Spatiotemporal image reconstruction for photoacoustic computed tomography of tumor vascular perfusion in murine models

Abstract

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.