研究目的
To develop an inversion method that reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging by using entropy maximization and structural prior-based fluence correction.
研究成果
The entropy maximization scheme delivers non-negative reconstructions with improved image quality, accurately resolving structures in numerical simulations, phantoms, and in-vivo samples. It shows potential for quantitative optoacoustic imaging and applications in pre-clinical and translational imaging.
研究不足
The method may be influenced by experimental factors such as transducer impulse response, limited view detection geometry, and noise. It assumes uniform speed of sound and homogenous optical properties, which may not hold in all biological tissues. Future work could integrate more accurate light propagation models and handle multi-wavelength scenarios for better quantitative analysis.
1：Experimental Design and Method Selection:
The study employs a maximum entropy-based algorithm for optoacoustic tomography reconstruction, using logarithmic regularization to ensure non-negative values. It involves numerical simulations, experimental phantom studies, and in-vivo animal imaging to validate the method.
2：Sample Selection and Data Sources:
Data sources include numerical simulations of a breast phantom, experimental star-shaped phantoms with tissue-mimicking materials, and in-vivo datasets from mice (abdomen and brain regions).
3：List of Experimental Equipment and Materials:
Equipment includes an MSOT scanner (MSOT256-TF, iThera Medical GmbH), transducers, laser systems, and materials like Intralipid, India ink, and agar for phantoms.
4：Experimental Procedures and Operational Workflow:
Procedures involve acquiring boundary pressure data, reconstructing images using conjugate gradient methods with entropy maximization, performing fluence correction using finite volume methods, and evaluating results with metrics like sharpness and RMSE.
5：Data Analysis Methods:
Analysis includes computing sharpness metrics, root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and using L-curve methods for regularization parameter selection.
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