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U-Net enhanced real-time LED-based photoacoustic imaging
  • Avijit Paul,
  • Srivalleesha Mallidi
Avijit Paul
Tufts University
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Srivalleesha Mallidi
Tufts University

Corresponding Author:srivalleesha.mallidi@tufts.edu

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Abstract

Photoacoustic (PA) imaging is hybrid imaging modality with good optical contrast and spatial resolution. Portable, cost-effective, smaller footprint LEDs are rapidly becoming important PA optical sources. However, the key challenge faced by the LED-based systems is the low fluence that is generally compensated by high frame averaging; consequently reducing acquisition frame-rate. In this study, we present a simple deep learning U-Net framework that enhances the signal-to-noise ratio (SNR) and contrast of the low number of frame-averaged PA images. The SNR increased by approximately 4-fold for both in-class in vitro phantoms (4.39 ± 2.55) and out-of-class in vivo models (4.27 ± 0.87). We also demonstrate the noise invariancy of the network and discuss the downsides (blurry outcome and fails to reduce the salt & pepper noise). Overall, the developed U-Net framework can provide a real-time image enhancement platform for clinically translatable low-cost and low-energy light source-based PA imaging systems.
08 Nov 2023Submitted to Journal of Biophotonics
09 Nov 2023Submission Checks Completed
09 Nov 2023Assigned to Editor
09 Nov 2023Review(s) Completed, Editorial Evaluation Pending
09 Nov 2023Reviewer(s) Assigned
19 Feb 20241st Revision Received
19 Feb 2024Reviewer(s) Assigned
13 Mar 2024Review(s) Completed, Editorial Evaluation Pending
17 Mar 2024Editorial Decision: Accept