SU‐D‐9A‐03: STAMP: Simulator for Texture Analysis in MRI/PET
Bibliographic record
Abstract
Purpose: To develop a convenient simulation platform to facilitate PET/MR image analysis with the prospect of gaining a better understanding of the influence of acquisition parameters on PET/MRI textural features. The simulation platform is demonstrated by showing textural variations of a representative case study using different image acquisition parameters. Methods: The simulation platform is composed of MRI simulators JEMRIS and SIMRI to achieve simulations of customized MRI sequences on sample tumor models. The PET simulator GATE is used to get 2D and 3D Monte Carlo acquisitions of voxelized PET sources using a phantom geometry and a customized scanner architecture. The platform incorporates a series of graphical user interfaces written in Matlab. Two GUIs are used to facilitate communication with the simulation executables installed on a computer cluster. A third GUI is used to collect and display the clinical and simulated images, as well as fused PET/MRI images, and perform computation of textural features. To illustrate the capabilities of this platform, one FDG‐PET and T1‐weighted (T1w) digitized tumor models were generated from clinical images of a soft‐tissue sarcoma patient. Numerically simulated MR images were produced using 3 different echo times (TE) and 5 different repetition times (TR). PET 2D images were simulated using an OSEM algorithm with 1 to 32 iterations and a post‐reconstruction Gaussian filter of 0, 2, 4 or 6 mm width. Results: STAMP was successfully used to produce numerically simulated FDG‐PET and MRI images, and to calculate their corresponding textures. Three typical textures (GLCM‐Contrast, GLSZM‐ZSV and NGTDM‐Coarseness) were found to vary by a range of 45% on average compared to reference scanning conditions in the case of FDG‐PET, and by a range of 40% in the case of T1w MRI. Conclusion: We have successfully developed a Matlab‐based simulation platform to facilitate PET/MRI texture image analysis for outcome prediction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".