A simplified framework to optimize MRI contrast preparation

Abstract

Purpose: This article proposes a rigorous optimal control framework for the design of preparation schemes that optimize MRI contrast based on relaxation time differences. Methods: Compared to previous optimal contrast preparation schemes, a drastic reduction of the optimization parameter number is performed. The preparation scheme is defined as a combination of several block pulses whose flip angles, phase terms and inter-pulse delays are optimized to control the magnetization evolution. Results: The proposed approach reduces the computation time of B 0 -robust preparation schemes to around a minute (whereas several hours were required with previous schemes), with negligible performance loss. The chosen parameterization allows to formulate the total preparation duration as a constraint, which improves the overall compromise between contrast performance and preparation time. Simulation, in vitro and in vivo results validate this improvement, illustrate the straightforward applicability of the proposed approach, and point out its flexibility in terms of achievable contrasts. Major improvement is especially achieved for short-T 2 enhancement, as shown by the acquisition of a non-trivial contrast on a rat brain, where a short-T 2 white matter structure (corpus callosum) is enhanced compared to surrounding gray matter tissues (hippocampus and neocortex). Conclusions: This approach proposes key advances for the design of optimal contrast preparation sequences, that emphasize their ability to generate non-standard contrasts, their potential benefit in a clinical context, and their straightforward applicability on any MR system. K E Y W O R D SBloch equations, Contrast, Optimal control, Preparation pulses, Rat Brain, White matter TE = 8.5 ms, NA = 2 and a matrix size of 128 × 128. The sOCP sequence is composed of a series of block pulses, eachHow to cite this article: Van Reeth E, Ratiney H, Ve Koon K, et al. A simplified framework to optimize MRI contrast preparation. Magn Reson Med.

Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations

“…The DESPO and MRF IR‐FISP sequences were simulated using the EPG functions and optimized for a single tissue. Additional sequence optimizations for quantitative DESS and an example reproduced from Reeth et al which uses the basic Bloch functions are presented in the Supporting Information. Sequences were optimized using the SLSQP implementation provided by SciPy until an accuracy of was achieved.…”

“…Optimal control methods have been applied to the design of RF pulses [19][20][21][22][23] and sequences. 7,24,25 These methods do not require an analytic expression for the magnetization and apply the Bloch equation directly using approximations to efficiently obtain the gradient of the control objective.…”

“…Assuming that each timestep is small, the gradient of the objective with respect to the RF amplitudes can be efficiently computed using a first or second order approximation of the control step . Reeth et al applied GRAPE to the design of preparation pulses by discretizing the pulse into N blocks with different flip angles, phases and TRs, and maximizing contrast between two tissues. Maidens et al designed a flip angle schedule for hyperpolarized Carbon‐13 MRI with an analytic expression of the CRLB, but used finite differences to obtain the gradient for the optimization.…”

Adaptive optimal control of entangled qubits

“…Driving quantum spins systems to a desired target state via optimal control theory ( 1 ) has been widely applied to a range of areas including nuclear magnetic resonance (NMR) ( 2 , 3 ), magnetic resonance imaging ( 4 , 5 ), electron paramagnetic resonance ( 6 , 7 ), quantum error correction and quantum information registers ( 8 , 9 ), cold atoms ( 10 , 11 ), terahertz technologies ( 12 , 13 ), control of trapped ions ( 14 , 15 ), and nitrogen vacancy centers in diamond ( 16 , 17 ). Along with applications in measurement science, algorithmic and numerical developments of optimal control methods remain active and challenging, with examples including geometric ( 18 , 19 ) and adiabatic ( 20 , 21 ) optimal control, GRAPE (gradient ascent pulse engineering) ( 22 , 23 ) and Krotov ( 24 , 25 ) algorithms, tensor product approach for large quantum systems ( 26 ), and optimal control over approximate control landscapes ( 27 ).…”

Modified Newton-Raphson GRAPE methods for optimal control of spin systems

“…The problem of transferring the state of a dynamical system to a desired target state while minimising the remaining distance and costs is often solved with optimal control theory [1,2]. Applications include quantum sensing [3][4][5][6], quantum computing [7][8][9], and nuclear magnetic resonance (NMR) spectroscopy [10][11][12][13] and imaging (MRI) [14,15].…”