Abstract
Spectral unmixing techniques for photoacoustic images are often used to isolate signal origins (e.g., blood, contrast agents, lipids). However, these techniques often require many (e.g., 12–59) wavelength transmissions for optimal performance to exploit the optical properties of different biological chromophores. Analysis of the acoustic frequency response of photoacoustic signals has the potential to provide additional discrimination of photoacoustic signals from different materials, with the added benefit of potentially requiring only a few optical wavelength emissions. This study presents our initial results testing this hypothesis in a phantom experiment, given the task of differentiating photoacoustic signals from deoxygenated hemoglobin (Hb) and methylene blue (MB). Coherence-based beamforming, principal component analysis, and nearest neighbor classification were employed to determine ground-truth labels, perform feature extraction, and classify image contents, respectively. The mean ± one standard deviation of classification accuracy was increased from 0.65 ± 0.16 to 0.88 ± 0.17 when increasing the number of wavelength emissions from one to two, respectively. When using an optimal laser wavelength pair of 710–870 nm, the sensitivity and specificity of detecting MB over Hb were 1.00 and 1.00, respectively. Results are highly promising for the differentiation of photoacoustic-sensitive materials with comparable performance to that achieved with more conventional multispectral laser wavelength approaches.
Highlights
In photoacoustic imaging, spectral unmixing techniques (Glatz et al, 2011) are often used to isolate signal origins in the fields of oxymetry (Tzoumas and Ntziachristos, 2017; Gröhl et al, 2019; Gröhl et al, 2021), reporter genes (Weissleder and Ntziachristos, 2003; Brunker et al, 2017), and molecular details (Weber et al, 2016)
Existing spectral unmixing techniques generally consist of generating an overdetermined system of equations from the signal response of each chromophore at different laser wavelengths, which can be solved with an optimization technique based on the Acoustic Frequency-Based Approach for Identification known optical absorption coefficient for each chromophore at each wavelength
1.00 varying areas of the locally weighted short-lag spatial coherence (LW-SLSC) signals and corresponding mask sizes for the methylene blue (MB) and Hb regions obtained with different laser wavelength emissions are responsible for different proportions of MB-to-Hb kernel sizes when calculating the quantitative metrics
Summary
Spectral unmixing techniques (Glatz et al, 2011) are often used to isolate signal origins in the fields of oxymetry (Tzoumas and Ntziachristos, 2017; Gröhl et al, 2019; Gröhl et al, 2021), reporter genes (Weissleder and Ntziachristos, 2003; Brunker et al, 2017), and molecular details (Weber et al, 2016). Existing spectral unmixing techniques generally consist of generating an overdetermined system of equations (i.e., more equations than variables) from the signal response of each chromophore at different laser wavelengths, which can be solved with an optimization technique based on the Acoustic Frequency-Based Approach for Identification known optical absorption coefficient for each chromophore at each wavelength. Grasso et al (2020) proposed an iterative approach to discriminate blood oxygenation levels by solving the system of equations with a nonnegative matrix factorization, which compensates for the illconditioned invertibility of the absorption coefficient matrix
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