Contrast Optimization for an Animal Model of Prostate Cancer MRI at 3T
Why this work is in the frame
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Bibliographic record
Abstract
Purpose: To optimize contrast to noise ratio (CNR) in magnetic resonance imaging (MRI) of prostate cancer using at 3T. Methods: CNR was expressed as a difference in MR signals of two samples. Amulti-echo spin-echo (MESE) pulse sequence was used. The theoretical value of the maximum CNR was obtained using the derivative of CNR with echo time (TE) as a variable. The T1 relaxation time was ignored as repetition time (TR) was assumed to be very long (TR >> T1). The theoretical calculations were confirmed with in vitro and in vivo experiments. For in vitro experiments we used samples with different T2 values using various concentrations of super paramagnetic iron oxide (SPIO) and for in vivo experiments we used an animal model of prostate cancer. Results: CNR was maximized by selecting the optimum TE for a multi-echo spin-echo (MESE) pulse sequence based on theoretical predictions. MR images of prostate cancer at 3T were obtained and showed maximum CNR at the predicted TE. Conclusions: It was possible to maximize CNR of prostate tumour by selecting the optimal TE based on simple theoretical calculations. The proposed method can be applied to other pulse sequences and tissues. It can be applied to any MRI system at any magnetic field. However the method requires knowledge of T2 relaxation times.
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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) | 0.000 | 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 it