Mr. Ankit Garikipati Profile

Ankit Garikipati

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 5 March 2021 Presentation + Paper

In vivo demonstration of reflection artifact reduction in LED-based photoacoustic imaging using deep learning

Sumit Agrawal, Kerrick Johnstonbaugh, Thaarakh Suresh, Ankit Garikipati, Mithun Kuniyil Ajith Singh, Sri Phani Krishna Karri, Sri-Rajasekhar Kothapalli

Proceedings Volume 11642, 116421K (2021) https://doi.org/10.1117/12.2579082

KEYWORDS: Radium, In vivo imaging, Photoacoustic imaging, Photoacoustic spectroscopy, Tissues, Tissue optics, Acoustics, Wave propagation, Statistical modeling, Signal detection

Read Abstract +

Reflection of photoacoustic (PA) signals from strong acoustic heterogeneities in biological tissue leads to reflection artifacts (RAs) in B-mode PA images. In practice, RAs often clutter clinically obtained PA images, making the interpretation of these images difficult in the presence of hypoechoic or anechoic biological structures. Towards PA artifact removal, several researchers have exploited 1) the frequency/spectrum content of time-series photoacoustic data in order to separate the true signal from artifacts, and 2) the multi-wavelength response of photoacoustic targets, assuming that the spectral nature of RAs correlates well with their corresponding source signals. These approaches are limited to extensive offline processing and sometimes fail to correctly identify artifacts in deep tissue. This study demonstrates the use of a deep neural network with the U-Net architecture to detect and reduce RAs in B-mode PA images. In order to train the proposed deep learning model for the RA reduction task, a program is designed to randomly generate anatomically realistic digital phantoms of human fingers with the capacity to produce RAs when subjected to PA imaging. In-silico PA imaging experiments, modeling photon transport and acoustic wave propagation, on these digital finger phantoms enabled the generation of 1800 training samples. The algorithm was tested on both PA images generated from digital phantoms and in-vivo PA data acquired from human fingers using a hand-held LED-based PA imaging system. Our results suggest that robust reduction of RAs with a deep neural network is possible if the network is trained with sufficiently realistic simulated images.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years