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Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with a low-energy excitation light source

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)6416-6430
Number of pages15
JournalBiomedical Optics Express
Issue number12
Accepted/In press2 Sep 2022
Published14 Nov 2022

Bibliographical note

Funding Information: Engineering and Physical Sciences Research Council (NS/A000027/1, NS/A000049/1); Wellcome Trust (203148/Z/16Z, WT101957); Chinese Government Scholarship 202008060071. Publisher Copyright: © 2022.


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    ViewAcceptedManuscript.pdf, 3.6 MB, application/pdf

    Uploaded date:02 Sep 2022

    Version:Accepted author manuscript

    Licence:CC BY

  • boe-13-12-6416

    boe_13_12_6416.pdf, 5.78 MB, application/pdf

    Uploaded date:16 Nov 2022

    Version:Final published version

    Licence:CC BY

King's Authors


Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution, and thus holds tremendous promise for a wide range of pre-clinical and clinical applications. Compact and affordable light sources such as light-emitting diodes (LEDs) and laser diodes (LDs) are promising alternatives to bulky and expensive solid-state laser systems that are commonly used as PA light sources. These could accelerate the clinical translation of PA technology. However, PA signals generated with these light sources are readily degraded by noise due to the low optical fluence, leading to decreased signal-to-noise ratio (SNR) in PA images. In this work, a spatiotemporal singular value decomposition (SVD) based PA denoising method was investigated for these light sources that usually have low fluence and high repetition rates. The proposed method leverages both spatial and temporal correlations between radiofrequency (RF) data frames. Validation was performed on simulations and in vivo PA data acquired from human fingers (2D) and forearm (3D) using a LED-based system. Spatiotemporal SVD greatly enhanced the PA signals of blood vessels corrupted by noise while preserving a high temporal resolution to slow motions, improving the SNR of in vivo PA images by 90.3%, 56.0%, and 187.4% compared to single frame-based wavelet denoising, averaging across 200 frames, and single frame without denoising, respectively. With a fast processing time of SVD (∼50 µs per frame), the proposed method is well suited to PA imaging systems with low-energy excitation light sources for real-time in vivo applications.

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