Nahal Geshnizjani1,
Kenneth A. Loparo2, Dan Ma3, Mark A. Griswold4,
5
1Dept.
of Electrical Engineering and Computer Science, Case Western Reserve
University , Cleveland, OH, United States; 2Dept. of Electrical
Engineering and Computer Science, Case Western Reserve University, Cleveland,
OH, United States; 3Dept. of Biomedical Engineering, Case Western
Reserve University, Cleveland, OH, United States; 4Dept. of
Biomedical Engineering, Case Western
Reserve University, Cleveland, OH, United States; 5Dept. of
Radiology, University Hospitals of Cleveland and Case Western Reserve
University, Cleveland, OH, United States
This work presents a framework for automatic Pulse sequence design. We used Artificial Neural Networks (ANN) with a novel sequential block structure to design a system to automatically construct MR pulse sequences. We are able to predict a pure but extended T2-weighted signal from a TrueFisp sequence. This method can be utilized in MR Fingerprinting by designing non-traditional randomized pulse sequences for quantitative imaging