Optoacoustic Tomography Using Accelerated Sparse Recovery and Coherence Factor Weighting

Area of Science:

• Medical Imaging
• Computational Imaging
• Biomedical Engineering

Background:

• Optoacoustic (photoacoustic) tomography (OAT) enables non-invasive imaging but requires significant computational resources for image reconstruction.
• Sparse recovery algorithms offer accurate OAT reconstruction from limited data but are computationally intensive.
• Existing methods face challenges with speed and image artifacts, limiting clinical applicability.

Purpose of the Study:

• To accelerate the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) for OAT image reconstruction.
• To improve image fidelity and reduce artifacts in OAT using a novel weighted approach.
• To evaluate the performance of the enhanced SALSA method against existing sparse recovery techniques.

Main Methods:

• Implementation of a modified SALSA incorporating least-square QR inversion for faster convergence.
• Introduction of a coherence factor weighting to enhance reconstruction quality.
• Validation using phantom and in vivo optoacoustic (photoacoustic) tomography data.

Main Results:

• The accelerated SALSA method demonstrated significantly faster convergence compared to standard sparse recovery algorithms.
• Image reconstruction exhibited reduced artifacts and improved fidelity when using coherence factor weighting.
• Both phantom and in vivo results confirmed the superior performance of the proposed method.

Conclusions:

• The enhanced SALSA algorithm offers a computationally efficient and accurate solution for limited-view OAT image reconstruction.
• Coherence factor weighting further improves image quality, making the method suitable for clinical applications.
• This accelerated approach advances the field of optoacoustic (photoacoustic) tomography imaging.