Learning to build low-field MRIs for remote northern communities
Bibliographic record
Abstract
Low-field Magnetic Resonance Imaging (MRI) has the potential to provide autonomous accessible neuroimaging in remote communities, particularly in the Canadian north. Remoteness necessitates that these MRIs be built and maintained within the communities. This approach not only ensures that the MRIs remain operational but will also allow the youth from the communities to pursue technical careers at home. The first step in this vision is to establish that the technical resources needed for building MRIs are available in remote communities and to establish an educational program that will give students the required technical skills. Over the summer of 2024, a team of students working within an Aircraft Maintenance Engineering (AME) program built the hardware for a wrist-sized prototype MRI. The student team included a high school student, AME students, engineering students and a post doctoral fellow. The skills required to maintain aircraft, namely 3D printing, sheet metal work and electrical harness building, were sufficient to build a low-field MRI. The prototype built was a radio frequency (RF) encoding MRI, whose design was optimized for eventual use in space, but the techniques and procedures developed are applicable to other MRI designs. Furthermore the breadth of students from high school to the post doctoral fellow level facilitated an extremely rich learning environment for the students while they focused on the task of designing and building the prototype MRI. Educational programs around building low-field MRIs can be created at all levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".