TU‐H‐BRA‐09: Relationship Between B0 and the Contrast‐To‐Noise Ratio (CNR) of Tumour to Background for MRI/Radiotherapy Hybrids
Bibliographic record
Abstract
Purpose: To investigate the relationship in MRI between B0 and the contrast‐to‐noise ratio (CNR) of various tumour/normal tissue pairs. This study is motivated by the current interest in MRI/radiotherapy hybrids, for which multiple magnetic field strengths have been proposed. CNR is the single most important parameter governing the ability of a system to identify a tumour in real time for treatment guidance. The MRI community has long since recognized that the SNR of a well‐designed MR system is roughly proportional to B0, the polarizing magnetic field. However, the CNR between two tissues is much more complicated ‐ dependent not only on this signal behavior, but also on the different relaxation properties of the tissues. Methods: Experimentally‐based models of B0‐dependant relaxation for various tumour and normal tissues from the literature were used in conjunction with signal equations for MR sequences suitable for rapid realtime imaging to develop field‐dependent predictions for CNR. These CNR models were developed for liver, lung, breast, glioma, and kidney tumours for spoiled‐gradient echo (SGE) and balanced steady‐state free precession (bSSFP) sequences. Results: In all cases there was an improved CNR at lower fields compared to linear dependency. Further, in some tumour sites, the CNR at lower fields was found to be comparable to, or sometimes higher than those at higher fields (i.e. bSSFP CNR for glioma, kidney and liver tumours). Conclusion: Due to the variation of tissue relaxation parameters with field, lower B0 fields have been shown to perform as well or better (in terms of CNR) than higher fields for some tumour sites. In other sites this effect was less pronounced. It is the complex relationship between CNR and B0 that leads to greater CNR at 0.5 T for certain tumour types studied here for fast imaging. B. Gino Fallone is a co‐founder and CEO of MagnetTx Oncology Solutions (under discussions to license Alberta bi‐planar linac MR for commercialization)
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".