Magnetic resonance of hearts in a jar: breathing new life into old pathological specimens
Bibliographic record
Abstract
BACKGROUND: Specimens of the normal and congenitally abnormal heart have been long preserved, collected, and studied. It is increasingly difficult to add to such pathological collections. These museum pieces are often inaccessible for teaching purposes. Magnetic resonance imaging of old pathological specimens could produce high-resolution unalterable datasets that could be processed to create three-dimensional reconstructions using inexpensive systems that could be used by untrained individuals. To our knowledge, the concept of "Virtual Autopsy" has not been applied to cardiac specimens of museum collections. METHODS: To determine optimal sequences and assure specimen safety, five different pulse sequences designed to create three-dimensional datasets were tried on a uterus specimen suspended in a fluid-filled glass container, using a 1.5 Tesla scanner with an eight-channel phased-array coil. Having found the best sequences and established specimen integrity, we scanned six historical heart specimens in their original fluid-filled glass containers. The datasets were processed on a laptop with a DICOM viewer available as freeware. RESULTS: All specimens were successfully scanned. The best image quality was obtained by using a three-dimensional FSPGR and the BRAVO pulse sequences. High-resolution three-dimensional and multi-planar image processing was possible for all datasets. Detailed examination of the specimens could be easily performed. CONCLUSION: Pathological specimens can successfully be scanned in minutes resulting in unalterable and portable high-resolution three-dimensional datasets that can be processed by using inexpensive readily available software. The final cardiac reconstructions can be widely shared for educational and scientific purposes and ensure a lasting access to pathological specimens.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".