Brain Phantoms for Ultra High Field MRI

Six-week projectLauren Villemaire

MBP 3970ZDepartment of Medical Biophysics

University of Western Ontario

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OutlineIntroduction

MRI Imaging at high magnetic fieldsPhantoms

ObjectiveMethodsResults DiscussionConclusionAcknowledgementsReferences

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Introduction to MRIUses nuclear magnetic resonance of protons to

produce proton density images

Magnetism- proton spin

Larmor Frequency- rate of precession

The 7T MRI at Robarts Research Institute.

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The primary magnet - main magnetic field- the tesla

The gradient magnet- alters magnetic field - focuses magnetic field

The RF coil- alters direction of proton spin- detects precession energy

T1 and T2 relaxation times- characteristic of tissues- image contrast

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Imaging at ultra high magnetic fields

Advantages

SNR

Spatial Resolution

Tissue Contrast

Disadvantages

RF

heterogeneities

Magnetic

susceptibility artifacts

Specific Absorption Rate (SAR)

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Brain images at 7T have shading and bright spots that compromise image homogeneity.

Human hippocampus

1.5 T 7 T

Images at 7T have much higher spatial resolution and SNR than at 1.5T

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What are phantoms?

An artificial object of known size and composition that is imaged to test, adjust or monitor an MRI system’s - homogeneity- imaging performance- orientation aspects.

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ObjectiveTo develop a brain-mimicking phantom for use in the

7T MRI with the following characteristics in common with the brain:

- Grey matter/white matter T1 and T2 relaxation times

- Electrical and wave properties

- Anatomical structure and size (not symmetrical)

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MethodThe relaxivity of varying concentrations of GdCl3 and agarose were measured

A concentric phantom was fabricated

Coil loading was measured and B1+ effects were empirically determined

T1 and T2 values that match average human grey matter and white matter values were determined via measurements done by MRI

CSF was mimicked by a 50-mM NaCl solution

Grey matterWhite matter

CSF

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Axial images of brain slices were obtained from Brain Web – Simulated Brain Database.

These images represent the standard size and structure of the human brain.

Number of slices 36Modality T1Slice thickness 5mmNoise 0%Intensity non-uniformity (RF) 0%

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14 brain slices, each 1cm apart, were selected

Images were, then, modified using Image J to sharpen and enhance contrast between grey matter, white matter, and CSF.

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Each image was manually outlined to distinguish between the different compartments.

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Tracings were scanned and made binary using ImageJ and then converted to SAT using Solid Works.

jpg pdf

dxf sat

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Images were then formatted to open in the MasterCam Mill 9 program where they were modified.

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Results  An agarose gel and saline solution phantom was

developed to mimic properties of the human brain for imaging at 7T.

TissueTarget T1

(ms)Target T2

(ms)% agarose

[GdCl3] (uM)

Grey matter 2000 55 2.1% 8

White matter 1300 45 2.2% 22

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T1W MP RAGE images of the same slice of the phantom with different inversion times.

TI = 500ms T1 = 1400ms to null GM T1 = 900ms to null WM

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Comparison of RF interference patterns.

Single element transmitting (located at back of head)

All elements transmitting with random phases to produce interferences.

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Now that each brain slice is compartmentalized into MasterCam, they can be milled out of plastic and eventually filled with the appropriate brain mimicking substances.

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Discussion

The phantom exhibits very similar dielectric properties (conductivity and permittivity) to the human brain

The phantom is the same size and shape of the average brain

The phantom has similar anatomical structure to the average brain

The phantom has grey matter/ white matter contrast with the same T1 and T2 relaxation times as human brain tissue imaged at 7T

I’ve successfully designed a head-mimicking phantom for use in the 7T MRI.

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ConclusionSuch a phantom is unique.

It would...

(1) allow the ability to instrument the phantom and measure RF power deposition (SAR)

and (2) optimize RF shimming techniques using

multiple transmitters.

Both of these are major challenges currently.

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Acknowledgements

Supervisor: Dr. Ravi S. Menon

Post-doctoral student: Kyle Gilbert

Graduate student: Andrew Curtis

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ReferencesBrain Web: Simulated Brain Database

http://mouldy.bic.mni.mcgill.ca/brainweb/

Rooney WD, et al. Magn Reson Med 2007; 57:308-318

Wright PJ et al. MAGMA 2008; 21:121-130

Yoshida A et al. Int J Hyperthermia 2004; 20:803-814