Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

TL;DR

This paper compares iterative and filtered backprojection algorithms for 3D optoacoustic tomography, demonstrating that iterative methods reduce artifacts and improve resolution, thus enhancing image quality and efficiency in biomedical imaging.

Contribution

It implements and evaluates two advanced iterative reconstruction algorithms with accurate physics modeling for 3D OAT, showing their advantages over traditional FBP methods.

Findings

Iterative algorithms reduce image artifacts.

Iterative algorithms better preserve spatial resolution.

They require less data for effective imaging.

Abstract

Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response, and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely, a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By use of quantitative measures of image quality, we demonstrate that the iterative reconstruction algorithms can mitigate image artifacts and preserve spatial resolution more effectively than FBP algorithms. These features suggest that the use of advanced image reconstruction algorithms can improve the effectiveness of 3D OAT while reducing the amount of data required for biomedical applications.