3D TrueFISP MRI Provides Accurate Longitudinal Measurements of Glioma Volumes in Mice
Bibliographic record
Abstract
Advances in the Biology, Imaging and Therapies for Glioblastoma 126 accurate 3D information is very important in longitudinal studies such as, for instance, the evaluation of a therapeutic treatment.An alternative to RARE T2 [6] or 3D contrast-enhanced T1 imaging is fully balanced SSFP imaging (also called bSSFP, TrueFISP or FIESTA) [7].In fact, it has recently been shown that this sequence can be used at high field, in 3D, to detect tumors of very small size [8].The advantage of this sequence is the ability to combine the speed of 3D gradient echo and T1/T2 contrast.The purpose of this study is to demonstrate that a 3D TrueFISP MRI sequence is applicable, at high magnetic field, to glioma-bearing mouse models in longitudinal studies.Theoretical considerations of tumor contrast and signal-to-noise ratio as a function of sequence parameters (TE/TR/flip angle) were carried out and compared with experimental data.The 3D TrueFISP MRI sequence was also compared with the sequence most widely used in clinical applications: 2D RARE.Finally, the sequence was used to perform accurate longitudinal measurements of glioma volumes in mice.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".