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MAASTRO
PHYSICS
Memo
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Master students (Bio-) Medical Engineering, Applied Physics, etc.
Dr. ir. Wouter van Elmpt, MAASTRO CLINIC – Maastricht University Medical Centre+
April 2013
Project proposal for Master thesis [approx. 9-12 months]
Project proposal: Kinetic analysis of perfusion CT imaging for lung cancer patients
Background
Maastro clinic is a world leading research facility in the fields of applied clinical radiotherapy. There is
a constant drive to provide the best, cutting edge treatment care to its patients. This appetite for
improvement also includes exploring new imaging modalities to optimize treatment delivery and
increase survival and favorable treatment outcome.
Dynamic contrast-enhanced computed tomography (DCE-CT or perfusion CT) imaging is such an
imaging technique that derives information about the vasculature of normal and tumour tissues.
Functional assessment of the kinetic parameters derived from the perfusion CT images give
information about tumour perfusion, blood volume and flow, and permeability of the vessels (1-4).
These parameters are related to accessibility for chemotherapy or anti-angiogenesis drugs.(5)
Whereas in the literature some CT perfusion studies were hampered by the limited field-of-view
(e.g. 3-5 cm) of the scanner in the cranial-caudal direction, the technical infrastructure nowadays
has the ability to capture dynamic CT scans of large volumes up to 12 cm.
The dynamic nature of CT perfusion imaging allows quantification of multiple physiological
parameters of the primary tumour such as blood flow, blood volume, vessel permeability. Kinetic
analysis techniques need to be implemented for this using either Patlak-analysis and/or multiple
compartment models.
Project
This project will focus on the extraction of the kinetic parameters (blood flow, blood volume,
permeability, etc) from the perfusion CT imaging dataset(6). We have commercial software
available, but the current implemented models are not sofisticated enough for advanced analysis.
We will therefor implement these kinetic fitting routines in Matlab and analyse a large group of
patient studies using the newly developed algorithm. The current input CT images contain a
significant proportion of noise, therefore the influence of noise reducing techniques or filtering,
averaging on the extracted parameters needs to be investigated (7, 8). The end-point will be a
method applicaple for both analysis on a tumor and sub-volume level for characterization of tumors
ultimately predicting treatment outcome (i.e. local control).
Techniques / Skills
Experience with imaging would be advantageous but not required. Programming experience using a
MATLAB or C++ (or equivalent) is required; understanding of mathematics of differential equations
and/or compartmental models is needed. The project is a mix of both theoretical and practical
image analysis.
Report
As a report we aim to write a scientific manuscript that will be submitted to an international peerreviewed journal and an abstract (poster or presentation) to a scientific conference.
Supervisors
Medical Physics:
- Dr. Ir. Wouter van Elmpt;
Supervision / Organizational matters:
- Prof. Dr. Ir. Frank Verhaegen;
T: 088 44 55 783; E: [email protected]
T: 088 44 55 792; E: [email protected]
MAASTRO
PHYSICS
Memo
General information
General information about graduation and MSc projects at Maastro Clinic can be obtained from
prof. Verhaegen ([email protected]).
Literature / Further reading
1. Ng QS, et al. Lung cancer perfusion at multi-detector row CT: reproducibility of whole tumor
quantitative measurements. Radiology. May 2006;239(2):547-553.
2. Ng QS, et al. Acute tumor vascular effects following fractionated radiotherapy in human lung
cancer: In vivo whole tumor assessment using volumetric perfusion computed tomography. Int J
Radiat Oncol Biol Phys. Feb 1 2007;67(2):417-424.
3. Miles KA, et al. Perfusion CT: a worthwhile enhancement? Br J Radiol. Apr 2003;76(904):220231.
4. Miles KA, et al. Standardized perfusion value: universal CT contrast enhancement scale that
correlates with FDG PET in lung nodules. Radiology. Aug 2001;220(2):548-553.
5. Lazanyi KS, et al. Usefulness of dynamic contrast enhanced computed tomography in patients
with non-small-cell lung cancer scheduled for radiation therapy. Lung Cancer. Apr 3.
6. Miles KA, et al. Blood flow-metabolic relationships are dependent on tumour size in non-small
cell lung cancer: a study using quantitative contrast-enhanced computer tomography and positron
emission tomography. Eur J Nucl Med Mol Imaging. Jan 2006;33(1):22-28.
7. Korporaal JG, et al. Dynamic contrast-enhanced CT for prostate cancer: relationship between
image noise, voxel size, and repeatability. Radiology. Sep 2010;256(3):976-984.
8. Korporaal JG, et al. Tracer kinetic model selection for dynamic contrast-enhanced computed
tomography imaging of prostate cancer. Investigative radiology. Jan 2012;47(1):41-48.