-modal guidance. Our method is evaluated on sparse-view neutron computed tomography, where reconstruction quality is substantially improved by incorporating X-ray computed tomography of the same samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01253 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography Efimov, Timofey Venkatakrishnan, Singanallur Hossain, Maliha Duba-Sullivan, Haley Ziabari, Amirkoushyar Computer Vision and Pattern Recognition Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to collect large amounts of measurements even for a single scan, leading to sparse data sets from which it is challenging to obtain high quality reconstructions even with diffusion models. One strategy to mitigate this challenge is to leverage a complementary, easily available imaging modality; however, such approaches typically require retraining the diffusion model with large datasets. In this work, we propose incorporating an additional modality without retraining the diffusion prior, enabling accelerated imaging of costly modalities. We further examine the impact of imperfect side modalities on cross-modal guidance. Our method is evaluated on sparse-view neutron computed tomography, where reconstruction quality is substantially improved by incorporating X-ray computed tomography of the same samples. |
| title | Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.01253 |