cmrseq 0.27.1

Define your MRI sequences in pure python!

Define your MRI sequences in pure python!

The cmrseq frame-work is build to define MRI sequences consisting of radio-frequency pulses, gradient waveforms and sampling events. All definitions follow the concept hierarchically assemble experiments where the basic building blocks (Arbitrary Gradients, Trapezoidals, RF-pulses and ADC-events) are forming the core functionality and are all instances of SequenceBaseBlock-instances. On instantiation all base-blocks are validated against the System specifications. Composition of base-blocks is done in a Sequence object. The Sequence object implements convenient definitions for addition and composition of multiple Sequence objects as well as to perform a variety on common operations on semantically grouped base-blocks.

Several semantically connected groups of building blocks (e.g. a slice selective excitation) are allready functionally defined in the parametric_definitions module. For a complete list of available definitions checkout the API-reference.

The original motivation for cmrseq was to create a foundation to define sequences for simulation experiments. Therefore Sequences can be easily gridded onto a regular (or even unregular grids with a maximum step width) grids. Furthermore, commonly useful functionalities as plotting, evaluation of k-space-trajectories, calculation of moments, etc.

To close the gap to real-world measurements, cmrseq includes an IO module that allows loading Phillips (GVE) sequence definitions as well as reading and writing Pulseq (>= 1.4) files, which then can be used to export the sequence to multiple vendor platforms. For more information on this file format please refer to the official PulSeq web-page.

Installation

The registry contains the versioned package, which can be installed using:

pip install cmrseq

There are only few package dependencies, namely: - numpy - matplotlib to display the waveforms - pint to assign physical units and assert correctness of calculations - tqdm progressbar visualizations - scipy selected functionalities

Documentation & Getting Started

The API documentation for released versions can be found here.