<scp>MRI4ALL</scp>: A Week‐Long Hackathon for the Development of an Open‐Source Ultra‐Low‐Field <scp>MRI</scp> System
Bibliographic record
Abstract
ABSTRACT The goal of the MRI4ALL hackathon, which took place in October 2023, was to develop a functional low‐field MRI scanner in just one week and to release all created source code and resources as open‐source packages. The event was attended by 52 participants from 16 institutions who assembled the scanner on the last day of the hackathon. The scanner's magnetic B0 field with a strength of 43 mT and a target field‐of‐view size of 11 cm3 was created with a Halbach array made from 990 N40UH permanent magnets, held in place using 3D printed ring formers. Gradient coils were fabricated by gluing enameled copper wire onto 3D printed holders with imprinted wire patterns. A solenoid coil for RF transmission and reception was built by winding 20 turns of Litz wire around a 3D printed cylinder. A Red Pitaya FPGA prototyping board running the MaRCoS framework was used to control the scanner components, and a GPA‐FHDO amplifier board was used to drive the gradients. To simplify the scanner's operation, console software with an intuitive graphical user interface was developed in Python using the PyPulseq package for sequence calculations. Furthermore, the scanner was equipped with a cooling system, as well as options for passive and active shimming. After resolving several technical issues that arose during the assembly, the scanner is now able to acquire MR images with different sequences. While not suitable for real‐world clinical applications, it can be utilized for educational purposes or as a low‐cost prototyping platform. Moreover, it may serve as a reference design for future MRI development projects. All source code and resources are available on the project website mri4all.org , allowing other groups to replicate the scanner. Evidence Level n/a Technical Efficacy Stage 1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".