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Reference : UMR5588-BASARN-001
Workplace : ST MARTIN D HERES
Date of publication : Tuesday, July 20, 2021
Scientific Responsible name : Bastien Arnal
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 October 2021
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly
Description of the thesis topic
Photoacoustic Imaging (PA) is an emerging biomedical imaging technique at the interface of optical and ultrasound imaging. It allows to obtain optical contrasts at centimetric depths. Thanks to the omnipresence of hemoglobin in living tissues, it allows the imaging of vascularization. By using tunable lasers, the photoacoustic spectroscopy technique allows to specifically identify chromophores and to quantify their concentrations. A key application of this technique is to measure oxygen saturation in blood vessels.
Conventional PA imaging systems are limited by visibility issues that restrict detection to near-horizontal vessels as well as vessels of a certain size range. Our group has established a dynamic technique that allows full vessels visibility to be recovered from a stack of images. Another solution we have explored is to use neural networks to enhance limited visibility images into full visibility images. This can be done using a simulated training dataset, but establishing an experimental procedure to acquire an experimental training dataset would allow the application of these neural networks to real-world cases, such as in vivo, where conditions may be variable. To date, there is no experimental way to acquire a good quality quantitative data as ground truth. We will provide a solution to this problem. Thus, this project lies in the joint use of dynamic technique and deep learning algorithms to achieve quantitative 3D imaging of vascularization and oxygenation using single-shot images.
Another problem concerning the spectral coloring effects of tissues must be addressed to achieve quantitative PA imaging in optical absorption. For this, we will develop a new method to image and predict the depth distribution of light in the tissue. We will investigate how this method can improve fluence prediction at any wavelength for quantitative in vivo oxygenation imaging as well as other quantitative applications.
The candidate should have knowledge of biomedical imaging and image processing. Knowledge of deep learning techniques is a plus. The candidate will be expected to navigate the interfaces between wave physics, biology and signal processing. A high degree of adaptability and curiosity is expected.
The Interdisciplinary Physics Laboratory (LiPhy) is located on the University of Grenoble Alpes campus in St Martin d'Hères, 5 minutes from Grenoble. At LiPhy, many teams are developing new cutting-edge instruments in optical imaging, especially at the biomedical interface. Over the last 5 years, the Optics and Imaging team has developed a 3D photoacoustic imaging device as well as new imaging methods with high potential. It is in this environment that the thesis can take place, which will include a modelling part ensured with the help of the computers available in the laboratory as well as in the Gricad cell of the UGA. This project benefits from an ANR funding.
Constraints and risks
The student will be trained in the risks and handling of class IV lasers.
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