Algorithms for Nonlinear Inversion in Medical Imaging

Non-invasive imaging techniques using near-infrared (NIR) light yield extensive information on cells and tissues and can reconstruct images of the deep interior of biological tissues. The place occupied by NIR imaging within the multidisciplinary field of molecular imaging has been growing in recent years. This is, in pat, because of its intrinsic advantages (such as its low cost and non-invasiveness) when compared to other imaging modalities such as positron emission tomography (PET), magnetic resonance imaging (MRI) and computed tomography (CT). In collaboration with ART Advanced Research Technologies, a leader in molecular imaging products for the healthcare and pharmaceutical industries, this project will develop mathematical algorithms for solving the nonlinear inverse problems that are at the heart of the NIR imaging process. The methods that will be developed will help ART and practitioners in related fields to obtain better images. Ultimately, the optimization software developed in this project will be used within ART’s medical imaging technology.

Faculty Supervisor:

Dr. Michael Friedlander

Student:

Shidong Shan

Partner:

ART Advanced Research Technologies Inc.

Discipline:

Computer science

Sector:

Life sciences

University:

University of British Columbia