Improvement of tumour heterogeneity quantification in PET images

Improvement of tumour heterogeneity quantification in PET images: A study to design and develop a new PET heterogeneous phantom

Ghesquière-Diérickx, Laura (TU Delft Mechanical, Maritime and Materials Engineering)

Schaart, D.R. (mentor)
Grootjans, Willem (mentor)
Lathouwers, D. (graduation committee)
Zadpoor, A.A. (graduation committee)

Delft University of Technology

Biomedical Engineering

2017-10-10

In medical images intratumour heterogeneity well translates specific cancer cells properties in a fast and non-invasive way. Medical community and researchers have developed proper tools (texture features, shape features…) permitting the quantification and the analysis of this tumour heterogeneity in particular in PET images. Yet so far, both doctors and researchers have mainly focused on the use of those tools before assessing their relevance and robustness. In this study, a new heterogeneous PET phantom allowing an extensive understanding of the key concept of heterogeneity quantification in PET images is designed and partially developed. This phantom aims at representing a complex enough environment mimicking clinical conditions to properly challenge PET heterogeneity data extraction and quantification methods further than commercial phantom already do. This study focuses, first, on defining sound specifications, design methodology and manufacturing method for this new object. Second, the study focuses on designing and developing the defined solution. Third, a proof-of-concept analysis is conducted within the study to test and validate the developed PET phantom prototype. It can be concluded that the produced PET phantom was successfully designed and developed and can be used by other operators; yet, there is still room for improvement. Finally, besides developing a novel PET phantom, this study yields recommendations to improve the work done toward a more handy, flexible and realistic tool.

pet
phantom
heterogeneity
Positron emission tomography

http://resolver.tudelft.nl/uuid:0183c922-76a4-4507-96dd-38362e27da0a

Student theses

master thesis

© 2017 Laura Ghesquière-Diérickx