Deep learning improves contrast in low-fluence photoacoustic imaging
Year: 2020
Type: article
Abstract: 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.
Source: Biomedical Optics Express
DOI: 10.1364/boe.395683
Institution: University of California San Diego
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,913
Awards: NSF/FDA SIR: Morphologically Complex Tissue-Mimicking Phantoms for Evaluating Tissue Scattering Artifacts in Photoacoustic Imaging, G4025325374, Advanced Polymeric Tissue-Mimicking Materials and Phantoms for Evaluation of Multispectral Photoacoustic Imaging Systems, Non-Invasive Detection and Staging of Decubitus and Diabetic Ulcers, G5510209804, A Miniaturized Tool for Ultrasound Quantification of Periodontal Disease, Therapeutic Drug Monitoring with a Wearable Ultrasound-based Sensor, G8917395279, G921180997
OpenAlex ID: W3017237793