Dictionary learning technique enhances signal in LED-based photoacoustic imaging

Parastoo Farnia; Ebrahim Najafzadeh; Ali Hariri; Saeedeh Navaei Lavasani; Bahador Makkiabadi; Alireza Ahmadian; Jesse V Jokerst

doi:10.1364/BOE.387364

Dictionary learning technique enhances signal in LED-based photoacoustic imaging

Biomed Opt Express. 2020 Apr 14;11(5):2533-2547. doi: 10.1364/BOE.387364. eCollection 2020 May 1.

Authors

Parastoo Farnia^{1

2

3}, Ebrahim Najafzadeh^{1

2

3}, Ali Hariri⁴, Saeedeh Navaei Lavasani^{2

5}, Bahador Makkiabadi^{1

2}, Alireza Ahmadian^{1

2}, Jesse V Jokerst^{4

6

7}

Affiliations

¹ Medical Physics and Biomedical Engineering Department, Faculty of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
² Research Centre of Biomedical Technology and Robotics (RCBTR), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
³ These authors contributed equally to this paper.
⁴ Department of Nano Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92092, USA.
⁵ Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
⁶ Materials Science and Engineering Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92092, USA.
⁷ Department of Radiology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92092, USA.

Abstract

There has been growing interest in low-cost light sources such as light-emitting diodes (LEDs) as an excitation source in photoacoustic imaging. However, LED-based photoacoustic imaging is limited by low signal due to low energy per pulse-the signal is easily buried in noise leading to low quality images. Here, we describe a signal de-noising approach for LED-based photoacoustic signals based on dictionary learning with an alternating direction method of multipliers. This signal enhancement method is then followed by a simple reconstruction approach delay and sum. This approach leads to sparse representation of the main components of the signal. The main improvements of this approach are a 38% higher contrast ratio and a 43% higher axial resolution versus the averaging method but with only 4% of the frames and consequently 49.5% less computational time. This makes it an appropriate option for real-time LED-based photoacoustic imaging.

Abstract

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