Recovery of Blood Flow From Undersampled Photoacoustic Microscopy Data Using Sparse Modeling

Photoacoustic microscopy (PAM) leverages the optical absorption contrast of blood hemoglobin for high-resolution, multi-parametric imaging of the microvasculature in vivo . However, to quantify the blood flow speed, dense spatial sampling is required to assess blood flow-induced loss of correlation...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2022-01, Vol.41 (1), p.103-120
Main Authors: Sathyanarayana, Sushanth G., Wang, Zhuoying, Sun, Naidi, Ning, Bo, Hu, Song, Hossack, John A.
Format: Article
Language:English
Subjects:
Blood flow
compressed sensing
Correlation
Data models
Error analysis
Fluence
Hemoglobin
Image reconstruction
Image resolution
In vivo
In vivo methods and tests
Interpolation
Lasers
Microscopy
Microvasculature
Microvessels
Modelling
Optical pulses
Photoacoustic microscopy
Photoacoustic Techniques
Pulse repetition rate
reconstruction
Recovery (Medical)
Sparse matrices
sparse modeling
Spectrum Analysis
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Summary:Photoacoustic microscopy (PAM) leverages the optical absorption contrast of blood hemoglobin for high-resolution, multi-parametric imaging of the microvasculature in vivo . However, to quantify the blood flow speed, dense spatial sampling is required to assess blood flow-induced loss of correlation of sequentially acquired A-line signals, resulting in increased laser pulse repetition rate and consequently optical fluence. To address this issue, we have developed a sparse modeling approach for blood flow quantification based on downsampled PAM data. Evaluation of its performance both in vitro and in vivo shows that this sparse modeling method can accurately recover the substantially downsampled data (up to 8 times) for correlation-based blood flow analysis, with a relative error of 12.7 ± 6.1 % across 10 datasets in vitro and 12.7 ± 12.1 % in vivo for data downsampled 8 times. Reconstruction with the proposed method is on par with recovery using compressive sensing, which exhibits an error of 12.0 ± 7.9 % in vitro and 33.86 ± 26.18 % in vivo for data downsampled 8 times. Both methods outperform bicubic interpolation, which shows an error of 15.95 ± 9.85 % in vitro and 110.7 ± 87.1 % in vivo for data downsampled 8 times.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2021.3104521