Model-Based Reconstruction of Large Three-Dimensional Optoacoustic Datasets

Area of Science:

• Medical Imaging
• Computational Imaging
• Biomedical Engineering

Background:

• Iterative model-based algorithms offer superior accuracy in optoacoustic (photoacoustic) tomographic reconstructions compared to standard back-projection.
• Three-dimensional (3D) model-based inversion faces challenges due to high computational demands and memory usage.
• Current GPU-accelerated methods reduce memory but remain computationally intensive for large 3D datasets.

Purpose of the Study:

• To reduce the computational complexity and memory overhead of 3D model-based iterative inversion algorithms for optoacoustic tomography.
• To develop faster and more practical methods for reconstructing large-scale 3D optoacoustic datasets.

Main Methods:

• Decomposing the 3D model matrix into a sparse matrix and a cyclic convolution matrix to decrease computational complexity.
• Implementing image reconstruction via multiplication with the transpose of the decomposed model matrix, simulating a large regularization parameter.
• Comparing the performance of the new iterative method and the transpose multiplication approach against standard back-projection and a GPU-based model-based method using in vivo data.

Main Results:

• The novel iterative method achieved an approximate order-of-magnitude acceleration in reconstruction time compared to existing methods.
• Reconstruction using the transpose of the model matrix demonstrated a speed comparable to standard back-projection.
• Both proposed methods address the computational bottlenecks of 3D optoacoustic tomography.

Conclusions:

• The proposed matrix decomposition significantly enhances the efficiency of 3D model-based iterative optoacoustic tomography.
• These optimized methods make high-quality 3D optoacoustic image reconstruction more feasible for extensive datasets.
• The findings pave the way for broader clinical and research applications of quantitative optoacoustic imaging.