# Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography
## 1 Introduction  
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. In prospective gating, the data is acquired during a limited time window when minimal, or no motion occurs. Alternately, 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]. In some parts of the body, such as the thoracic region, non-rigid motion is further produced. Thus, more sophisticated models are generally required to estimate and correct for the effects of respiratory motion [10].  
High-frame-rate imaging modalities can avoid motion if sub-pixel displacements are produced during the effective image integration time. Particularly, optoacoustic tomography (OAT) can render 2D and 3D images via excitation of an entire volume with a single laser pulse [11]. This corresponds toan effective integration time in the order of the pulse duration, typically a few nanoseconds. This way, the tissue motion can be \"frozen\" much more efficiently than most other imaging modalities. OAT has found applicability in biological studies demanding high-frame-rate imaging, such as characterization of cardiac dynamics [12], mapping of neuronal activity [13], monitoring hemodynamic patterns in tumors [14] or visualization of freely-behaving animals [15]. Moreover, real-time imaging has been paramount in the successful translation of OAT to render motion-free images acquired in a handheld mode [16]. While motion correction in OAT might not be relevant for images rendered with a single laser pulse, acquisition of multiple frames is still required in many applications, e.g., for rendering volumetric data from multiple cross-sections or for extending the effective field of view (FOV) of a given imaging system [17]. Multiple frames are also required for multi-spectral optoacoustic tomography (MSOT) applications, where mapping of intrinsic tissue chromophores or extrinsically administered agents is achieved via spectral or temporal unmixing [18; 19; 20; 21]. Cardiac and breathing motion could readily be captured by OAT systems running at frame rates of tens of hertz [22; 23], and several approaches have been suggested to mitigate motion artefacts in applications involving multi-frame data analysis. For instance, respiratory motion gating was suggested by simultaneously capturing the animal's respiratory waveforms [24]. Motion correction was alternatively performed with 3D rigid-body transformations [25] and with free-form deformation models [26]. Models of body motion have also been suggested for other types of scanning-based systems [27; 28]. Additionally, motion suppression could be achieved by reducing the delay between consecutive pulsed light excitations [29; 30], which requires dedicated laser systems.  
In this work, we demonstrate that motion rejection in OAT can effectively be performed on-the-fly, before image reconstruction. The suggested approach consists in clustering a sequence of OAT frames that employs the raw time-resolved signals without involving computationally and memory extensive post-processing. This represents an important advantage over other known approaches operating in the image domain [31].