The Problem MRI image validation using MRI simulation

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The Problem MRI image validation using MRI simulation

Brandman University, US has reference to this Academic Journal, MRI image validation using MRI simulation Emily Koch CIS II April 10, 2001 The Problem Validation of MRI based images can be difficult. Without landmarks there is no guarantee that the image is correct. Need so that evaluate the effectiveness of a post-imaging algorithm. Without ?base standard? there is no guarantee that the post-imaging processing was accurate Flexibility of MRI makes it extremely difficult so that set a known standard so that compare against Differences in image contrast Differences in image quality

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Goal Want so that create realistic image of known object. The more accurate the image of the object, the more accurate the image of the unknown object Want so that create the maximally accurate image of known objects References R.K.-S. Kwan, MRI Stimulation in consideration of Quantitative Evaluation of Image-Processing Methods, bic.mni.mcgill.ca/users/rkwan Remi K.-S. Kwan, Alan C. Evans, G. Bruce Pike. An Extensible MRI Simulator in consideration of Post-Processing Evaluation. Visualization in Biomedical Computing (VBC?96). Proceedings. Lecture Notes in Computer Science, vol. 1131. Springer-Verlag, 1996. 135-140. Solutions Creation of a physical phantom Expensive Time consuming Multiple image relationships Expensive Invasive Time Consuming Simulation of MRI images so that create a ?absolute base-line? in consideration of studies

Simulation of MRI images Program developed using Object Oriented Design techniques Simulation involves two different aspects: Signal Production Image Production Spin Model Pulse Sequence image RF Coil Scanner Phantom Signal Production Image Production Simulator Design Signal Production Timing of events in the signal production are described by the Pulse Sequence model RF pulses Message sent so that Spin Model as a pulse is applied so that an event

Alumni Resources in consideration of Hanover College Alumni Statistics Degreed alumni: 10,495 Non-degreed: 2,373 TOTAL: 12,868 Top 10 States Top Ten Majors Alumni of Note Alumni of Note Alumni of Note Alumni of Note Alumni of Note Bill Bunch ?65, Brooklyn, NY Retired Senior United Nations Official in charge of conferences world-wide Alumni of Note Jim Green ?72, Portola Valley, CA Owner/CEO/entrepreneur in computer technology Alumni of Note Mike Pence ?81, Indianapolis, IN Governor of Indiana Alumni of Note Jennifer Evins ?88, Spartanburg, SC President in addition to CEO of The Arts Partnership of Greater Spartanburg, a conglomerate of 9 non-profit partners Alumni of Note S. Cole Duke ?98, Flora, IL Financial advisor in addition to partner. CEO of Duke Family Enterprises Alumni of Note Alumni of Note Dima Elissa ?85, Chicago, IL President/CEO of VisualMedia

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 Image Production Signal Production Models -> Image Production Models -> MRI Volumes Phantom Model: spatial distribution of tissues in addition to properties of the tissues Scanner Model: coordination of all components, interface so that the Pulse Sequence Model RF Coil Model: control of signal reception Noise control Different RF Coil Models: Simulate noiseless conditions Noise level depending on imaging parameters Slice thickness

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 so that create a pseudo-MRI volume Basic Results Method Evaluation Sharp tissue boundaries – possible so that 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 in addition to image slice size

fMRI Results No motion Motion Motion Corrected 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.

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

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