SU‐FF‐J‐100: Mutual Information as a Metric of Multimodality Contrast Agent Efficacy
Bibliographic record
Abstract
Purpose: Mutual information (MI) has been previously employed for image registration. Assuming a priori registration, MI becomes an estimator of image similarity. We apply the concept of MI to the analysis of a feature space formed from multimodality signal intensity distributions. We demonstrate the utility of this concept through the development of a reproducible inter‐image metric to evaluate the efficacy of a multimodal contrast agent for x‐ray computed tomography (CT) and magnetic resonance (MR) imaging. Method and Materials: MR and CT scans were acquired of an anesthetized rabbit in the presence of a multimodal contrast agent. MR and CT scans were acquired pre‐ and post‐injection at fixed time intervals. For each time interval, the MR and CT images were registered, the multimodality feature space was formed and the mutual information between MR and CT was computed using in‐house software. The resulting MI metric was compared with measurements of signal intensity enhancement in user‐defined regions‐of‐interest within the liver, kidneys and aorta. Results: The MI metric correlates with measurements of signal enhancement in MR and CT due to the presence of contrast agent as seen in the liver, kidneys and aorta. Voxels identified as contributing to the increase in MI correspond to areas of visual contrast enhancement observed on MR and CT. Conclusion: We have developed an inter‐image metric for the characterization and optimization of a multimodal contrast agent. The computation of the MI metric is automated and can be extended to 3D for the assessment of image volumes and allows for the inclusion of other modalities such as positron emission tomography. Future investigations will further the development of MI and multimodality feature space analysis as a tool in radiation therapy applications including image registration, target definition and treatment response monitoring.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".