Open-source dMRI-bSSFP sequences for ex-vivo brain studies with the GinkgoSequence development suite
Poster No:
1911
Submission Type:
Abstract Submission
Authors:
Eléa GRANIER1, Anaïs Artiges1, Christophe Destrieux2, Igor Lima Maldonado2, Franck Mauconduit1, Cyril Poupon1, Ivy Uszynski1
Institutions:
1BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France, 2iBrain, Université de Tours, Inserm, Tours, France
First Author:
Co-Author(s):
Introduction:
Diffusion MRI (dMRI) enables investigation of the brain's structural connectivity. Various pulse-sequences can be used, such as the classical Pulsed Gradient Spin Echo sequence (PGSE), fit for in-vivo studies with short acquisition times. Although subject to motion artifacts, Diffusion-Weighted Balanced Steady-State Free Precession sequences (DW-bSSFP) use a lower flip angle, show high SNR and strong diffusion sensitization with a unipolar diffusion weighting9, keeping gradients safer from heating; they offer high-resolution dMRI data, making them particularly suited for post-mortem acquisitions at higher fields as they are free from movement and time constraints. However, state-of-the-art dMRI pulse sequences can be challenging to obtain and customize on scanners. MRI manufacturers generally only provide a selected few on their systems, catering to clinical applications' basic needs. Some advanced ones are shared within the community, while others must be adapted from existing proprietary source code, which is often challenging to obtain, to modify, and can lead to ownership conflicts. Addressing these limitations, the open-source GinkgoSequence1 framework makes custom pulse sequences accessible while focusing on optimizing dMRI sequence development. This work introduces a novel DW-bSSFP development suite in GinkgoSequence.
Methods:
The GinkgoSequence framework follows an object-oriented approach with modules that can be instantiated in a pulse sequence. To make a wide variety of sequences, there needs to be a complete panel of modules to be picked from, derivating from mother classes (Fig.1), that correspond to all stages required for a sequence: preparation, excitation, encoding, refocusing, reading, and spoiling. Two new reading modules that balance gradient moments were developed. A unipolar diffusion gradient8 module that loops over a parameters text file containing the gradient diffusion duration, its strength, and all diffusion orientations was further implemented. The new modules were quickly assembled (Fig.1) into a set of DW-bSSFP sequences available in 2D and 3D. Phase cycling5 strategies were implemented to eliminate banding artifacts.
To assess the quality of these sequences, we performed a diffusion protocol10 (parameters Fig.2) on NeuroSpin's 3T Prisma Siemens system with an ex-vivo human brain prepared by the iBrain Unit (University of Tours, France). Following our open-source approach, acquisitions were reconstructed using the Gadgetron8 framework.
The analysis pipeline, performed using the Ginkgo toolbox7, includes a computation of a precise mask of the brain, a diffusion tensor imaging2 (DTI) model giving quantitative fractional anisotropy (FA) and mean diffusivity (MD) maps, as well as an analytical q-ball4 (QBI) model to obtain a diffusion Orientation Distribution Functions (ODF) map.
To assess the quality of these sequences, we performed a diffusion protocol10 (parameters Fig.2) on NeuroSpin's 3T Prisma Siemens system with an ex-vivo human brain prepared by the iBrain Unit (University of Tours, France). Following our open-source approach, acquisitions were reconstructed using the Gadgetron8 framework.
The analysis pipeline, performed using the Ginkgo toolbox7, includes a computation of a precise mask of the brain, a diffusion tensor imaging2 (DTI) model giving quantitative fractional anisotropy (FA) and mean diffusivity (MD) maps, as well as an analytical q-ball4 (QBI) model to obtain a diffusion Orientation Distribution Functions (ODF) map.
Results:
Results of the acquisitions and post-processing (Fig.2) include raw data of the non-DW reference scan and DW volumes, as well as the FA map superimposed with the ODF map, with a specific focus on the expected left-right diffusion pathways in the corpus callosum, thus confirming that the GinkgoBalancedSSFP3Ddw pulse sequence and its modules perform well in exploring the brain microstructure and structural connectivity.
Conclusions:
The new modules implemented in GinkgoSequence allowed us to efficiently develop an open-source, performing, and customizable alternative to proprietary DW-bSSFP pulse sequences. These sequences will undergo further testing at a higher resolution, where whole-brain tractograms will be computed to show their ability to highlight fine fiber fascicles. 2D-GRAPPA3 and 2D-partial-Fourier6 accelerations are available in all preparation and readout modules and could be used to further increase the spatial and angular resolutions while maintaining the acquisition time.
Future developments will focus on adapting this sequence to ultra-high-field imaging (7T and 11.7T).
Future developments will focus on adapting this sequence to ultra-high-field imaging (7T and 11.7T).
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Methods Development
Novel Imaging Acquisition Methods:
Diffusion MRI 1
Keywords:
Acquisition
MRI
MRI PHYSICS
Open-Source Code
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - pulse sequences
1|2Indicates the priority used for review
·Fig.1 : class diagram and chronogram of the DW-bSSFP sequence, developed with the corresponding colored modules
·Fig.2 : diffusion weighted acquisition parameters, raw data reconstructed with Gadgetron and images processed with the Ginkgo Toolbox
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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