Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction

Mohamed A. Naser; Diego R.T. Sampaio; Nina M. Munoz; Cayla A. Wood; Trevor M. Mitcham; Wolfgang Stefan; Konstantin V. Sokolov; Theo Z. Pavan; Rony Avritscher; Richard R. Bouchard

doi:10.1109/TMI.2018.2867602

Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction

Mohamed A. Naser, Diego R.T. Sampaio, Nina M. Munoz, Cayla A. Wood, Trevor M. Mitcham, Wolfgang Stefan, Konstantin V. Sokolov, Theo Z. Pavan, Rony Avritscher, Richard R. Bouchard

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

As photoacoustic (PA) imaging makes its way into the clinic, the accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO ₂ ) in tissue. With data from a Vevo LAZR system, performance of our LFC approach was assessed in ex vivo blood targets (37.6%-99.6% SO ₂ ) and in vivo rat arteries. Estimation error of absolute SO ₂ and change in SO ₂ reduced from 10.1% and 6.4%, respectively, without LFC to 2.8% and 2.0%, respectively, with LFC, while the accuracy of the LFC method was correlated with the number of wavelengths acquired. This paper demonstrates the need for an SNR-regularized LFC to accurately quantify SO ₂ with PA imaging.

Original language	English (US)
Article number	8458163
Pages (from-to)	561-571
Number of pages	11
Journal	IEEE Transactions on Medical Imaging
Volume	38
Issue number	2
DOIs	https://doi.org/10.1109/TMI.2018.2867602
State	Published - Feb 2019

Keywords

Photoacoustic imaging
finite element modeling
image reconstruction methods
liver
ultrasound

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TMI.2018.2867602

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Naser, M. A., Sampaio, D. R. T., Munoz, N. M., Wood, C. A., Mitcham, T. M., Stefan, W., Sokolov, K. V., Pavan, T. Z., Avritscher, R., & Bouchard, R. R. (2019). Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction. IEEE Transactions on Medical Imaging, 38(2), 561-571. Article 8458163. https://doi.org/10.1109/TMI.2018.2867602

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title = "Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction",

abstract = " As photoacoustic (PA) imaging makes its way into the clinic, the accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO 2 ) in tissue. With data from a Vevo LAZR system, performance of our LFC approach was assessed in ex vivo blood targets (37.6%-99.6% SO 2 ) and in vivo rat arteries. Estimation error of absolute SO 2 and change in SO 2 reduced from 10.1% and 6.4%, respectively, without LFC to 2.8% and 2.0%, respectively, with LFC, while the accuracy of the LFC method was correlated with the number of wavelengths acquired. This paper demonstrates the need for an SNR-regularized LFC to accurately quantify SO 2 with PA imaging.",

keywords = "Photoacoustic imaging, finite element modeling, image reconstruction methods, liver, ultrasound",

author = "Naser, {Mohamed A.} and Sampaio, {Diego R.T.} and Munoz, {Nina M.} and Wood, {Cayla A.} and Mitcham, {Trevor M.} and Wolfgang Stefan and Sokolov, {Konstantin V.} and Pavan, {Theo Z.} and Rony Avritscher and Bouchard, {Richard R.}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.",

year = "2019",

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doi = "10.1109/TMI.2018.2867602",

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volume = "38",

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AU - Wood, Cayla A.

AU - Mitcham, Trevor M.

AU - Stefan, Wolfgang

AU - Sokolov, Konstantin V.

AU - Pavan, Theo Z.

AU - Avritscher, Rony

AU - Bouchard, Richard R.

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N2 - As photoacoustic (PA) imaging makes its way into the clinic, the accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO 2 ) in tissue. With data from a Vevo LAZR system, performance of our LFC approach was assessed in ex vivo blood targets (37.6%-99.6% SO 2 ) and in vivo rat arteries. Estimation error of absolute SO 2 and change in SO 2 reduced from 10.1% and 6.4%, respectively, without LFC to 2.8% and 2.0%, respectively, with LFC, while the accuracy of the LFC method was correlated with the number of wavelengths acquired. This paper demonstrates the need for an SNR-regularized LFC to accurately quantify SO 2 with PA imaging.

AB - As photoacoustic (PA) imaging makes its way into the clinic, the accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO 2 ) in tissue. With data from a Vevo LAZR system, performance of our LFC approach was assessed in ex vivo blood targets (37.6%-99.6% SO 2 ) and in vivo rat arteries. Estimation error of absolute SO 2 and change in SO 2 reduced from 10.1% and 6.4%, respectively, without LFC to 2.8% and 2.0%, respectively, with LFC, while the accuracy of the LFC method was correlated with the number of wavelengths acquired. This paper demonstrates the need for an SNR-regularized LFC to accurately quantify SO 2 with PA imaging.

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Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction

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