Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced...

Area of Science:

• Biomedical imaging
• Medical physics
• Computational imaging

Background:

• Photoacoustic tomography (PAT) is a hybrid imaging modality combining optical and acoustic principles.
• Sparse-view sampling in PAT leads to degraded image resolution and artifacts with conventional reconstruction.
• Existing methods struggle with insufficient photoacoustic signal acquisition.

Purpose of the Study:

• To develop a novel sinogram-domain priors guided reconstruction method for extremely sparse-view photoacoustic tomography.
• To enhance image quality and reduce artifacts in PAT under limited-view conditions.
• To leverage enhanced diffusion models for improved prior learning in reconstruction.

Main Methods:

• Proposed a novel sinogram-domain priors guided reconstruction method for photoacoustic tomography.
• Utilized an enhanced diffusion model to learn prior information from full-ring sinogram data (512 projections).
• Employed iterative reconstruction with prior information as a constraint in least-squares optimization and delay-and-sum for image domain transformation.

Main Results:

• The proposed method significantly improved image quality compared to U-Net and delay-and-sum methods.
• For in vivo data (32 projections), sinogram structural similarity improved by ~21% over U-Net.
• Image structural similarity increased by ~51% over U-Net and ~84% over delay-and-sum.

Conclusions:

• Sinogram-domain reconstruction for photoacoustic tomography effectively enhances sparse-view imaging capabilities.
• The proposed method offers superior performance in mitigating artifacts and improving resolution in sparse-view PAT.
• This advancement holds potential for expanding the clinical and research applications of photoacoustic tomography.