Introduction:

Photoacoustic (PA) imaging is an emerging technology that has a wide range of applications in both biological and clinical studies such as hemodynamics, oxygen metabolism, biomarkers, and gene expression [1-2]. PA is a coupled-physics imaging modality: light is sent to the surface of the sample and ultrasound is detected as measurement. Therefore, PA is advantageous to pure optical or ultrasound imaging technologies as it achieves good optical contrast at high acoustic resolution.

Motivation of the project:

The forward model of PA is composed of two parts: photon excitation (optical) and ultrasound detection (acoustic). This project focuses on the acoustic part.

The acoustic reconstruction algorithm varies with the experimental setup, e.g. the geometry of the detector. There are three common geometries: planar, cylindrical and spherical (see above, [4]). We are interested in designing robust reconstruction algorithms under different setups.

Tasks:

The student will gain insight into computational imaging by learning how to build a mathematical model based on the physics of PA imaging, formulate the corresponding forward model and inverse problems, design and implement reconstruction algorithms, and finally test the algorithms on numerical phantoms.

Prerequisites:

Solid background in analysis and basic physics, good programming skills (mainly Python for this project), and enthusiasm in biomedical imaging.

References:

[1] Wang, L. V. & Hu, S. Photoacoustic Tomography: In Vivo Imaging from Organelles to Organs. Science 335, 1458–1462 (2012).
[2] Wang, L. V. Multiscale photoacoustic microscopy and computed tomography. Nature Photon 3, 503–509 (2009).
[3] Wang, L. V. & Yao, J. A practical guide to photoacoustic tomography in the life sciences. Nat Methods 13, 627–638 (2016).
[4] Xu, M. & Wang, L. V. Universal back-projection algorithm for photoacoustic computed tomography. Phys. Rev. E 71, 016706 (2005).