3D Modeling for Multimodal Visualization of Medical Data
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to produce anatomical images and physiological informations of the brain. Based on the principle of magnetic resonance, this non-invasive tool provides numerous types of advanced acquisitions. Some of these acquisitions can provide information on the concentrations of molecules within a well-localized volume in the cortex. Several studies have shown the important role of modeling and visualizing these concentrations according to brain anatomy and in high resolution to improve diagnosis and treatment of tumor areas.To visualize this data, physicians use the concentrations estimated after an acquisition in a grid composed of large voxels. In this acquisition grid, each voxel has a concentration value distributed evenly over its entire 3D space. Interpolation algorithms are usually applied on this grid in order to improve the spatial resolution. However, none of them takes into account the anatomical composition of the acquisition volume that is a mixture of three substances that constitute a brain for a healthy subject, while in the case of a tumor subject, it also includes the presence of a tumor. These partial volumes which make up the acquisition volume admit very different molecular concentrations.In this work, we present a multimodal process allowing to model molecular concentrations taking into account the heterogeneity of the tissues making up the acquisition volume. This process also makes it possible to visualize it in a higher resolution and in 3D. This new method ensures a better distribution of molecular concentrations by considering the real nature of the underlying tissues. It is based on distribution rates evaluated in clinical studies and corrects the effect of partial volumes of brain substances in the entire acquisition volume.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".