Abstract
.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.
Highlights
During in vivo experiments, it is desirable to develop an understanding of the subject’s systemic physiology, to control for experimental aberrations and to better interpret experimental results
In magnetic resonance imaging (MRI), for example, physiological monitoring enables the acquisition of cardiac- and respiratory-gated images, enabling better interimage correlations that are not corrupted by motion
We demonstrate the concept in terms of respiratory motion observed in freely breathing anesthetized mice undergoing photoacoustic imaging during a gas breathing challenge
Summary
It is desirable to develop an understanding of the subject’s systemic physiology, to control for experimental aberrations and to better interpret experimental results. In realistic in vivo scenarios, some proportion of these images will be displaced relative to others as a result of motion caused by physiological processes, e.g., breathing, as noted in several previous publications.[3,4,5,6] Online averaging of images “smooths out” this displacement[7] but compromises resolution in the process. This is due to the fact that averaging is ostensibly intended to improve signal-to-noise ratio, according to the central limit theorem (CLT).
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