TU-AB-201-11: A Novel Theoretical Framework for MRI-Only Image Guided LDR Prostate and Breast Brachytherapy Implant Dosimetry
Bibliographic record
Abstract
Purpose: To propose a novel framework for accurate model-based dose calculations using only MR images for LDR prostate and breast seed implant brachytherapy. Methods: Model-based dose calculation methodologies recommended by TG-186 require further knowledge about specific tissue composition, which is challenging with MRI. However, relying on MRI-only for implant dosimetry would reduce the soft tissue delineation uncertainty, costs, and uncertainties associated with multi-modality registration and fusion processes. We propose a novel framework to address this problem using quantitative MRI acquisitions and reconstruction techniques. The framework includes three steps: (1) Identify the locations of seeds(2) Identify the presence (or absence) of calcification(s)(3) Quantify the water and fat content in the underlying tissueSteps (1) and (2) consider the sources that limit patient dosimetry, particularly the inter-seed attenuation and the calcified regions; while step (3) targets the quantification of the tissue composition to consider the heterogeneities in the medium. Our preliminary work has shown that the seeds and the calcifications can be identified with MRI using both the magnitude and the phase images. By employing susceptibility-weighted imaging with specific post-processing techniques, the phase images can be further explored to distinguish the seeds from the calcifications. Absolute quantification of tissue, water, and fat content is feasible and was previously demonstrated in phantoms and in-vivo applications, particularly for brain diseases. The approach relies on the proportionality of the MR signal to the number of protons in an image volume. By employing appropriate correction algorithms for T1 - and T2*-related biases, B1 transmit and receive field inhomogeneities, absolute water/fat content can be determined. Results: By considering calcification and interseed attenuation, and through the knowledge of water and fat mass density, accurate patient-specific implant dosimetry can be achieved with MRI-only. Conclusion: The proposed framework showed that model-based dose calculation is feasible using MRI-only state-of-the-art techniques.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".