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Author Spotlight: Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

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Multispectral Interlaced Sparse Sampling Photoacoustic Tomography.

Xipan Li, Shuangyang Zhang, Jian Wu

IEEE Transactions on Medical Imaging

|August 4, 2020

Summary

Interlaced sparse sampling (ISS) photoacoustic tomography (PAT) reduces data size by using fewer detectors. This novel method achieves high-quality spectral un-mixing and image reconstruction comparable to dense sampling techniques.

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Area of Science:

Biomedical imaging
Optical imaging
Photoacoustic tomography

Background:

Multispectral photoacoustic tomography (PAT) uses spectral unmixing to map tissue chromophores based on absorption spectra at multiple wavelengths.
Large datasets from multispectral acquisition necessitate data reduction methods like sparse sampling.
Sparse sampling in PAT faces challenges in image reconstruction due to limited angular coverage, amplifying artifacts during spectral unmixing.

Purpose of the Study:

To introduce a novel interlaced sparse sampling (ISS) PAT method for efficient and accurate multispectral image reconstruction.
To address the limitations of sparse sampling in PAT by improving angular coverage and reducing artifacts.
To develop a reconstruction algorithm that leverages anatomical priors for enhanced spectral unmixing.

Main Methods:

Implemented a scanning-based acquisition scheme where a sparse detector array rotates with changing illumination wavelengths for dense angular coverage.
Developed an image reconstruction algorithm utilizing a self-generated anatomical prior image derived from ISS data.
Employed a specialized iterative imaging model incorporating the anatomical prior to guide PAT image computation.

Main Results:

ISS-PAT achieved comparable image reconstruction and spectral unmixing results to conventional dense sampling methods, even at 1/6 or 1/8 sampling rates.
The self-generated prior image effectively fused multispectral and angular information, yielding rich anatomical features with minimal artifacts.
Simulations, phantom studies, and in vivo experiments validated the method's performance.

Conclusions:

ISS-PAT offers a viable solution for reducing data volume in multispectral photoacoustic tomography while maintaining high image quality.
The developed reconstruction strategy effectively mitigates artifacts associated with sparse sampling.
This method holds promise for improved diagnostic capabilities in biomedical imaging.