Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction
<|reference_start|>Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction: Photoacoustic tomography (PAT) is a rapidly-evolving medical imaging modality that combines optical absorption contrast with ultrasound imaging depth. One challenge in PAT is image reconstruction with inadequate acoustic signals due to limited sensor coverage or due to the density of the transducer array. Such cases call for solving an ill-posed inverse reconstruction problem. In this work, we use score-based diffusion models to solve the inverse problem of reconstructing an image from limited PAT measurements. The proposed approach allows us to incorporate an expressive prior learned by a diffusion model on simulated vessel structures while still being robust to varying transducer sparsity conditions.<|reference_end|>
@article{dey2024score-based,
    title={Score-Based Diffusion Models for Photoacoustic Tomography Image
  Reconstruction},
    author={Sreemanti Dey, Snigdha Saha, Berthy T. Feng, Manxiu Cui, Laure
  Delisle, Oscar Leong, Lihong V. Wang, Katherine L. Bouman},
    journal={ICASSP 2024 - 2024 IEEE International Conference on Acoustics,
  Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp.
  2470-2474},
    year={2024},
    doi={10.1109/ICASSP48485.2024.10447579},
    archivePrefix={arXiv},
    eprint={2404.00471},
    primaryClass={physics.med-ph cs.CV cs.LG eess.IV}
}