Abstract
Photoacoustic imaging relies on diffused photons for optical contrast and diffracted ultrasound for high resolution. As a tomographic imaging modality, often an inverse problem of acoustic diffraction needs to be solved to reconstruct a photoacoustic image. The inverse problem is complicated by the fact that the acoustic properties, including the speed of sound distribution, in the image field of view are unknown. During reconstruction, subtle changes of the speed of sound in the acoustic ray path may accumulate and give rise to noticeable blurring in the image. Thus, in addition to the ultrasound detection bandwidth, inaccurate acoustic modeling, especially the unawareness of the speed of sound, defines the image resolution and influences image quantification. Here, we proposed a method termed feature coupling to jointly reconstruct the speed of sound distribution and a photoacoustic image with improved sharpness, at no additional hardware cost. Simulations, phantom studies, and in vivo experiments demonstrated the effectiveness and reliability of our method.
Highlights
As a high resolution optical imaging modality that overcomes the optical diffusion limit, photoacoustic (PA) tomography (PAT) can form images with high spatial resolution and sensitivity for endogenous and exogenous chromophores [1]
We demonstrated the effectiveness and robustness of feature coupling (FC) through numerical simulations, phantom studies, and in vivo experiments, on a panoramic (360° in-plane detection) PAT system
The calculation of time of flight (TOF) needs to take into account distinct speed of sound (SOS) values of different part, and the formula of back-projection can be modified as p(b) 0 (r)
Summary
As a high resolution optical imaging modality that overcomes the optical diffusion limit, photoacoustic (PA) tomography (PAT) can form images with high spatial resolution and sensitivity for endogenous and exogenous chromophores [1]. It is more tempting to jointly reconstruct the distributions of the SOS and the PA initial pressure (IP) by minimizing the difference between model-based predictions and experimental data, without resorting to additional SOS measurement hardware [17,18,19,20,21] Most of these methods were only validated in simulations. Zhang et al [29] obtained a single SOS value by minimizing the discrepancy between half-time reconstructions This method is not based on gradient search either and its computational complexity depends exponentially on the number of unknowns. Our method, termed feature coupling (FC), iteratively optimizes an SOS distribution by maximizing the similarity of images from the partial arrays. Similar systems have shown significance in both preclinical [30] and clinical applications [31]
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