Accelerated high-resolution photoacoustic tomography via compressed sensing

Area of Science:

• Biomedical Imaging
• Optoacoustics
• Medical Physics

Background:

• Current 3D photoacoustic tomography (PAT) systems struggle to achieve both high spatial and temporal resolution simultaneously, limiting 4D imaging of dynamic biological processes.
• Planar Fabry-Pérot (FP) based PAT offers high resolution but requires slow, point-by-point scanning, leading to data redundancy.

Purpose of the Study:

• To develop novel PAT acquisition systems and model-based reconstruction methods for faster 4D imaging.
• To combine spatial sub-sampling acquisition schemes with variational image reconstruction using sparsity constraints.

Main Methods:

• Described and modeled two spatial sub-sampling schemes for PAT acquisition.
• Implemented sub-sampling using a Fabry-Pérot interferometer.
• Utilized model-based variational image reconstruction with spatial sparsity constraints, specifically total variation (TV) regularization enhanced by Bregman iterations.

Main Results:

• Demonstrated that combining sub-sampling acquisition with variational reconstruction yields high-resolution images from sparse data.
• Validated the approach using simulated, experimental dynamic phantom, and in vivo data.
• Achieved good spatial resolution and contrast from highly sub-sampled photoacoustic data.

Conclusions:

• Novel compressed sensing PAT devices combined with advanced reconstruction strategies dramatically increase acquisition speed.
• These methods enable high-resolution 4D photoacoustic tomography for dynamic biological processes.
• The approach reduces scanning time for sequential systems and channel count for parallelized detector arrays.