Medical College of Georgia Research Institute, Inc.

Tom C Hu

Nathan E Yanasak

A test object for use with diffusion MRI and a system and methods of synthesizing complex diffusive geometries. The test object, which includes anisotropic structures, can be used to monitor DTI measures by providing a baseline measurement. Using measurements of the phantom, data characteristic of more complicated diffusive behavior can be 'synthesized', or composed of actual measurements re-arranged into a desired spatial distribution function describing diffusion. Unlike a typical DTI scan, the ADC measurements of the present invention are treated in a 'reconstruction' phase as if the gradients were applied in different directions. Given a set of reconstruction directions, a judicious choice of acquisition directions for each reconstruction direction allows for the synthesis of any distribution.

Test object for use with diffusion MRI and system and method of synthesizing complex diffusive geometries using novel gradient directions

A system and method for minimizing, if not completely eliminating, the systematic bias present in an MR system used for DTI is disclosed. A test object or 'phantom' of the present invention is scanned with a desired DTI protocol. The eigenvalues measured with the phantom are compared to the actual values that should have been measured, and a parametric map that links measured eigenvalues to actual eigenvalues is calculated, which is applicable to the desired protocol. Future eigenvalue measurements using this protocol can be recalibrated to actual eigenvalues using this map.

Systems and methods for parametric mapping for correction of noise-based systematic bias of DTI metrics, using a DTI mapping phantom

A method for scaling MR signal intensity after noise has been removed is disclosed. Because the signal in a DTI series varies with the apparent diffusivity in the direction of an applied gradient, one can multiply image data collected under actual clinical conditions with a spatially-dependent scaling function to synthesize different spatial diffusion distributions, after removal of noise. Recombination of the data with the removed noise preserves the bias in the system.

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Evans, GA, United States of America

Tom C Hu