Poster - 05: Automated analysis of MR distortion using a novel anthropomorphic phantom and open source software
Bibliographic record
Abstract
Purpose: MRI in stereotactic radiosurgery is the primary imaging modality, but due to inherent distorting effects MR images have significant geometric distortions. In this work we present framework to calculate, visualize, track MRI distortion using a combination of open source tools, and in-house software on a novel anthropomorphic head phantom. Methods: The phantom used in this study was an anthropomorphic skull containing a 3D orthogonal grid of rods whose intersections are used as principle points for CT and MR image comparison. High resolution CT images were taken of the phantom. MRI images of the phantom were obtained on our 3T MRI system using a three-dimensional T1 weighted sequence. MRI data was collected with and without using Siemens model based 3D distortion correction method. MRI images were automatically rigidly registered to the CT images. For comparison, images were automatically rigidly registered using the Velocity software. The same process was repeated using deformable imaging methods. A point-by-point comparison of the centroids generates a distortion map which can be applied to subsequent patient MR images. This distortion map is compared to a baseline value and monitored over time using an open source tracking software. Results and Conclusions: We have developed in-house software to identify principle points within the phantom using CT imaging to a high spacing accuracy. With the current implemented imaging optimization, this open source software framework has promise in creating accurate distortion maps, and and thus could be used in a stereotactic radiosurgery setting for routine quality assurance.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".