EE Seminar: Guy Nadav
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Guy Nadav,
M.Sc. student under the supervision of Prof. Nahum Kiryati and Dr. Dafna Ben-Bashat
Monday, February 16, 2015 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering
Pharmacokinetics Parameters Estimation from MRI
Abstract
We consider the problem of estimating the underlying anatomical structure and dynamics in brain pathologies from a set of 3-D images of the brain following the injection of a contrast agent, acquired using a Magnetic Resonance Imaging (MRI) method. The goal is to produce quantitative clinically relevant maps of the brain that can help in the assessment of neurological pathologies such as disruption of the Blood-Brain-Barrier (BBB) in a tumor, decrease of blood flow in a stroke etc.
To do that, a contrast agent (Gadolinium) is injected to a subject and temporal tracing of its concentration in the brain is conducted. Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method that enables temporal tracing, where -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The brain tissue effect on the measured contrast agent concentration over time (concentration-time-curve), is modelled as an Impulse Response Function (IRF). The IRF is parameterized by Pharmacokinetics (PK) parameters that represent physiological characteristics, and explain the change in the concentration-time-curve from artery to tissue voxel.
This work describes the entire process of deriving pharmacokinetics parameters from an MRI signal, and suggests novel techniques for handling estimation issues originating from the problem of accurately measuring concentration-time-curve in every voxel of the brain. We first present a few possible brain models, then describe the work that has been previously done and continue by suggesting improvements to the pharmacokinetics parameters estimation. The estimation process was tested in simulation and on data acquired from patients.
The proposed optimization method, denoted ACoPeD (AIF-Corrected-Perfusion-DCE-MRI), showed significant correlation (r=0.46, p<0.001) between flow parameter extracted from DCE-MRI and DSC-MRI (a commonly used perfusion MRI method) in patients, and is applicable in clinical settings.
In conclusion, this study proposes an optimized method, ACoPeD for tissue perfusion and permeability estimation using DCE-MRI, which is applicable in clinical settings, and includes recommendations for DCE-MRI data acquisition and analysis.
