3.2 | Validating U-Net architecture with out-of-class data (photoacoustic images of in vivo mouse tumors)
The subcutaneous mouse tumors were imaged with Acoustic X system under LED illumination. A representative schematic of the parameters used for low frame averaged image for a certain cross-section of the tumor has been provided. Clearly the blood vessels in the tumor that absorb the LED illumination generate photoacoustic response that is shown with yellow arrow in Fig. 4(a). The rightmost image denotes the outcome from our U-Net whereas the high frame averaged image is shown at the bottom of Fig. 4(a). Similar subcutaneous tumors have been imaged for several mice (n=8) out of which three representative images are shown in different columns (Fig. 4 (b-m)). The first row (Fig. 4 (b-d)) shows the respective ultrasound images of the tumor, second row (Fig. 4 (e-g)) denotes low number of frame averaged images of the tumors, and the last row (Fig. 4 (k-m)) has the corresponding high-number of frames averaged photoacoustic images. Our U-Net outcomes are given in the middle row (Fig. 4 (h-j)). The deep network model can remove the noise significantly enough to qualitatively be similar to the high-frame averaged image where the PA signals from the blood vessels are retained. Given the importance of enhancing SNR in our images, we compared the background noise distribution for the low number of frame averaged input images with the corresponding U-Net outcome image. In Fig. S3(c), we showed a low number of frame averaged image and the blue ROI that is considered to calculate the background noise level. The noise level of the same region in the corresponding U-Net provided output image was also calculated and plotted in Fig. S3(d). Both the high-frame averaged images and our U-Net model outcomes are visually congruent to each other with respect to noise levels where U-Net images had 7-fold lower noise (7.72 ± 3.07) than the low number of frame averaged image.