Andrew Dupuis1, Reid Bolding2, Jessie EP Sun1, Yong Chen3, Rasim Boyacioglu3, and Mark A Griswold1,2,3
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Physics, Case Western Reserve University, Cleveland, OH, United States, 3Radiology, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
Synopsis
Motivation:
Developing robust MRF acquisitions and reconstructions requires
meticulous manual tracking of parameters and dependencies, as well as
integration of diverse tools. Increasing ease of development and use,
regardless of research or clinical purposes, would help with broader
adoption and understanding of MRF.
Goal(s): Improve tooling to increase MRF research flexibility while maximizing traceability of acquisitions and reconstructions.
Approach: Develop a unified Python framework, mrftools, supporting MRF-specific modular sequence abstraction, integration of reconstruction dependencies, and online reconstruction.
Results:
Built-in support for many common techniques and high extensibility for
research use is demonstrated for both sequence and reconstruction
development.
Impact: By providing a unified framework for sequence development and reconstruction, mrftools
streamlines workflow, ensuring traceability and repeatability of MRF
for research or clinical usage. Key contributions lie in MRF-specific
modular sequence design, the integration of reconstruction dependencies,
and online reconstruction.
Introduction
We
developed an end-to-end framework for designing and using Magnetic
Resonance Fingerprinting (MRF) [1] sequences, prioritizing research
flexibility, while ensuring complete traceability and repeatability of
acquisitions and reconstructions in a deployment setting. The mrftools framework introduces MRF-specific abstraction blocks for sequence definition and simulation. mrftools
supplies a modular approach to creation, modification, and combination
of sequence elements without needing to recompile sequences or
reconstruction environments. Additionally, mrftools fully integrates MRF reconstruction dependencies, such as timing and trajectory information, into the raw data stream.Methods
A
cross-platform Python toolkit was developed leveraging PyTorch [2] and
the ISMRM-RD [3] ecosystem. SequenceModules and ReconstructionModules
provide the basis for abstraction of MRF sequence execution, dictionary
simulation, and image reconstruction. All SequenceModules and
ReconstructionModules are members of class inheritance trees, through
which parameters, simulation approaches, serialization schemes, and
sequence runtime implementations are shared. Making use of these
modules, the
mrftools framework consists of four primary structural containers:
- SequenceParameters: A
sequential schedule of abstracted SequenceModules fully specifying the
acquisition parameters for execution of a specific sequence on a
scanner.
- DictionaryParameters: A collection of tissue
property combinations that specify a unique MRF tissue dictionary in
T1/T2 space, with support for additional named dimensions such as B1.
- Simulations:
A pair of SequenceParameters and DictionaryParameters objects, as well
as necessary settings to specify a unique MRF dictionary simulation and
SVD time-domain compression, if desired
- ReconstructionParameters:
A sequential schedule of abstracted ReconstructionModules fully
specifying the reconstruction parameters for a specific sequence.
In addition to the standard loop counters normally encoded alongside
raw data in vendor-specific or neutral raw data formats such as
ISMRM-RD, the
framework requires that
Repetition Time (TR), Echo Time (TE), Requested Flip Angle (FAr),
Applied Flip Angle (FAa), RF Phase (PH), Trajectory Readout ID (ID), and
Preparation ID (PR) are added to all acquisition headers. Unit
specifiers for both time and angle, as well as an
mrftools version specifier must be added to the dataset measurement header.
If
all required header fields are populated by the acquisition
environment, reconstruction can be performed offline from ISMRM-RD raw
data files as well as online via Gadgetron [4] or an ISMRM-RD python
server [3,5]. Regardless of environment, all necessary dependencies are
embedded within the ISMRM-RD file, allowing an
mrftools
compatible reconstruction pipeline to reconstitute the
SequenceParameters used for a specific measurement allowing online
Simulation lookup or execution, SVD compression, and pattern matching
during map generation.
Similar to SequenceModules, all
ReconstructionModules are designed as modular subclasses that share
access to the SequenceParameters and Simulation results associated with a
dataset. Specific functionality of ReconstructionModules can be
overridden for research purposes via class inheritance without impacting
serialization or pipeline modularity. A full list of reconstruction
capabilities currently provided within
mrftools is included in
Figure 1. While many commonly-used functions for MRF research are
already available, such as SVD compression, CG iterative reconstruction,
and pattern matching, additional modules can be added at runtime or via
extension of the
mrftools package.
Reconstructed
images are returned in ISMRM-RD format along with a MetaAttribute XML
set that encodes the ReconstructionParameters applied to generate a
specific set of images, including the unique hash of the Simulation used
during reconstruction.
Results
mrftools
focuses on ease of use in research and clinical settings. Figure 1
enumerates the current capabilities of the framework, including the
available SequenceModules and ReconstructionModules. Figure 2
demonstrates an example design and simulation process for a simple
single-inversion FISP MRF experiment, including the source Python code
and simulation results in the time, readout, and truncated SVD domains.
Figure 3 demonstrates the programmatic configuration of an iterative
reconstruction and pattern matching pipeline, as well as exporting of
the configuration for deployment and the resulting online-reconstructed
maps. Figure 4 demonstrates an entirely different use of the flexible
architecture, in which SequenceParameters, dictionary simulations, and
partial volume maps are generated directly from an mrftools-compatible ISMRM-RD raw dataset.Discussion
The mrftools
framework introduces a valuable resource for MRF both in research and
in clinical applications. This framework prioritizes flexibility,
enabling researchers and clinicians alike to design, simulate, and
reconstruct MRF sequences easily, while also allowing for highly
granular customization to support advanced acquisition and
reconstruction technical development. Its key contributions lie in the
abstraction of MRF-specific elements, modular sequence design, and the
integration of reconstruction dependencies. mrftools
streamlines the workflow through a unified framework for sequence
development and reconstruction, ensuring traceability and repeatability
in diverse acquisition settings. Furthermore, the framework's
compatibility with ubiquitous data formats and inclusion of essential
header fields within the raw data stream supports data accessibility and
compatibility.Acknowledgements
No acknowledgement found.References
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