Highlights of the society for magnetic resonance angiography 2024 conference
Bibliographic record
Abstract
The 36th Annual International Meeting of the Society for Magnetic Resonance Angiography (SMRA), held from November 12-15, 2024, in Santiago de Chile, marked a milestone as the first SMRA conference in Latin America. Themed "The Ever-Changing Landscape of MRA", the event highlighted the rapid advancements in magnetic resonance angiography (MRA), including cutting-edge developments in contrast-enhanced MRA, contrast-free techniques, dynamic, multi-parametric, and multi-contrast MRA, 4D flow, low-field solutions and artificial intelligence (AI)-driven technologies, among others. The program featured 174 attendees from 15 countries, including 43 early-career scientists and 30 industry representatives. The conference offered a rich scientific agenda, with 12 plenary talks, 24 educational talks, 98 abstract presentations, a joint SMRA-MICCAI challenge on intracranial artery lesion detection and segmentation and a joint session with the Society for Cardiovascular Magnetic Resonance (SCMR) emphasizing accessibility, low-field MRI, and AI's transformative role in cardiac imaging. The meeting's single-track format fostered engaging discussions on interdisciplinary research and highlighted innovations spanning various vascular beds. This paper summarizes the conference's key themes, emphasizing the collaborative efforts driving the future of MRA, while reflecting on SMRA's vision to advance research, education, and clinical practice globally.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.034 |
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".