Score-Based Diffusion Models for Photoacoustic Tomography Image Reconstruction
This work addresses image reconstruction challenges in photoacoustic tomography for medical imaging applications, representing an incremental improvement by applying an existing method to a new domain.
The authors tackled the ill-posed inverse problem of reconstructing images from limited photoacoustic tomography measurements by using score-based diffusion models, resulting in a method that incorporates a learned prior on simulated vessel structures and remains robust to varying transducer sparsity conditions.
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.