Ultrashort Echo Time for Improved Positive-Contrast Manganese-Enhanced MRI of Cancer
Bibliographic record
Abstract
OBJECTIVE: Manganese (Mn) is a positive magnetic resonance imaging (MRI) contrast agent that has been used to obtain physiological, biochemical, and molecular biological information. There is great interest to broaden its applications, but a major challenge is to increase detection sensitivity. Another challenge is distinguishing regions of Mn-related signal enhancement from background tissue with inherently similar contrast. To overcome these limitations, this study investigates the use of ultrashort echo time (UTE) and subtraction UTE (SubUTE) imaging for more sensitive and specific determination of Mn accumulation. MATERIALS AND METHODS: Simulations were performed to investigate the feasibility of UTE and SubUTE for Mn-enhanced MRI and to optimize imaging parameters. Phantoms containing aqueous Mn solutions were imaged on a MRI scanner to validate simulations predictions. Breast cancer cells that are very aggressive (MDA-MB-231 and a more aggressive variant LM2) and a less aggressive cell line (MCF7) were labeled with Mn and imaged on MRI. All imaging was performed on a 3 Tesla scanner and compared UTE and SubUTE against conventional T1-weighted spoiled gradient echo (SPGR) imaging. RESULTS: Simulations and phantom imaging demonstrated that UTE and SubUTE provided sustained and linearly increasing positive contrast over a wide range of Mn concentrations, whereas conventional SPGR displayed signal plateau and eventual decrease. Higher flip angles are optimal for imaging higher Mn concentrations. Breast cancer cell imaging demonstrated that UTE and SubUTE provided high sensitivity, with SubUTE providing background suppression for improved specificity and eliminating the need for a pre-contrast baseline image. The SubUTE sequence allowed the best distinction of aggressive breast cancer cells. CONCLUSIONS: UTE and SubUTE allow more sensitive and specific positive-contrast detection of Mn enhancement. This imaging capability can potentially open many new doors for Mn-enhanced MRI in vascular, cellular, and molecular imaging.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".