Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)

One of the main applications of photoacoustic (PA) imaging is the recovery of functional tissue properties, such as blood oxygenation (sO2). This is typically achieved by linear spectral unmixing of relevant chromophores from multispectral photoacoustic images. Despite the progress that has been made towards quantitative PA imaging (qPAI), most sO2 estimation methods yield poor results in realistic settings. In this work, we tackle the challenge by employing learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI) to obtain quantitative estimates for blood oxygenation. LSD-qPAI computes sO2 directly from pixel-wise initial pressure spectra Sp0, which are vectors comprised of the initial pressure at the same spatial location over all recorded wavelengths. Initial results suggest that LSD-qPAI is able to obtain accurate sO2 estimates directly from multispectral photoacoustic measurements in silico and plausible estimates in vivo.Comment: 5 page

absO2luteU-Net: Tissue Oxygenation Calculation Using Photoacoustic Imaging and Convolutional Neural Networks

Photoacoustic (PA) imaging uses incident light to generate ultrasound signals within tissues. Using PA imaging to accurately measure hemoglobin concentration and calculate oxygenation (sO2) requires prior tissue knowledge and costly computational methods. However, this thesis shows that machine learning algorithms can accurately and quickly estimate sO2. absO2luteU-Net, a convolutional neural network, was trained on Monte Carlo simulated multispectral PA data and predicted sO2 with higher accuracy compared to simple linear unmixing, suggesting machine learning can solve the fluence estimation problem. This project was funded by the Kaminsky Family Fund and the Neukom Institute

Towards accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in 3D

Significance: 2D fully convolutional neural networks have been shown capable of producing maps of sO$_2$ from 2D simulated images of simple tissue models. However, their potential to produce accurate estimates in vivo is uncertain as they are limited by the 2D nature of the training data when the problem is inherently 3D, and they have not been tested with realistic images. Aim: To demonstrate the capability of deep neural networks to process whole 3D images and output 3D maps of vascular sO$_2$ from realistic tissue models/images. Approach: Two separate fully convolutional neural networks were trained to produce 3D 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: 3D fully convolutional networks were shown capable of producing accurate sO$_2$ maps using the full extent of spatial information contained within 3D images generated under conditions mimicking real imaging scenarios. This work demonstrates that networks can cope with some of the confounding effects present in real images such as limited-view artefacts, and have the potential to produce accurate estimates in vivo

Deep learning for photoacoustic imaging: a survey

Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging. The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.Comment: A review of deep learning for photoacoustic imagin

Deep learning for biomedical photoacoustic imaging: A review

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicabilityComment: 31 pages, 8 figures, 3 tables, 169 reference