Fractional regularization improves photoacoustic image reconstruction

Jaya Prakash; Dween Sanny; Sandeep Kalva; Manojit Pramanik; Phaneendra Yalavarthy

doi:10.1117/12.2582486

5 March 2021 Fractional regularization improves photoacoustic image reconstruction

Jaya Prakash, Dween Sanny, Sandeep Kalva, Manojit Pramanik, Phaneendra Yalavarthy

Proceedings Volume 11642, Photons Plus Ultrasound: Imaging and Sensing 2021; 1164236 (2021) https://doi.org/10.1117/12.2582486
Event: SPIE BiOS, 2021, Online Only

Conference Poster

Abstract

The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, l1 -norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data. This work introduces a fractional framework to weigh the singular values with respect to a fractional power. This fractional framework was implemented for Tikhonov, l1-norm, and total-variation regularization schemes. The fractional framework outperformed the standard regularization schemes by 54% in terms of observed contrast/signal-to-noise-ratio.

Conference Presentation

Citation Download Citation

Jaya Prakash, Dween Sanny, Sandeep Kalva, Manojit Pramanik, and Phaneendra Yalavarthy "Fractional regularization improves photoacoustic image reconstruction", Proc. SPIE 11642, Photons Plus Ultrasound: Imaging and Sensing 2021, 1164236 (5 March 2021); https://doi.org/10.1117/12.2582486

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available