SU‐E‐J‐209: Geometric Distortion at 3T in a Commercial 4D MRI‐Compatible Phantom
Bibliographic record
Abstract
Purpose: There are very few commercial 4D phantoms that are marketed as MRI compatible. We are evaluating one such commercial phantom, made to be used with an MRI‐Linear accelerator. The focus of this work is to characterize the geometric distortions produced in this phantom at 3T using 3 clinical MR pulse sequences. Methods: The CIRS MRI‐Linac Dynamic Phantom (CIRSTM) under investigation in this study consists of a softwaredriven moving tumour volume within a thorax phantom body and enables dose accumulation by placing a dosimeter within the tumour volume. Our initial investigation is to evaluate the phantom in static mode prior to examining its 4D capability. The water‐filled thorax phantom was scanned using a wide‐bore Philips 3T Achieva MRI scanner employing a Thoracic xl coil and clinical 2D T1W FFE, 2D T1W TSE and 3D T1W TFE pulse sequences. Each of the MR image sets was rigidly fused with a reference CT image of the phantom employing a rigid registration with 6 degrees of freedom. Geometric distortions between the MR and CT image sets were measured in 3 dimensions at selected points along the periphery of the distortion grid embedded within the phantom body (11.5, 7.5 and 3 cm laterally, ant/post and sup/inf of magnetic isocenter respectively). Results: The maximal measured geometric distortions between the MR and reference CT points of interest were 0.9, 1.8 and 1.3 mm in the lateral, anteriorposterior and cranio‐caudal directions, respectively. For all 3 spatial dimensions, the maximal distortions occurred for the FFE pulse sequence. Maximal distortions for the 2D FFE, 2D TSE and 3D TFE sequences were 1, 0.7 and 1.8 mm, respectively. Conclusion: Our initial static investigation of this phantom shows minimal geometric distortions at 3T along the periphery of the embedded grid. CIRS has provided us with a phantom at no charge for evaluation at 3 Tesla.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".