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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

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Published on: March 22, 2018

Pulseq: A rapid and hardware-independent pulse sequence prototyping framework.

Kelvin J Layton¹, Stefan Kroboth¹, Feng Jia¹

¹Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, BW, Germany.

Magnetic Resonance in Medicine

|June 9, 2016

Summary

This study introduces a hardware-independent, open-source programming environment for rapid magnetic resonance (MR) sequence prototyping. This flexible approach simplifies the development and implementation of novel MR experiments across diverse platforms.

Keywords:

Pulseq open-source platform independent pulse sequence programming rapid development

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Area of Science:

Magnetic Resonance Imaging (MRI)
Biomedical Engineering
Computational Physics

Background:

Developing new magnetic resonance (MR) sequences typically requires time-consuming, costly programming on vendor-specific platforms.
Implementing research sequences across multiple field strengths or hardware platforms presents significant challenges.

Purpose of the Study:

To present a novel, hardware-independent, open-source programming environment for rapid MR sequence prototyping.
To offer an alternative to vendor-specific programming, reducing development time and cost.

Main Methods:

A new file format was developed for efficient storage of MR pulse sequence hardware events and timing.
Platform-dependent interpreter modules translate sequence files into hardware instructions.
Sequences can be designed using high-level languages (e.g., MATLAB) or a graphical interface, with integrated spin physics simulation tools.

Main Results:

The developed framework allows for the implementation of advanced MR sequences with minimal effort.
Successful execution of sequences on three distinct MR platforms demonstrates the approach's flexibility and hardware independence.

Conclusions:

A high-level, flexible, and hardware-independent programming approach is crucial for accelerating the development of new MR sequences.
While currently not optimized for large patient studies or routine scanning, the framework offers potential for deeper integration into clinical workflows.