SU‐GG‐J‐11: Contrast to Noise Ratio Measurements for Real Time MR Lung Tumour Imaging Sequences at Lower Fields — A Phantom Study
Bibliographic record
Abstract
Purpose: Magnetic Resonance Imaging (MRI) has been proposed for real‐time image guidance during lung radiotherapy treatment. Dynamic lung MR studies in the literature have demonstrated the feasibility of real‐time tumour tracking at 1.5T scanners. Lower field magnets offer several advantages over high field magnets, but has lower signal to noise ratio and a different contrast environment due to field strength dependences in T1 and T2*. The purpose of this study was to determine the expected contrast to noise ratio (CNR) at 0.2T for several rapid MR sequences by performing experiments in a 3T MRI. Method and Materials: Lung tumour is simulated by loading solution containing CuSO4 and MnCl2 in a sphere. To simulate the lower relative proton densities (PD) of lung parenchyma, 2mm acrylic beads are uniformly suspended in gelatin. T1, T2 and T2* and relative PD are measured for the phantom in 3T and compared against their expected values at 0.2T from literature. For real time imaging, rapid gradient echo sequences (FLASH and balanced SSFP) are used to acquire images from 3–10 frames per second using acceleration techniques of halfscan and parallel acquisition. A dynamic noise scan is used to estimate noise and is adjusted to reflect the lower SNR at 0.2T. Measurements are repeated using a body coil and a 6 channel thoracic SENSE coil for parallel imaging. Results: The measured T1, T2 and T2* and relative PD of the phantom are similar to the values given in literature. For dynamic lung images, CNR ranges from 9.4–31.7 for bSSFP and 4.0–13.8 for FLASH. In house auto‐contouring algorithm shows good quality contours of spherical tumours with CNR > 2.5. Conclusion: In this phantom study, dynamic lung imaging sequences are shown to provide sufficient tumour‐tissue CNR and temporal resolution for real time MR lung tumour tracking at 0.2T.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".