Feasibility of using a single MRI acquisition for fiducial marker localization and synthetic CT generation towards MRI-only prostate radiation therapy treatment planning
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Purpose. To investigate the feasibility of using a single MRI acquisition for fiducial marker identification and synthetic CT (sCT) generation towards MRI-only treatment planning for prostate external beam radiation therapy (EBRT). Methods. Seven prostate cancer patients undergoing EBRT, each with three implanted gold fiducial markers, participated in this study. In addition to the planning CT scan, all patients were scanned on a 3 T MR scanner with a 3D double-echo gradient echo (GRE) sequence. Quantitative susceptibility mapping (QSM) was performed for marker localization. QSM-derived marker positions were compared to those from CT. The bulk density assignment technique for sCT generation was adopted. The magnitude GRE images were segmented into muscle, bone, fat, and air using a combination of unsupervised intensity-based classification of soft tissue and convolutional neural networks (CNN) for bone segmentation. Results. All implanted markers were visualized and accurately identified (average error: 0.7 ± 0.5 mm). QSM generated distinctive contrast for hemorrhage, calcifications, and gold fiducial markers. The estimated susceptibility/HU values on QSM/CT for gold and calcifications were 31.5 ± 2.9 ppm/1220 ± 100 HU and 14.6 ± 0.9 ppm/440 ± 100 HU, respectively. The intensity-based soft tissue classification resulted in an average Dice score of 0.97 ± 0.02; bone segmentation using CNN resulted in an average Dice score of 0.93 ± 0.03. Conclusion. This work indicates the feasibility of simultaneous fiducial marker identification and sCT generation using a single MRI acquisition. Future works includes evaluation of the proposed method in a large cohort of patients with optimized acquisition parameters as well as dosimetric evaluations.
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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