Design and evaluation of a diffusion MRI fibre phantom using 3D printing
Bibliographic record
Abstract
Diffusion weighted magnetic resonance imaging (dMRI) has enabled the in vivo imaging of structures with a highly fibrous composition, such as brain white matter due to the ability to detect the hindered and restricted diffusion of water along its defined tracts. In order to increase this non-invasive technique’s sensitivity to the intricate fibrous structure and to better calibrate diffusion pulse sequences and validate fibre reconstruction modelling techniques, physical diffusion phantoms have been developed. These phantoms have a known structure and diffusion behaviour. This work aims to simplify the process of creating complex fibre-based diffusion phantoms using 3D printing material to model and mimic brain white matter fiber architecture for dMRI. We make use of a printing material consisting of a mixture of polyvinyl alcohol (PVA) and a rubber-elastomeric polymer (Gel-Lay by Poro-Lay), printed using a fused deposition modelling (FDM) printer. It is 3D printed as a rigid object but, following immersion in room-temperature water, the PVA dissolves away leaving behind the porous rubber-elastomeric polymer component to mimic the structure of brain white matter tracts. To test the validity of the methodology, two preliminary main phantoms were created: a linear 10mm × 10mm × 30mm block phantom and an orthogonal fibercrossing phantom where two blocks cross at a 90-degree angle. This was followed by creating 3 disk phantoms with fibres crossing at 30, 60 and 90 degrees. Results demonstrate reproducible high diffusion anisotropy (FA= 0.56 and 0.60) for the phantoms aligned with the fibre direction for the preliminary linear blocks. With multi-fibre ball & stick modelling in the orthogonal fibre-crossing phantom and the disk phantoms at 30, 60 and 90 degrees, image post-processing yielded crossing fibre populations that reflected the known physical architecture. These preliminary results reveal the potential of 3D-printed phantoms to validate fibre-reconstruction models and optimize acquisition protocols, paving the way for more complex phantoms and the investigation of long-term stability and reproducibility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".