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

Nima Akhlaghi,William C Vogt,T Joshua Pfefer,Brian S Garra,Keith A Wear

doi:10.1117/1.jbo.24.12.121910

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.

Highlights

1.1 Photoacoustic ImagingBreast cancer is the second leading cause of cancer-related death for American women,[1] and early detection and accurate diagnosis are critical for reducing its mortality rate.[2]
Our aims were to: (1) couple a previously developed three-dimensional (3D) Monte Carlo (MC) model with k-Wave, (2) verify the model by comparing outputs against published simulation data, (3) validate the model against experimental images acquired with a custom Photoacoustic imaging (PAI) system, and (4) demonstrate model utility through parametric study of how key system design parameters influence image quality
There are small discrepancies in terms of RF signal trace shape and precise cyst boundary location that may be attributed to our use of 2D acoustic simulations as opposed to 3D, as well as a different choice of photoacoustic image reconstruction algorithm

Summary

Introduction

Breast cancer is the second leading cause of cancer-related death for American women,[1] and early detection and accurate diagnosis are critical for reducing its mortality rate.[2] The current standard of care includes mammography screening as well as adjunct supplemental ultrasound for diagnosing suspicious lesions.[3] mammography requires ionizing radiation and has low sensitivity in dense breast tissue while ultrasound has low specificity for breast cancer.[1,2,3,4] Photoacoustic imaging (PAI) is a rapidly emerging modality combining pulsed optical excitation and acoustic detection for deep mapping of lightabsorbing chromophores to depths of several centimeters.[5,6] Because optical absorption in tissue is dominated by oxy- and deoxy-hemoglobin (HbO2, Hb), PAI is capable of visualizing vasculature and, through multispectral measurements of tissue absorption, mapping tissue blood oxygen saturation (SO2).[7,8] As some hallmarks of malignant cancer include tissue hypoxia and abnormal vasculature,[9] PAI may potentially improve cancer detection by providing information on tissue function to

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