A phantom to assess the accuracy of tumor delineation using MRSI
Bibliographic record
Abstract
A phantom to assess the accuracy of tumor delineation using MRSIBackground. Studies have demonstrated that magnetic resonance spectroscopic imaging (MRSI) can detect regions of abnormal activity (tumor) that would not have been covered using conventional imaging and contouring methods. With increased interest in MRSI it is important that its accuracy in tumor delineation be investigated. While some effort has been made to design phantoms to examine the performance of MRSI sequences, most phantoms rely on using traditional glass or acrylic as the phantom building material.Material and methods. In this work, a gel-based detail phantom has been developed to assess the ability of the spectroscopic imaging sequences to accurately represent the geometry of tumors. The gel-based phantom is used as an alternative to conventional acrylic or glass based phantoms for use with MRSI.Results. Gel-based phantoms have the advantage of having a magnetic susceptibility close to that of water. In addition, we demonstrate the benefits of having no finite wall thickness separating phantom compartments. The utility of the phantom was illustrated in comparisons between different MRSI sequences of the same nominal resolution as well as different filtering parameters.Conclusions. Due to their ease of construction and the reduced artifacts, gel phantoms are a
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".