Photoacoustic image reconstruction from few-detector and limited-angle data

Area of Science:

• Biomedical imaging
• Medical physics
• Image reconstruction

Background:

• Photoacoustic tomography (PAT) is a promising non-invasive imaging modality.
• Conventional PAT reconstruction methods struggle with limited data, leading to artifacts.
• Few-detector and limited-angle measurements are common challenges in PAT.

Purpose of the Study:

• To develop an enhanced iterative reconstruction algorithm for photoacoustic tomography.
• To address image distortions and artifacts caused by limited measurement data.
• To improve the performance of PAT in scenarios with few detectors or limited angular coverage.

Main Methods:

• Implementation of a total-variation-minimization (TVM) technique within an iterative reconstruction framework.
• Development of an algorithm to enhance photoacoustic image reconstruction.
• Validation using phantom experiments with few-detector and limited-angle data acquisition.

Main Results:

• The TVM-enhanced algorithm successfully reduced artifacts in photoacoustic images.
• Excellent image reconstruction quality was achieved even with sparse measurement data.
• Phantom experiments confirmed the effectiveness of the proposed method.

Conclusions:

• The total-variation-minimization enhanced iterative algorithm offers superior performance for photoacoustic tomography.
• This method overcomes limitations of conventional algorithms in few-detector and limited-angle scenarios.
• The developed technique holds significant potential for advancing biomedical imaging applications of PAT.