Optimizing the gVERSE RF Pulse Sequence: An Evaluation of Two Competitive Software Algorithms

Radio Frequency (RF) pulses cause elevated patient temperatures during Magnetic Resonance Imaging (MRI) procedures. Generalized Variable Rate Selective Excitation (gVERSE) is a co-design method for Radio Frequency (RF) pulse and slice gradient which minimizes Specific Absorption Rate (SAR) (the accepted predictor of patient heating). After developing a rigorous mathematical model, the nonlinear gVERSE optimization problem is solved using two competitive software packages. The gVERSE solutions generated by Sparse Optimal Control Software (SOCS) and AMPL-MINOS produce two separate variations of SAR reducing pulses. The different software solutions are compared using numerical simulations of slice selection. The computational experiments involved with the gVERSE model provided insight towards using different software to solve highly demanding mathematical optimization problems.

[1]  Marcello Guarini,et al.  Chebyshev series for designing RF pulses employing an optimal control approach , 2004, IEEE Transactions on Medical Imaging.

[2]  P Xu,et al.  Delayed‐focus pulses for magnetic resonance imaging: An evolutionary approach , 1991, Magnetic resonance in medicine.

[3]  Yu-Chung N. Cheng,et al.  Magnetic Resonance Imaging: Physical Principles and Sequence Design , 1999 .

[4]  P. Lauterbur,et al.  Principles of magnetic resonance imaging : a signal processing perspective , 1999 .

[5]  A. Macovski,et al.  Variable-rate selective excitation , 1988 .

[6]  D. Teichroew,et al.  Optimal control of dynamic operations research models , 1969 .

[7]  Andrea Lodi Algorithmic Operations Research , 2007 .

[8]  A. Dobell Optimal control of dynamic operations research models , 1968 .

[9]  Stewart C. Bushong,et al.  Magnetic Resonance Imaging: Physical and Biological Principles , 1988 .

[10]  R. C. Murry,et al.  Christensen's physics of diagnostic radiology , 1990 .

[11]  Tamás Terlaky,et al.  The gVERSE RF Pulse: An Optimal Approach to MRI Pulse Design , 2006, HPSC.

[12]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[13]  A. O. Rodríguez,et al.  Principles of magnetic resonance imaging , 2004 .

[14]  Anil V. Rao,et al.  Practical Methods for Optimal Control Using Nonlinear Programming , 1987 .

[15]  B. Hargreaves,et al.  Variable‐rate selective excitation for rapid MRI sequences , 2004, Magnetic resonance in medicine.

[16]  J. Shen Delayed-focus pulses optimized using simulated annealing. , 2001, Journal of magnetic resonance.