Image reconstruction for an optical "PET"

Project type: BSc thesis / MSc thesis / semester project

Light when penetrating living tissue is exponentially attenuated and highly scattered, rendering the FMT image reconstruction an ill-posed problem. The aim of the project is to improve the reconstruction algorithm for FMT. We have developed Smart Toolkit for fluorescence tomography (STIFT), a comprehensive software platform for simulation, optimization and reconstruction based on finite element method (FEM). Light propagation through heterogeneous tissues has been addressed. The FMT reconstruction quality has been validated by a series of silicone phantom studies together with a few in vivo applications.

REN_Image reconstruction for an optical “PET”.jpg

Keywords: Image reconstruction, medical imaging, optical imaging, finite element method

Date: Earliest starting date is Sep 1, 2018

Fluorescence molecular tomography (FMT) provides molecular information on tracer bio-distribution in the organism similar to positron emission tomography (PET). Instead of using radioactive tracers in PET, FMT non-invasively resolves the three-dimensional distribution of fluorescent probes in vivo. Thus, it can be considered an optical version of PET (but only 1% cost of PET). Although both image reconstruction algorithms and instrumentation for FMT have evolved over the past decade, none of the setups has been widely accepted as a standard molecular imaging tool for routine biomedical research.

The project will be carried out in The Biomedical Optics Research Laboratory (BORL) at University Hospital Zurich and Animal Imaging Center (AIC) at ETH Zurich. BORL is focused on the development of diagnostic tools using light and their research or clinical application. AIC provides access to complementary imaging modalities in rodents within one single facility including Magnetic resonance imaging and spectrosopy, Fluorescence imaging, Two-photon microscopy and Micro X-ray.

1. Development of image reconstruction algorithm for high-speed FMT;
2. Validation of such an algorithm using silicone phantom studies;
3. Potential in vivo studies can be performed if the former two targets finished.

1. Basic knowledge of image processing;
2. Basic programming skills with Matlab /Python;
3. Optimization theory or machine learning basics is preferred, but not necessary.

NOTE: The project was selected as 1 of 32 startup projects in 2017, which are supported by the 'BRIDGE Proof-of-concept' fellowship. (

Dr. Wuwei Ren
E-Mail: Tel: 044 633 76 52
Biomedical Optics Research Laboratory,
Neonatologie, UniversitätsSpital Zürich,
8091 Zürich

Prof. Martin Wolf
E-Mail: Tel: 044 255 53 46
Biomedical Optics Research Laboratory,
Neonatologie, UniversitätsSpital Zürich,
8091 Zürich

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