Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography Using Radially Symmetric Expansion Functions

Abstract

Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an emerging computed biomedical imaging modality that exploits optical contrast and ultrasonic detection principles. Iterative image reconstruction algorithms that are based on discrete imaging models are actively being developed for OAT due to their ability to improve image quality by incorporating accurate models of the imaging physics, instrument response, and measurement noise. In this work, we investigate the use of discrete imaging models based on Kaiser-Bessel window functions for iterative image reconstruction in OAT. A closed-form expression for the pressure produced by a Kaiser-Bessel function is calculated, which facilitates accurate computation of the system matrix. Computer-simulation and experimental studies are employed to demonstrate the potential advantages of Kaiser-Bessel function-based iterative image reconstruction in OAT.

A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography

“…Kaiser-Bessel function-based D-D PACT model The Kaiser-Bessel (KB) function-based D-D PACT model employs the KB functions of order m as the expansion functions. These KB functions are defined as (Lewitt, 1990;Schweiger and Arridge, 2017;Wang, Schoonover, Su, Oraevsky and Anastasio, 2014) b…”

Deep Learning of truncated singular values for limited view photoacoustic tomography

“…Here ' i @rA a '@r r i A are translated version of a fixed basis function 'X R 2 3 R, where the centers r i are arranged on a Cartesian grid. In particular, we consider so-called Kaiser-Bessel functions which are radially symmetric functions of compact support that are very popular for tomographic inverse problems [31,32,17] Here I m is the modified Bessel function of the first kind of order m P N, > H the window taper and a the radius of the support. Since the forward operator e is linear we have ef a…”

Optical and Optoacoustic Model-Based Tomography: Theory and current challenges for deep tissue imaging of optical contrast

“…The quality of the reconstruction is not only determined by the performance of the imaging hardware but also by the accuracy of the discretization that leads to (3). In particular, when , p t 0 r = h is approximated with a finite sum of base functions that are not sufficiently smooth, the time derivative in (8) may lead to imaging artifacts [27], [29]. Alternatively, using a Fourier-domain formalism to model the optoacoustic operator leads to a nonsparse model matrix W and may lead to Gibbs-like artifacts owing to the finite number of harmonics used to approximate the Fourier transform [27].…”