The ability to perform dynamic imaging of time-varying physiological processes in small animal models is critically needed to understand the progression of human diseases and develop new therapies. Photoacoustic computed tomography (PACT) has been recognized as a promising tool for small animal imaging because of its relatively low expense, high resolution, and good signal-to-noise ratio. By exploiting the optical absorption of hemoglobin or exogenous contrast agents, dynamic PACT holds excellent potential for measuring important time-varying biomarkers like tumor vascular perfusion. Nonetheless, current dynamic PACT technologies possess several limitations. Most three-dimensional (3D) PACT imagers employ a tomographic measurement process in which a gantry containing acoustic transducers is rotated about the animal. Such a rotating gantry is advantageous for limiting the cost of the system due to the decreased number of acoustic transducers and associated electronics and for enabling convenient delivery of the light to the object. However, this presents significant challenges for dynamic image reconstruction because only a few tomographic views are available to reconstruct each temporal frame. This work presents an efficient and accurate dynamic image reconstruction method that can be deployed with widely available 3D imagers using rotating gantries. In particular, a low-rank matrix estimationbased spatiotemporal image reconstruction (LRME-STIR) algorithm is proposed. In a stylized virtual dynamic contrast-enhanced imaging study, the proposed LRME-STIR algorithm is shown to accurately recover a wellcharacterized dynamic numerical murine phantom in which tumor vascular perfusion and breathing motion are modeled.
When developing a new quantitative optoacoustic computed tomography (OAT) system for diagnostic imaging of breast cancer, objective assessments of various system designs through human trials are infeasible due to cost and ethical concerns. In prototype stages, however, different system designs can be costefficiently assessed via virtual imaging trials (VITs) employing ensembles of digital breast phantoms, i.e., numerical breast phantoms (NBPs), that convey clinically relevant variability in anatomy and optoacoustic tissue properties. Conclusions:The proposed framework will enhance the authenticity of virtual OAT studies and can be widely employed for the investigation and development of advanced image reconstruction and machine learning-based methods, as well as the objective evaluation and optimization of the OAT system designs.
Monitoring critical physiological processes in murine models like tumor vascular perfusion and its response to prospective anti-cancer treatments is a significant potential use case of dynamic photoacoustic computed tomography (PACT). Previously reported studies of dynamic PACT are based on a frame-by-frame image reconstruction (FBFIR) procedure in which full-view measurement data are assumed to be rapidly acquired. However, many commercial three-dimensional PACT imagers acquire measurements at each tomographic view rotating the object in discrete steps. The time to collect the full-view data is limited by the rotation speed of the object holder and the laser repetition rate. Therefore, FBFIR techniques are not applicable, and there is a critical need for accurate and efficient spatiotemporal image reconstruction (STIR) techniques that can account for spatiotemporal redundancies in the object’s features. To address this, we propose a low-rank matrix estimation-based STIR technique in which the sought-after dynamic image is approximated using a semiseparable approximation in space and time. To validate the proposed method, we also develop a virtual imaging framework for PACT employing dynamic numerical mouse phantoms with a physiologically realistic respiratory motion and perfusion model. The studies demonstrated that the proposed method accurately recovers the tumor vascular perfusion and object motion.
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