Pulseq: A rapid and hardware‐independent pulse sequence prototyping framework

@article{Layton2017PulseqAR,
  title={Pulseq: A rapid and hardware‐independent pulse sequence prototyping framework},
  author={Kelvin J. Layton and Stefan Kroboth and Feng Jia and Sebastian Littin and Huijun Yu and Jochen Leupold and Jon-Fredrik Nielsen and Tony St{\"o}cker and Maxim Zaitsev},
  journal={Magnetic Resonance in Medicine},
  year={2017},
  volume={77}
}
Implementing new magnetic resonance experiments, or sequences, often involves extensive programming on vendor‐specific platforms, which can be time consuming and costly. This situation is exacerbated when research sequences need to be implemented on several platforms simultaneously, for example, at different field strengths. This work presents an alternative programming environment that is hardware‐independent, open‐source, and promotes rapid sequence prototyping. 
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Portable and platform‐independent MR pulse sequence programs
To introduce a new sequence description format for vendor‐independent MR sequences that include all calculation logic portably. To introduce a new MRI sequence development approach which utilizes…
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To introduce a framework for rapid prototyping of MR pulse sequences, a series of experiments were conducted to demonstrate the ability of this approach to solve the challenge of rapidly prototyping complex MR sequences.
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Pulseq is introduced, which enables pulse sequence programming in Python with zero licensing fees and easy integration with deep learning methods developed in Python, aimed at MRI researchers, faculty, students, and other allied field researchers such as those in neuroscience.
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An innovative design wherein the newly developed pulses sequences are seamlessly integrated into the machine with due validation and registration and also without affecting the major software workflow is one of the key features of the software platform.
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This work introduces the insight that Cartesian-FRONSAC retains many desirable features of Cartesian imaging, and improves accelerated imaging while retaining the robustness and flexibility critical to real clinical use.
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