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

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.

[1]  Cheng Li,et al.  Pulse Sequence Programming in a Dynamic Visual Environment , 2005 .

[2]  J C Gore,et al.  Measurements of restricted diffusion using an oscillating gradient spin-echo sequence. , 2000, Journal of magnetic resonance.

[3]  Michael Schacht Hansen,et al.  Gadgetron: An open source framework for medical image reconstruction , 2013, Magnetic resonance in medicine.

[4]  Oliver Speck,et al.  Prospective motion correction in brain imaging: A review , 2013, Magnetic resonance in medicine.

[5]  Boguslaw Tomanek,et al.  The integration of real and virtual magnetic resonance imaging experiments in a single instrument. , 2009, The Review of scientific instruments.

[6]  Robin Dykstra,et al.  A practical and flexible implementation of 3D MRI in the Earth's magnetic field. , 2006, Journal of magnetic resonance.

[7]  Steven M. Wright,et al.  A desktop magnetic resonance imaging system , 2001, Magnetic Resonance Materials in Physics, Biology and Medicine.

[8]  Thies H Jochimsen,et al.  ODIN-object-oriented development interface for NMR. , 2004, Journal of magnetic resonance.

[9]  Boguslaw Tomanek,et al.  Bloch simulations with intra-voxel spin dephasing. , 2010, Journal of magnetic resonance.

[10]  Steven M. Conolly,et al.  Medusa: A Scalable MR Console Using USB , 2012, IEEE Transactions on Medical Imaging.

[11]  Kristine Louise Gould,et al.  Novel software architecture for rapid development of magnetic resonance applications , 2002 .

[12]  Johnpauly.  A k-Space Analysis of Small-Tip-Angle Excitation , 2012 .

[13]  N Jon Shah,et al.  High‐performance computing MRI simulations , 2010, Magnetic resonance in medicine.

[14]  Mark R. Morelande,et al.  Modelling and Estimation of Multicomponent $T_{2}$ Distributions , 2013, IEEE Transactions on Medical Imaging.

[15]  T. Loenneker,et al.  “Silent” MRI with soft gradient pulses , 1999, Magnetic resonance in medicine.

[16]  Feng Jia,et al.  Trajectory optimization based on the signal‐to‐noise ratio for spatial encoding with nonlinear encoding fields , 2016, Magnetic resonance in medicine.

[17]  Michael Garwood,et al.  Fast and quiet MRI using a swept radiofrequency. , 2006, Journal of magnetic resonance.