Exploring polynomial based interpolation schemes for photoacoustic tomographic image reconstruction.

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Computational Imaging

Background:

• Photoacoustic tomography (PAT) is a hybrid imaging modality combining optical excitation with ultrasound detection.
• Accurate image reconstruction in PAT is crucial for resolving anatomical structures and physiological processes.
• Polynomial-based interpolation methods are often employed in PAT for constructing the model matrix.

Purpose of the Study:

• To evaluate the performance of different polynomial-based interpolation methods in photoacoustic tomography (PAT).
• To compare image reconstruction quality using nearest-neighbor, bilinear, bicubic, and biquintic algorithms.
• To determine the optimal interpolation strategy for various source distributions in PAT.

Main Methods:

• Implementation of nearest-neighbor, bilinear, bicubic, and biquintic interpolation algorithms.
• Construction of the PAT model matrix using these interpolation schemes.
• Image reconstruction utilizing Tikhonov regularization and total variation (TV) minimization.
• Validation through numerical simulations and experimental phantom studies.

Main Results:

• Interpolation method performance was comparable for large photoacoustic (PA) sources.
• The nearest-neighbor method demonstrated superior image reconstruction for sparse PA sources.
• Bilinear, bicubic, and biquintic methods showed similar performance, particularly for dense sources.

Conclusions:

• The choice of interpolation method in PAT can impact image reconstruction quality, especially for sparse sources.
• Nearest-neighbor interpolation is a viable and potentially advantageous method for vascular imaging using PAT.
• Further investigation into interpolation's role in PAT for specific clinical applications is warranted.