MRI of prostate brachytherapy seeds at high field: A study in phantom
Bibliographic record
Abstract
Postimplant evaluation of prostate brachytherapy using magnetic resonance imaging (MRI) at 1.5 T has met with some difficulties due to the uncertainty associated with seed localization despite the excellent anatomical delineation this imaging modality can achieve. Seeds in vascularized regions or outside the prostate, where signal heterogeneity or drop off can obscure their position, can be difficult to identify. The increase in SNR available at 3.0 T offers the potential to improve these issues with visualization. However, before moving directly to in vivo studies, it is important to investigate the effects of artifact size on the ability to localize multiple seeds in close proximity. These artifacts are of extra concern at higher field because of the increased induced field distortions surrounding the seeds. A single prostate brachytherapy seed (IMC6711, OncoSeed) and arrays of seed pairs were suspended in a porcine gel medium and imaged on 1.5 and 3 T MRI scanners for comparison. Two basic acquisition techniques utilized in a wide array of clinical sequences [spin-echo based and gradient-echo (GE) based] were investigated for the types of artifacts they produce, and their dependence on field. Analysis of the resulting voids was performed to determine the relative size of seeds as seen on the images, as well as the ability to distinguish seeds at close proximity. The seed voids at 3 T were only slightly larger than those obtained at 1.5 T (0.5 mm longer and wider) when using a spin-echo type sequence. For this work, the authors used a proton density fast spin-echo (FSE) sequence. These results are promising for the use of 3 T imaging for postimplant evaluation since the SNR will increase by roughly a factor of 2 with only a limited corresponding increase in artifact size. The minimum separation of the seeds to be completely distinguished using void analysis increased from between 1.5 and 3 mm to between 3 and 4.5 mm when going from 1.5 to 3 T FSE imaging. The minimum separation of the seeds for GE at the demonstration TE of 11 ms was found to be between 3 and 4.5 mm for 1.5 T and between 4.5 and 6 mm for 3 T. These GE artifact dimensions will scale down with TE and, as this happens, approach the dimensions of the FSE artifacts given above.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".