Development of an MRI-Based Workflow for Post-implant Dosimetry of Prostate Low-Dose-Rate (Ldr) Brachytherapy
Bibliographic record
Abstract
Permanent implantation of low-dose-rate (LDR) brachytherapy seeds is a well-established treatment modality for patients with localized prostate cancer. The quality of the implant is assessed within 30 days following implantation through post-implant dosimetry. The standard recommended procedure for post-implant dosimetry is based on computed tomography (CT). CT provides excellent seed visualization and localization; however, due to poor soft tissue contrast and challenging anatomical identificatio,n it leads to significant interobserver variabilities. The current MRI-CT fusion-based workflow for post-implant dosimetry LDR prostate brachytherapy takes advantage of the superior soft tissue contrast of MRI but still relies on CT for seed visualization and detection, and it suffers from image fusion uncertainties and extra cost and logistics. The lack of positive contrast from brachytherapy seeds in conventional MR images remains a major challenge towards an MRI-only workflow for post-implant dosimetry of Low- Dose-Rate (LDR) brachytherapy. In this thesis, a clinically feasible MRI-based workflow has been developed for brachytherapy seed visualization and localization. The seed visualization is based on a novel Quantitative Susceptibility Mapping (QSM) algorithm. The proposed seed localization on QSM utilizes machine learning algorithms. The reliability of the proposed workflow has been validated on 23 patients by comparing the seed positions and final dosimetric parameters between the proposed MRI-only workflow and the clinical CT-MRI fusion-based approach and there was excellent agreement between the two methods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".