TY - JOUR
T1 - Tomographic breathing detection
T2 - A method to noninvasively assess in situ respiratory dynamics
AU - O'Kelly, Devin
AU - Zhou, Heling
AU - Mason, Ralph P.
N1 - Funding Information:
The research was supported in part by the Cancer Prevention and Research Institute of Texas Grant Nos. RP140399 and RP120753-03, the National Institutes of Health (NIH) Grant No. 1 P50 CA196516, and the assistance of the Southwestern Small Animal Imaging Resource through the National Cancer Institute Cancer Center Support Grant No. 1P30 CA142543. iThera MSOT was purchased under NIH Grant No. 1 S10 OD018094-01A1.
Publisher Copyright:
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).
PY - 2018/5/1
Y1 - 2018/5/1
N2 - Physiological monitoring is a critical aspect of in vivo experimentation, particularly imaging studies. Physiological monitoring facilitates gated acquisition of imaging data and more robust experimental interpretation but has historically required additional instrumentation that may be cumbersome. As frame rates have increased, imaging methods have been able to capture ever more rapid dynamics, passing the Nyquist sampling rate of most physiological processes and allowing the capture of motion, such as breathing. With this transition, image artifacts have also changed their nature; rather than intraframe motion causing blurring and deteriorating resolution, interframe motion does not affect individual frames and may be recovered as useful information from an image time series. We demonstrate a method that takes advantage of interframe movement for detection of gross physiological motion in real-time image sequences. We further demonstrate the ability of the method, dubbed tomographic breathing detection to quantify the dynamics of respiration, allowing the capture of respiratory information pertinent to anesthetic depth monitoring. Our example uses multispectral optoacoustic tomography, but it will be widely relevant to other technologies.
AB - Physiological monitoring is a critical aspect of in vivo experimentation, particularly imaging studies. Physiological monitoring facilitates gated acquisition of imaging data and more robust experimental interpretation but has historically required additional instrumentation that may be cumbersome. As frame rates have increased, imaging methods have been able to capture ever more rapid dynamics, passing the Nyquist sampling rate of most physiological processes and allowing the capture of motion, such as breathing. With this transition, image artifacts have also changed their nature; rather than intraframe motion causing blurring and deteriorating resolution, interframe motion does not affect individual frames and may be recovered as useful information from an image time series. We demonstrate a method that takes advantage of interframe movement for detection of gross physiological motion in real-time image sequences. We further demonstrate the ability of the method, dubbed tomographic breathing detection to quantify the dynamics of respiration, allowing the capture of respiratory information pertinent to anesthetic depth monitoring. Our example uses multispectral optoacoustic tomography, but it will be widely relevant to other technologies.
KW - breathing detection
KW - motion detection
KW - multispectral optoacoustic tomography
KW - photoacoustics
KW - respiratory motion
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U2 - 10.1117/1.JBO.23.5.056011
DO - 10.1117/1.JBO.23.5.056011
M3 - Article
C2 - 29851331
AN - SCOPUS:85048136958
SN - 1083-3668
VL - 23
JO - Journal of biomedical optics
JF - Journal of biomedical optics
IS - 5
M1 - 056011
ER -