PyPulseq: A Python Package for MRI Pulse Sequence Design

Abstract

Magnetic Resonance Imaging (MRI) is a critical component of healthcare. MRI data is acquired by playing a series of radio-frequency and magnetic field gradient pulses. Designing these pulse sequences requires knowledge of specific programming environments depending on the vendor hardware (generations) and software (revisions) intended for implementation. This impedes the pace of prototyping. Pulseq (Layton et al., 2017) introduced an open source file standard for pulse sequences that can be deployed on Siemens/GE via TOPPE (Nielsen & Noll, 2018)/Bruker platforms. In this work, we introduce PyPulseq, which enables pulse sequence programming in Python. Its advantages are zero licensing fees and easy integration with deep learning methods developed in Python. PyPulseq is aimed at MRI researchers, faculty, students, and other allied field researchers such as those in neuroscience. We have leveraged this tool for several published research works (Poojar, Geethanath, Reddy, & Venkatesan, n.d.;

Pulseq‐CEST: Towards multi‐site multi‐vendor compatibility and reproducibility of CEST experiments using an open‐source sequence standard

“…Originally intended as a hardware-independent MRI sequence prototyping framework, Pulseq allows for rapid and simple sequence definitions from within MATLAB, Python, and other software programming packages, which are usually open source. 23,24,28 Within these programs, RF pulse, gradient, ADC, and trigger events can easily be defined and are written to a pulseq-file, which is then read and played out by a native interpreter sequence on the scanner. Because Pulseq includes built-in functions to generate block, Gaussian, apodized sinc, and arbitrary user-defined pulse shapes, theoretically, every excitation or saturation CEST preparation scheme can be defined with only a few lines of code.…”

Autonomous magnetic resonance imaging

“…This experiment was designed to demonstrate cognizance: the system is aware of not meeting the prescribed requirements and attempts to derive a set of working parameters by relaxing a certain condition. Experiment #1 was considered to be the internal control, as the sequences implemented were based on PyPulseq [26] and a vendor-specific interpreter module. Table time was…”

Autonomous Magnetic Resonance Imaging

“…This experiment was designed to demonstrate cognizance: the system is aware of not meeting the prescribed requirements and attempts to derive a set of working parameters by relaxing a certain condition. Experiment #1 was considered to be the internal control, as the sequences implemented were based on PyPulseq [26] and a vendor-specific interpreter module. Table time was defined as the time spent by the subject in the scanner, inclusive of the communication overheads between the user node, cloud and scanner and the time spent registering the subject.…”

“…The key identifies the event and the value indicates the state of the event. The vendoragnostic and open source Pulseq [25][26][27] file standard (.seq) was leveraged by the cloud to configure pulse sequences based on the parameters derived from the LUT. The scanner executed these .seq files at the time of data acquisition.…”