1. MRI image validation using MRI simulation Emily Koch CIS II April 10, 2001

2. The Problem Validation of MRI based images can be difficult. Without landmarks there is no guarantee that the image is correct. Need to evaluate the effectiveness of a post- imaging algorithm. Without “base standard” there is no guarantee that the post-imaging processing was accurate

3. Flexibility of MRI makes it extremely difficult to set a known standard to compare against –Differences in image contrast –Differences in image quality

4. Goal Want to create realistic image of known object. The more accurate the image of the object, the more accurate the image of the unknown object Want to create the maximally accurate image of known objects

5. References R.K.-S. Kwan, MRI Stimulation for Quantitative Evaluation of Image-Processing Methods, www.bic.mni.mcgill.ca/users/rkwan Remi K.-S. Kwan, Alan C. Evans, G. Bruce Pike. An Extensible MRI Simulator for Post-Processing Evaluation. Visualization in Biomedical Computing (VBC’96). Proceedings. Lecture Notes in Computer Science, vol. 1131. Springer-Verlag, 1996. 135-140.

6. Solutions Creation of a physical phantom –Expensive –Time consuming Multiple image relationships –Expensive –Invasive –Time Consuming Simulation of MRI images to create a “absolute base-line” for studies

7. Simulation of MRI images Program developed using Object Oriented Design techniques Simulation involves two different aspects: –Signal Production –Image Production

8. Spin Model Pulse Sequence image RF CoilScannerPhantom Signal Production Image Production Simulator Design

9. Signal Production Timing of events in the signal production are described by the Pulse Sequence model –RF pulses Message sent to Spin Model as a pulse is applied to an event

10. The Spin Model Current state of tissue magnetization Illustrates behavior under influence of events: –RF pulses, gradient fields, relaxation Interface: defines everything that must be implemented in all subsequent models All extraneous data is hidden so that the behavior of the model can be determined by only the model being used

11. Image Production Signal Production Models -> Image Production Models -> MRI Volumes Phantom Model: spatial distribution of tissues and properties of the tissues Scanner Model: coordination of all components, interface to the Pulse Sequence Model

12. RF Coil Model: control of signal reception –Noise control Different RF Coil Models: –Simulate noiseless conditions –Noise level depending on imaging parameters Slice thickness

13. Creating Realistic Images To create realistic phantoms from the MRI simulator, the author input pre-labeled data set generated from a MRI volumetric data set –3D brain model pre-labeled Signal Production Simulation: –Signal intensities are calculated from the data –Mapped to create a pseudo-MRI volume

14. Basic Results

15. Method Evaluation Sharp tissue boundaries - possible to smooth using higher resolution or blurring the edges of the data set Highly accurate reconstruction of the original image Useful in the evaluation of image contrast and image slice size

16. fMRI Results

17. No motion Motion Motion Corrected

18. Evaluation This information was the result of Kwan’s masters project Little other information on the subject was found. Most of the information is old- the latest information that was used was published in 1997.

19. This method is potentially very useful in the creation of a database of brain function Extremely important to validate the results of the testing as the goal is to create an atlas. The creation of a simulation program would be very time consuming but validation would be necessary for the success of the long term goals of the project.