- By:
- Efimov, Timofey D; Singanallur Vaidyanathan, Venkatakrishnan ; Hossain, Maliha ; Sullivan, Haley E; Ziabari, Amir K
- Page Number:
- 11562-11566
- Book Title:
- ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publication Date:
- May 20, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Conference Location:
- Barcelona, Spain
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/ICASSP55912.2026.11460705
Abstract
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.
