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. Recently, a new technology called photoswitching photoacoustic imaging has been proposed [3], which demonstrates great potential in further improving the spatial resolution. It aims to quantitatively reconstruction the the concentrations of multiple reporters using a controlled illumination schedule that switches the reporters ON and OFF sequentially.

Motivation of the project:

To maximally exploit the advantage of this new technology, novel reconstruction methods need to be designed and implemented for the unmixing of different reporters.

Tasks:

The student will start from the existing implementation of linear unmixing, explore different options of regularization techniques including the classical ones: l1, l2, and TV as well as recent neural network based ones. The student will design numerical experiments to test different methods, document and compare their performances, then propose new approaches.

Prerequisites:

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

References:

• [1] L. V. Wang and J. Yao, “A practical guide to photoacoustic tomography in the life sciences,” Nat Methods, vol. 13, no. 8, pp. 627–638, Aug. 2016.
• [2] T. Vu, D. Razansky, and J. Yao, “Listening to tissues with new light: recent technological advances in photoacoustic imaging,” J. Opt., vol. 21, no. 10, p. 103001, Oct. 2019.
• [3] M. Stankevych, K. Mishra, V. Ntziachristos, and A. C. Stiel, “A practical guide to photoswitching optoacoustics tomography,” in Methods in Enzymology, Elsevier, 2021, pp. 365–383.