Keywords: Software Tools, Software Tools, Data Processing, Microstructure, Quantitative Imaging
Motivation: Parameter mapping is a computationally intensive task in quantitative MRI with demands increase with model complexity and higher-resolution data, hindering their applicability in research and clinical applications.
Goal(s): To develop a tool facilitating GPU acceleration with deep learning optimisers in parameter mapping, enabling researchers to quickly apply it to their specific domain.
Approach: We introduce GACELLE, a Matlab package for GPU-accelerated minimisation-based and Markov-Chain-Monte-Carlo estimations, facilitating high-throughput processing of hundreds of thousands of voxels simultaneously.
Results: GACELLE achieves between 360- and 2210-fold acceleration in our demonstration, with high-quality results comparable to conventional methods. Full documentation and tutorials are provided online for easy dissemination.
Impact: GACELLE removes the obstacle of time-consuming data processing associated with quantitative MRI multi-parametric non-linear problems, promoting wider adoption of quantitative MRI for the scientific community.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords