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Abstract #2877

Comparison and Optimization of Fitting Methods for the Quantification of Multi-Parametric Relaxometric GE-SE EPIK Data

Fabian Küppers1, Omar Al Omari1,2, and N. Jon Shah1,3,4,5
1Institute of Neuroscience and Medicine 4, Forschungszentrum Jülich, Jülich, Germany, 2Fachhochschule Aachen, Aachen, Germany, 3Institute of Neuroscience and Medicine 11, Forschungszentrum Jülich, Jülich, Germany, 4JARA – BRAIN – Translational Medicine, RWTH Aachen University, Aachen, Germany, 5Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany

Synopsis

Keywords: Data Processing, Data Processing, Noise Fitting, RF Pulse correction

Motivation: Imperfect refocusing pulses and imaging noise hinder the quantification performance of simultaneous T2 and T2* fitting approaches and require specific correction methods.

Goal(s): Improve fitting results using a combination of slice profile correction terms and sophisticated fitting models to consider noise.

Approach: Individual free fitting parameters are added for signals acquired following the two refocusing pulses, and different fitting models are compared: NLSQ, offset-noise model and Rician distributions of first and second order.

Results: Correction parameters significantly improve the resulting T2 and T2* distributions. Offset-model fitting underestimates relaxation times, while Rician models improve the fit precision and significantly reduce residuums.

Impact: Individual free pulse correction parameters for subsequent refocusing pulses combined with noise consideration in the form of Rician model fitting significantly improve simultaneous T2 and T2* quantification based on GE-SE EPIK data in terms of better precision and reduced residuums.

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