Multidomain computational modeling of photoacoustic imaging: verification, validation, and image quality prediction

Abstract

As photoacoustic imaging (PAI) technology matures, computational modeling will increasingly represent a critical tool for facilitating clinical translation through predictive simulation of real-world performance under a wide range of device and biological conditions. While modeling currently offers a rapid, inexpensive tool for device development and prediction of fundamental image quality metrics (e.g., spatial resolution and contrast ratio), rigorous verification and validation will be required of models used to provide regulatory-grade data that effectively complements and/or replaces in vivo testing. To address methods for establishing model credibility, we developed an integrated computational model of PAI by coupling a previously developed three-dimensional Monte Carlo model of tissue light transport with a two-dimensional (2D) acoustic wave propagation model implemented in the well-known k-Wave toolbox. We then evaluated ability of the model to predict basic image quality metrics by applying standardized verification and validation principles for computational models. The model was verified against published simulation data and validated against phantom experiments using a custom PAI system. Furthermore, we used the model to conduct a parametric study of optical and acoustic design parameters. Results suggest that computationally economical 2D acoustic models can adequately predict spatial resolution, but metrics such as signal-to-noise ratio and penetration depth were difficult to replicate due to challenges in modeling strong clutter observed in experimental images. Parametric studies provided quantitative insight into complex relationships between transducer characteristics and image quality as well as optimal selection of optical beam geometry to ensure adequate image uniformity. Multidomain PAI simulation tools provide high-quality tools to aid device development and prediction of real-world performance, but further work is needed to improve model fidelity, especially in reproducing image noise and clutter.

Modeling combined ultrasound and photoacoustic imaging: Simulations aiding device development and artificial intelligence

“…The credibility of such simulation models needs to be assessed through parametric verification and validation studies against the experiments. Towards this goal, several PAI simulation approaches have been proposed to optimize the PAI device geometries and quantitative image reconstruction [ [36] , [37] , [38] , [39] , [40] , [41] , [42] ]. These simulations have been predominantly reported for photoacoustic computed tomography geometries that involve rotating single element transducers or sparsely distributed ultrasound transducer elements [ 36 , 37 ].…”

“…However, despite rapid progress in the development and clinical translation of dual-modality USPA devices, no simulation studies have been reported to the best of our knowledge for modeling dual-modality B-mode US and PA imaging of realistic heterogeneous tissue medium. Recently, PAI simulations using k-Wave toolbox have become popular [ 38 , [37] , [38] , [39] , [40] , [41] , [42] ]. Since k-Wave models only acoustic wave propagation, majority of these PAI simulations assumed uniform optical fluence across tissue depth [ 33 , 42 ], while some studies employed light transportation models such as NIR Fast [ 38 ] or Monte Carlo [ 39 ] for estimating light fluence inside homogenous tissue medium and subsequently converting the fluence into initial pressure distribution, followed by photoacoustic reconstruction using k-Wave.…”

“…Recently, PAI simulations using k-Wave toolbox have become popular [ 38 , [37] , [38] , [39] , [40] , [41] , [42] ]. Since k-Wave models only acoustic wave propagation, majority of these PAI simulations assumed uniform optical fluence across tissue depth [ 33 , 42 ], while some studies employed light transportation models such as NIR Fast [ 38 ] or Monte Carlo [ 39 ] for estimating light fluence inside homogenous tissue medium and subsequently converting the fluence into initial pressure distribution, followed by photoacoustic reconstruction using k-Wave. Mastanduno et al, integrated k-Wave and NIRFast toolboxes for quantitative photoacoustic computed tomography reconstruction using circular ultrasound detector geometry [ 38 ].…”

Modeling Combined Ultrasound and Photoacoustic Imaging: Simulations aiding Device Development and Deep Learning

“…The credibility of such simulation models needs to be assessed through the extensive verification and validation studies against the experiments. Towards this P goal, several PAI simulation approaches have been proposed to optimize the PAI device geometries and quantitative image reconstruction [28][29][30]. These simulations have been predominantly reported for photoacoustic computed tomography geometries that involve rotating single element transducers or sparsely distributed ultrasound transducer elements [28].…”

“…However, K-Wave does not account for the realistic PAI scenarios where the optical fluence strongly depends on the heterogeneous optical properties of the tissue, irradiation wavelength and attenuates as a function of tissue depth. Nima et al [29] recently used Monte Carlo simulations for estimating optical fluence in a homogeneous tissue phantom and reconstructed PA images using K-Wave toolbox. However, this study did not report B-mode US images to display corresponding structural information of the tissue phantom.…”