Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions

Abstract

Significance: Two-dimensional (2-D) fully convolutional neural networks have been shown capable of producing maps of sO 2 from 2-D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2-D nature of the training data when the problem is inherently three-dimensional (3-D), and they have not been tested with realistic images. Aim: To demonstrate the capability of deep neural networks to process whole 3-D images and output 3-D maps of vascular sO 2 from realistic tissue models/images. Approach: Two separate fully convolutional neural networks were trained to produce 3-D maps of vascular blood oxygen saturation and vessel positions from multiwavelength simulated images of tissue models. Results: The mean of the absolute difference between the true mean vessel sO 2 and the network output for 40 examples was 4.4% and the standard deviation was 4.5%. Conclusions: 3-D fully convolutional networks were shown capable of producing accurate sO 2 maps using the full extent of spatial information contained within 3-D images generated under conditions mimicking real imaging scenarios. We demonstrate that networks can cope with some of the confounding effects present in real images such as limited-view artifacts and have the potential to produce accurate estimates in vivo.

Oxygen Saturation Imaging Using LED-Based Photoacoustic System

“…Guo et al utilized the acoustic spectra of PA signals to compute sO in the tissue [ 32 ]. Deep learning-based methods were also used for sO [ 33 , 34 ]. Xia et al utilized the dynamics in sO to correct for the fluence.…”

Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties

“…The training could be done with such a device free of visibility artefacts, to train a DLA to be applied on a simpler device. Quantitative reconstruction has been obtained with such an approach in the context of simulations [51] …”

Role of blood oxygenation saturation in ovarian cancer diagnosis using multi‐spectral photoacoustic tomography

“…A second limitation is the assumption of wavelength‐independent fluence in the linear unmixing method. Quantitative %sO 2 calculation without this assumption has been a hot topic in the field of PAT in recent years, and several methods, including deep learning models and model‐based algorithms, have been proposed [53–58]. Nevertheless, although interesting results have been observed for simulation and phantom data, applying these methods to clinical data remains a challenge.…”