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Abstract #1470

A Serial Artificial Neural Network Model for TrueFISP Sequence Design

Nahal Geshnizjani 1 , Kenneth A. Loparo 1 , Dan Ma 2 , Debra McGivney 3 , Vikas Gulani 2,3 , and Mark A. Griswold 2,3

1 Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, United States, 2 Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States, 3 Radiology, University Hospitals of Cleveland and Case Western Reserve University, Cleveland, Ohio, United States

The purpose of this work is to design a system that is able to extract basic MR sequence parameters such as FA and TR from TrueFIsp signal evolutions. Artificial Neural Networks are used as the main tool because of their ability to be trained and learn and then solve complicated mathematical equations. We use an efficient method to predict FAs of TrueFISP signal evolutions one excitation at a time using the magnetization preceding and following the excitation. ANNs are trained by arbitrary initial magnetizations and random flip angles.

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