A research team at Tianjin University (TJU) has achieved the first “True Perspective” imaging of the optical function of in vivo deep tissue using a quantitative optoacoustic method based on deep learning. This result is published in Optica, a top international optical journal, on January 6, 2022. Associate Professor Jiao Li is the first author, and Associate Professor Biao Sun and Professor Feng Gao from TJU and Professor Vasilis Ntziachristos from Technical University of Munich are the co-corresponding authors of the paper.

This method will provide a high spatial resolution quantitative imaging strategy for obtaining images of blood oxygen characteristics related to the physiology and pathology of living tissues. It also can be used for early detection of tumors, benign and malignant tumors diagnosis, and in vivo monitoring and quantitative anticancer drug efficacy evaluation.

Quantitative optoacoustic tomography (QOAT) combines the functionality of traditional optical imaging and the high resolution of traditional ultrasound imaging, is a new non-invasive biomedical imaging technology. QOAT can directly obtain the optical absorption coefficient images of deep tissue and has drawn attention from research institutions and medical enterprises both in China and worldwide. However, light intensity gradually attenuates during propagation in tissues. Optoacoustic imaging cannot truly reflect the optical absorption coefficient of tissue due to the light intensity attenuation in deep tissue, negatively affecting the accuracy and reliability of deep tissue imaging. The disadvantages of presenting QOAT including huge computational resources, time-consuming, poor stability, heavy dependence on prior information, and large margins of error.

In recent years, the successful application of deep learning in medical imaging field has demonstrated its potential in biomedical optical imaging. Deep learning generally involves two processes to realize its functions: the training process and the actual testing process. To utilize the full learning ability of the deep neural network, a large amount of paired training data with labels were required for the training process. However, in many biomedical imaging methods, it is difficult to obtain the true values of deep tissue, especially those of living tissue. Therefore, it is difficult to construct a large amount of paired training data with labeled experimental data for deep neural network training, resulting its limitation in the application and promotion of deep learning methods in many biomedical imaging field.

Jiao Li and Feng Gao led their team from the School of Precision Instrument and Opto-Electronics Engineering of TJU proposed a deep learning-based QOAT method to solve these problems. It could accurately reconstruct the absorption coefficient of deep tissues without real labeled experimental data for the first time.

One of the innovative points of this method is that it can solve the problem of limited training data for neural network training. Usage of a style transfer network- Simulation-to-Experiment End-to-end Data translation network (SEED-Net) realized the unsupervised conversion of the simulation data and experimental data, converting data rich with labeled simulation to the experiment domain, and generating large amounts of labeled "experimental data" for subsequent neural network training. “Not only the SEED-Net we proposed solves the lack of real labeled experimental data problems in the QOAT field, we could also use it to generate the labeled "experimental data" from simulation data in other biomedical imaging areas such as diffuse optical/fluorescence tomography, which is also limited by the lack of adequate real labeled experimental data, to further develop deep learning methods for biomedical imaging suitable for practical applications. This method is universally applicable to different optoacoustic imaging systems, other optical imaging techniques, and the entire biomedical imaging field.” said Jiao Li. “Also it solves the generalization problem of deep learning methods to some extent.” said Biao Sun.

Another innovation of the paper is that the team designed a dual-path neural network combining the actual optoacoustic and mathematical model which takes the influence of light intensity distribution and optical absorption coefficient of tissue on the initial pressure image into consideration. “Usually, network models developed in other fields are directly used to solve problems in the current deep learning-based optoacoustic field. How to improve neural networks to be closer to the mathematical models of optoacoustic or other imaging technologies will be one of the important problems for deep learning in biomedical imaging”, Jiao Li added.

Using the optoacoustic tomography system independently developed by the team to obtain the test data, the developed deep learning-based QOAT method successfully reconstructed the quantitative distribution image of the optical absorption coefficient of deep tissues with high spatial resolution. This is the first time that deep learning-based QOAT methods have been used to achieve “True Perspective” imaging of the optical absorption coefficient of in vivo deep tissue. The successful application of neural networks without real labeled experimental data also expands the development space of deep learning-based methods in biomedical imaging.

FIG.1 Functional graphic of deep learning-based QOAT method

FIG.2 Comparison generated "experimental data" with real experimental data

FIG.3 Comparison of traditional optoacoustic reconstruction (P0) images and QOAT reconstruction (μa) images

By: School of Precision Instrument and Optoelectronics Engineering

Editor: Qin Mian