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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