Potential applications of the quantitative susceptibility mapping (QSM) in MR-guided radiation therapy
Bibliographic record
Abstract
Magnetic resonance-guided radiation therapy (MR-GRT) offers great potential to improve radiation treatment outcomes by providing more accurate and patient-tailored therapy. Despite superior soft tissue contrast in MRI, one of the challenges towards MRI-only workflows is that the process often requires some sort of 'MR-invisible' metal-based devices. In this study, the feasibility of quantitative susceptibility mapping (QSM) for visualization of some MR-invisible radiation therapy devices was studied. Our recently proposed QSM-based algorithm for brachytherapy seed visualization was modified and the feasibility of the optimized algorithm for visualization of different devices including: brachytherapy seeds, plastic interstitial needles, CT-markers and obturators, and different types of fiducial markers in agar, prostate and meat phantoms were studied. All phantoms were scanned using 3T MR scanner with a 3D multi-echo gradient recalled echo (ME-GRE) pulse sequence. The QSM results in all phantoms were compared to CT images for spatial accuracy of the QSM. The applied post-processing algorithm was found to be insensitive to the seeds' type; also, presence of nearby calcifications had no effect on seed visualization. QSM successfully generated positive contrast for both types of investigated fiducial markers with high spatial accuracy compared to CT. Interstitial needles containing both aluminum-based CT-maker and titanium-based obturators were accurately depicted on the QSM. The proposed QSM-based technique relies on the standard MR pulse sequences and visualize the conventional MR-invisible metallic devices with CT-like positive contrast solely through post-processing. Upon in vivo validation of the technique, QSM may have the potential to replace CT for an MR-only guided radiation therapy.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".