Open-source Low-field MRI: Learn, Design, Build, Scan

About This Grant

SUMMARY We propose a three-day workshop, “Open-source Low-field MRI: Learn, Build, Scan,” to train biomedical imaging scientists through hands-on construction, scanning, and AI-based image reconstruction using low-cost, open- source MRI systems. Building on the successful “DELTA DIY MRI” at Johns Hopkins, it supports NIBIB’s leadership in biomedical imaging and NINDS’s neurotechnology workforce goals. The workshop’s three phases—Learn, Design & Build, and Scan—combine lectures, hardware prototyping, and real-time scanning with modular MRI setups. Daily milestones include RF Spin Echo and 1D projection (Day 1), 2D Turbo Spin Echo with deep learning super-resolution (Day 2), and 3D imaging with EMI mitigation (Day 3). Mentors with publication records will teach subsystems from magnets to phantoms. Luminary speakers will provide historical context and updates, fostering an engaging environment. Introductory sessions in GitHub and 3D printing will help participants of varied experience levels. Participants will use and contribute to open tools like PyPulseq, Virtual Scanner, and MRI4All, promoting reproducibility. The workshop plans to host 50 participants, with early registration, accessible venues, safety protocols, and NIH-compliant conduct policies. Attendees will gain access to detailed schematics, code, hardware docs, and recordings. The program expands access to MRI by enabling resource-sharing and providing post-workshop materials. Post-event support includes a resource hub, virtual mentoring, and remote scanner access. Promotion efforts include social media, society postings, and networks, with dissemination via project kits, walkthroughs, and tutorials. Scientific outputs include shared builds, software, and data reuse. Strategic backup plans address disruptions. Organizers—with experience and contributions to open-source MRI tools like PyPulseq—lead this initiative. The workshop aligns with PA-25-080 goals: technology dissemination, training, and research strengthening. It supports NIBIB’s imaging, workforce, and AI priorities, and NINDS’s neuroimaging goals. Deep learning super-resolution and phantom validation advance imaging rigor. Contextual understanding enhances critical thinking. Participants are encouraged to share knowledge, expanding capacity. Modular design allows replication in underserved regions. This scalable model aims to democratize MRI science and foster sustainable community growth, empowering technically fluent scientists to lead the future.