Blood flow recovery from subsampled data in photoacoustic microscopy

Sushanth G. Sathyanarayana; Zhuoying Wang; Naidi Sun; Bo Ning; Song Hu; John A. Hossack

doi:10.1109/IUS52206.2021.9593787

Blood flow recovery from subsampled data in photoacoustic microscopy

Sushanth G. Sathyanarayana, Zhuoying Wang, Naidi Sun, Bo Ning, Song Hu, John A. Hossack

Research output: Contribution to journal › Conference article › peer-review

Abstract

Photo acoustic microscopy (PAM) achieves high contrast, intravital imaging of the microvasculature by utilizing the specificity of endogenous optical absorption. PAM has been further augmented by using loss of correlation (LoC) methods to image blood flow. However, estimating blood flow using LoC methods necessitates dense spatial sampling which increases laser fluence. To address concerns over the increase in laser fluence, we develop a sparse modeling algorithm to reconstruct blood flow in PAM from downsampled data. The proposed method is superior to reconstruction by bicubic interpolation, exhibiting an error of 5.6 ± 3.4% in vivo compared to 33.4 ± 32.7% for bicubic interpolation, for data downsampled eight times.

Original language	English
Journal	IEEE International Ultrasonics Symposium, IUS
DOIs	https://doi.org/10.1109/IUS52206.2021.9593787
Publication status	Published - 2021
Externally published	Yes
Event	2021 IEEE International Ultrasonics Symposium, IUS 2021 - Virtual, Online, China Duration: 11 Sept 2011 → 16 Sept 2011

Keywords

Photoacoustic microscopy
sparse modeling

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10.1109/IUS52206.2021.9593787

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title = "Blood flow recovery from subsampled data in photoacoustic microscopy",

abstract = "Photo acoustic microscopy (PAM) achieves high contrast, intravital imaging of the microvasculature by utilizing the specificity of endogenous optical absorption. PAM has been further augmented by using loss of correlation (LoC) methods to image blood flow. However, estimating blood flow using LoC methods necessitates dense spatial sampling which increases laser fluence. To address concerns over the increase in laser fluence, we develop a sparse modeling algorithm to reconstruct blood flow in PAM from downsampled data. The proposed method is superior to reconstruction by bicubic interpolation, exhibiting an error of 5.6 ± 3.4% in vivo compared to 33.4 ± 32.7% for bicubic interpolation, for data downsampled eight times.",

keywords = "Photoacoustic microscopy, sparse modeling",

author = "Sathyanarayana, {Sushanth G.} and Zhuoying Wang and Naidi Sun and Bo Ning and Song Hu and Hossack, {John A.}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Ultrasonics Symposium, IUS 2021 ; Conference date: 11-09-2011 Through 16-09-2011",

year = "2021",

doi = "10.1109/IUS52206.2021.9593787",

language = "English",

journal = "IEEE International Ultrasonics Symposium, IUS",

issn = "1948-5719",

publisher = "IEEE Computer Society",

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TY - JOUR

T1 - Blood flow recovery from subsampled data in photoacoustic microscopy

AU - Sathyanarayana, Sushanth G.

AU - Wang, Zhuoying

AU - Sun, Naidi

AU - Ning, Bo

AU - Hu, Song

AU - Hossack, John A.

PY - 2021

Y1 - 2021

N2 - Photo acoustic microscopy (PAM) achieves high contrast, intravital imaging of the microvasculature by utilizing the specificity of endogenous optical absorption. PAM has been further augmented by using loss of correlation (LoC) methods to image blood flow. However, estimating blood flow using LoC methods necessitates dense spatial sampling which increases laser fluence. To address concerns over the increase in laser fluence, we develop a sparse modeling algorithm to reconstruct blood flow in PAM from downsampled data. The proposed method is superior to reconstruction by bicubic interpolation, exhibiting an error of 5.6 ± 3.4% in vivo compared to 33.4 ± 32.7% for bicubic interpolation, for data downsampled eight times.

AB - Photo acoustic microscopy (PAM) achieves high contrast, intravital imaging of the microvasculature by utilizing the specificity of endogenous optical absorption. PAM has been further augmented by using loss of correlation (LoC) methods to image blood flow. However, estimating blood flow using LoC methods necessitates dense spatial sampling which increases laser fluence. To address concerns over the increase in laser fluence, we develop a sparse modeling algorithm to reconstruct blood flow in PAM from downsampled data. The proposed method is superior to reconstruction by bicubic interpolation, exhibiting an error of 5.6 ± 3.4% in vivo compared to 33.4 ± 32.7% for bicubic interpolation, for data downsampled eight times.

KW - Photoacoustic microscopy

KW - sparse modeling

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U2 - 10.1109/IUS52206.2021.9593787

DO - 10.1109/IUS52206.2021.9593787

M3 - Conference article

AN - SCOPUS:85122857573

SN - 1948-5719

JO - IEEE International Ultrasonics Symposium, IUS

JF - IEEE International Ultrasonics Symposium, IUS

T2 - 2021 IEEE International Ultrasonics Symposium, IUS 2021

Y2 - 11 September 2011 through 16 September 2011

ER -

Blood flow recovery from subsampled data in photoacoustic microscopy

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