Room: ePoster Forums
Purpose: To study the quantitative relationship between the oxidation saturation of the tissue and the grayscale value of reconstructed image by the Time-Reversal (TR) image reconstruction algorithms based on the first-order k-space model for photoacoustic imaging.
Methods: The first-order k-space model in Matlab (MathWorks Inc.), k-Wave toolkit, was used to simulate the reconstruction of some 2-D photoacoustic images. The photoacoustic signal is determined by the absorption and distribution of light energy. In the original image we used, the capacity of absorbing light energy in the background is much higher than that in the target area. The photoacoustic signal generated by the background reaches the sensor before the signal from the target area, which results the photoacoustic signal from the target area is covered by the signal from background area and leads to severe distortion of the reconstructed image. To avoid this problem, a grayscale inversion program was added before inputting the original image to the code.
In the process of simulated reconstruction, the difference between the red and blue-purple grayscale values can be used to simulate the two situations of high and low oxygen saturation, a color modified program was added to change the color of tumor area to the assumption color, and then the TR reconstruction algorithm were carried out to observe the influence of the oxygen saturation on the reconstruction.
Results: Comparing the reconstructed images from different oxygen saturations. The grayscale value of the part of tumor area in the red group reconstructed image is higher than that of the blue-purple group.
Conclusion: The oxygen saturation could significantly affect the grayscale value of reconstructed image, and the reconstructed image could be quantitatively evaluated the relative oxidic concentration of the local tissue. A simulation program was developed to investigate the influence of the tissue oxidation level.
Funding Support, Disclosures, and Conflict of Interest: This study was supported by the university student innovation project of Hefei University of Technology (2018CXCYS209).