Abstract
Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners.
Highlights
Motion during signal acquisition is known to result in image blurring and can further hinder proper registration of images acquired by different modalities [1,2,3,4]
We demonstrate that motion rejection in optoacoustic tomography (OAT) can effectively be performed on-the-fly, before image reconstruction
The algorithm suggested in this work aims at motion rejection in OAT systems based on a multi-frame acquisition of time-resolved pressure signals with transducer arrays
Summary
Motion during signal acquisition is known to result in image blurring and can further hinder proper registration of images acquired by different modalities [1,2,3,4]. Respiratory motion compensation in tomographic imaging methods is often based on a gated acquisition assisted by physiological triggers, e.g., an electrocardiogram (ECG) signal. Retrospective gating correlates between the acquired images and physiological triggers during post-processing [5]. More advanced retrospective approaches are based on self-gated methods where the physiological trigger is extracted from the image data itself [6,7,8]. An alternative solution consists in motion tracking of specific points and subsequent correction with rigid-body transformations [9]. More sophisticated models are generally required to estimate and correct for the effects of respiratory motion [10]
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