/Envisioning photoacoustic-based sensors for continuous blood monitoring

Envisioning photoacoustic-based sensors for continuous blood monitoring

Leuven | More than two weeks ago

Exploring the potential of photoacoustic techologies for next generation blood sensing

Continuous monitoring of blood analytes (CBM) is a cornerstone in daily-life wellness management and demands wearable sensing devices capable of high accuracy, specificity, and sensitivity, all while preventing any discomfort for the individuals. 

 

Within the CBM context, the photo-acoustic technology (PA) stands out as a compelling option. It holds the potential to retrieve molecular information from optically scattering tissues with high specificity, and blends together the non-invasive nature of optical irradiation and ultrasound detection in one unique sensing concept.1 

 

In PA, pulsed illumination of tissue at specific wavelengths generates molecule-specific light absorption events which translates into ultrasound waves via thermoelastic expansion. In contrast to purely optical techniques, the molecular information transported by the PA ultrasound waves can travel undisturbed within optically scattering media and can get revealed by ultrasound sensors in proximity (e.g. microphones) from different tissue depths.  

As such, PA holds significant implications in the CBM as it could retrieve blood-rich information from deeper tissue layers in a non-invasive fashion while providing molecule-specific estimations with no discomfort for the user.   

 

The aim of this internship is to envision different measuring configurations to explore the applicability of the PA concept to CBM. In brief, by making use of Monte Carlo simulations (MC-matlab) and the k-wave acoustic toolbox, the student will explore PA detection of blood analytes in simulated tissue phantoms and reveal detecting performances by combining the ultra-sound microphone developed at IMEC (opto-mechanical ultrasound sensor: OMUS)2 with different and cost-effective light sources. 

Eventually, the generated datasets will be used for advanced A.I. data learning. 

 

We are looking for an internship student with good scripting and simulations skills, thinking out of the box capabilities and enthusiastic about working and learning in a multidisciplinary environment. 

 

For more information on our activities have a look at: 

Literature/simulations/data analyses : 20% literature, 60% simulations, 20% data analyses   


Type of project: Internship

Required degree: Master of Engineering Technology, Master of Engineering Science, Master of Bioengineering, Master of Science

Required background: Bioscience Engineering, Physics, Biomedical engineering, Nanoscience & Nanotechnology

Supervising scientist(s): For further information or for application, please contact: Lisa Tripodi (Lisa.Tripodi@imec.be) and Hilde Jans (Hilde.Jans@imec.be) and Kathleen Vunckx (Kathleen.Vunckx@imec.be)

Imec allowance will be provided for students studying at a non-Belgian university.

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