Contrast Enhancement in MRI Using Combined Double Action Contrast Agents and Image Post-Processing in the Breast Cancer Model
Bibliographic record
Abstract
Gd- and Fe-based contrast agents reduce T1 and T2 relaxation times, respectively, are frequently used in MRI, providing improved cancer detection. Recently, contrast agents changing both T1/T2 times, based on core/shell nanoparticles, have been introduced. Although advantages of the T1/T2 agents were shown, MR image contrast of cancerous versus normal adjacent tissue induced by these agents has not yet been analyzed in detail as authors considered changes in cancer MR signal or signal-to-noise ratio after contrast injection rather than changes in signal differences between cancer and normal adjacent tissue. Furthermore, the potential advantages of T1/T2 contrast agents using image manipulation such as subtraction or addition have not been yet discussed in detail. Therefore, we performed theoretical calculations of MR signal in a tumor model using T1-weighted, T2-weighted, and combined images for T1-, T2-, and T1/T2-targeted contrast agents. The results from the tumor model are followed by in vivo experiments using core/shell NaDyF4/NaGdF4 nanoparticles as T1/T2 non-targeted contrast agent in the animal model of triple negative breast cancer. The results show that subtraction of T2-weighted from T1-weighted MR images provides additional increase in the tumor contrast: over two-fold in the tumor model and 12% in the in vivo experiment.
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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 | 0.000 |
| 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".