Software Development For Software Development For Correction of Gradient-Correction of Gradient-

Nonlinearity Distortions in MR Nonlinearity Distortions in MR ImagesImages

T.S. Lee, K.E. Schubert

Computer Science

CSUSB

R.W. Schulte

Radiation Medicine

LLUMC

Functional Proton Radiosurgery Functional Neurosurgery

– Trigeminal Neuralgia

– Parkinson’s DiseaseParkinson’s Disease

– Brain regions (< 1 cm)

Proton Radiosurgery– Accurate to less than 1 mm

MRI for target localization– Distinguish tissue types

– 512512 images

– 262,144 pixels/study

– Gradient nonlinearity distortions (~2mm)

Example MR Phantom Images

Sagittal Plane

Coronal Plane

Axial Plane

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Finding EdgesFinding Edges

DisconnectsDisconnects

Remove Extraneous FeaturesRemove Extraneous Features

Can We Fix it?A

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Example Edge Images

Axial Plane

Coronal Plane

Sagittal Plane

MidplanesMidplanes

Calculate Midpoints Fit Midplane

Ideal shape, size, and orientation of phantom’s faces

Stack Midpoints

Ideal PlanesShift ±½ the phantom dimensionPerpendicular to face

Distortion ModelingMagnetic Field of CylinderSum of spherical harmonics:

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Distortion ModelingMeasured

Corrected

Applying Distortion CorrectionAxial Plane

Applying Distortion CorrectionCoronal Plane

Applying Distortion CorrectionSagittal Plane

ResultsTheoretical undistorted points vs. corrected pointsStandard deviations of correction

+X face: Standard Deviation = 0.23503 mm

-X face: Standard Deviation = 0.25168 mm

+Y face: Standard Deviation = 0.15322 mm

-Y face: Standard Deviation = 0.15823 mm

+Z face: Standard Deviation = 0.13548 mm

-Z face: Standard Deviation = 0.14173 mm

Conclusions3 range 0.4 – 0.8 mm

–1-2 pixels on each image–Originally 2mm (5-6 pixels)

Accurate localization of anatomical targets

Future WorkFurther verification and testingClinical trialsFDA approvalTreatment on humans