Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging
This addresses the need for safer, more affordable clinical photoacoustic imaging by enhancing image contrast, though it appears incremental as it builds on existing denoising and neural network techniques.
The paper tackles the problem of low image quality in low-fluence photoacoustic imaging by proposing a denoising method using a multi-level wavelet-convolutional neural network, resulting in substantial improvements such as up to 4.3-fold increase in CNR and up to 1.76-fold enhanced contrast in vivo.
Low fluence illumination sources can facilitate clinical transition of photoacoustic imaging because they are rugged, portable, affordable, and safe. However, these sources also decrease image quality due to their low fluence. Here, we propose a denoising method using a multi-level wavelet-convolutional neural network to map low fluence illumination source images to its corresponding high fluence excitation map. Quantitative and qualitative results show a significant potential to remove the background noise and preserve the structures of target. Substantial improvements up to 2.20, 2.25, and 4.3-fold for PSNR, SSIM, and CNR metrics were observed, respectively. We also observed enhanced contrast (up to 1.76-fold) in an in vivo application using our proposed methods. We suggest that this tool can improve the value of such sources in photoacoustic imaging.