Dynamic image reconstruction to monitor tumor vascular perfusion in small animals using 3D photoacoustic computed-tomography imagers with rotating gantries

Abstract

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.

Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers

“…48,58,59 Subsequent investigations may focus on evaluating the method's efficacy in imaging complex dynamic physiological processes and quantifying relevant parameters, such as wash-in and wash-out rates for tumor vascular perfusion. 37,60 These evaluations might encompass both in-vivo and in-silico experiments, further advancing the understanding and application of the proposed method.…”

“…In numerous biomedical applications, the spatiotemporal object function of interest has been demonstrated to be effectively approximated by a small set of weights σr and functions ur(r) and vr(t), depending only on the space or time variables, known as the semiseparable approximation 29 , 35 – 40 Consequently, the object function is expressed as f(r,t)≈∑r=1Rσrur(r)vr(t) 29 , 35 – 40 Leveraging the semiseparable approximation, the spatiotemporal reconstruction problem can be reduced to the problem of estimating R weights and R spatial and temporal functions.…”

“… – 40 Consequently, the object function is expressed as f(r,t)≈∑r=1Rσrur(r)vr(t) 29 , 35 – 40 Leveraging the semiseparable approximation, the spatiotemporal reconstruction problem can be reduced to the problem of estimating R weights and R spatial and temporal functions. In a discretized formulation, this is algebraically equivalent to enforcing a low-rank structure on the matrix F.…”