An anthropomorphic polyvinyl alcohol brain phantom based on Colin27 for use in multimodal imaging
Bibliographic record
Abstract
PURPOSE: In this paper, the method for the creation of an anatomically and mechanically realistic brain phantom from polyvinyl alcohol cryogel (PVA-C) is proposed for validation of image processing methods such as segmentation, reconstruction, registration, and denoising. PVA-C is material widely used in medical imaging phantoms because of its mechanical similarities to soft tissues. METHODS: The phantom was cast in a mold designed using the left hemisphere of the Colin27 brain dataset [C. Holmes et al., "Enhancement of MR images using registration for signal averaging," J. Comput. Assist. Tomogr. 22(2), 324 (1998)]. Marker spheres and inflatable catheters were also implanted to enable good registration comparisons and to simulate tissue deformation, respectively. RESULTS: The phantom contained deep sulci, a complete insular region, and an anatomically accurate left ventricle. It was found to provide good contrast in triple modality imaging, consisting of computed tomography, ultrasound, and magnetic resonance imaging. Multiple sets of multimodal data were acquired from this phantom. CONCLUSIONS: The methods for building the anatomically accurate, multimodality phantom were described in this work. All multimodal data are made available freely to the image processing community (http://pvabrain.inria.fr). We believe the phantom images could allow for the validation and further aid in the development of novel medical image processing techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".