Comparison of T2 and T2*-weighted MR molecular imaging of a mouse model of glioma
Bibliographic record
Abstract
BACKGROUND: Standard MRI has been used for high-grade gliomas detection, albeit with limited success as it does not provide sufficient specificity and sensitivity to detect complex tumor structure. Therefore targeted contrast agents based on iron oxide, that shorten mostly T2 relaxation time, have been recently applied. However pulse sequences for molecular imaging in animal models of gliomas have not been yet fully studied. The aim of this study was therefore to compare contrast-to-noise ratio (CNR) and explain its origin using spin-echo (SE), gradient echo (GE), GE with flow compensation (GEFC) as well as susceptibility weighted imaging (SWI) in T2 and T2* contrast-enhanced molecular MRI of glioma. METHODS: A mouse model was used. U87MGdEGFRvIII cells (U87MG), derived from a human tumor, were injected intracerebrally. A 9.4 T MRI system was used and MR imaging was performed on the 10 day after the inoculation of the tumor. The CNR was measured prior, 20 min, 2 hrs and 24 hrs post intravenous tail administration of glioma targeted paramagnetic nanoparticles (NPs) using SE, SWI, GE and GEFC pulse sequences. RESULTS: The results showed significant differences in CNR among all pulse sequences prior injection. GEFC provided higher CNR post contrast agent injection when compared to GE and SE. Post injection CNR was the highest with SWI and significantly different from any other pulse sequence. CONCLUSIONS: Molecular MR imaging using targeted contrast agents can enhance the detection of glioma cells at 9.4 T if the optimal pulse sequence is used. Hence, the use of flow compensated pulse sequences, beside SWI, should to be considered in the molecular imaging studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".