WE-AB-204-03: A Novel 3D Printed Phantom for 4D PET/CT Imaging and SIB Radiotherapy Verification
Bibliographic record
Abstract
Purpose: To construct and test a 3D printed phantom designed to mimic variable PET tracer uptake seen in lung tumor volumes. To assess segmentation accuracy of sub-volumes of the phantom following 4D PET/CT scanning with ideal and patient-specific respiratory motion. To plan, deliver and verify delivery of PET-driven, gated, simultaneous integrated boost (SIB) radiotherapy plans. Methods: A set of phantoms and inserts were designed and manufactured for a realistic representation of lung cancer gated radiotherapy steps from 4D PET/CT scanning to dose delivery. A cylindrical phantom (40x 120 mm) holds inserts for PET/CT scanning. The novel 3D printed insert dedicated to 4D PET/CT mimics high PET tracer uptake in the core and lower uptake in the periphery. This insert is a variable density porous cylinder (22.12×70 mm), ABS-P430 thermoplastic, 3D printed by uPrint SE Plus with inner void volume (5.5×42 mm). The square pores (1.8×1.8 mm2 each) fill 50% of outer volume, resulting in a 2:1 SUV ratio of PET-tracer in the void volume with respect to porous volume. A matching in size cylindrical phantom is dedicated to validate gated radiotherapy. It contains eight peripheral holes matching the location of the porous part of the 3D printed insert, and one central hole. These holes accommodate adaptors for Farmer-type ion chamber and cells vials. Results: End-to-end test were performed from 4D PET/CT scanning to transferring data to the planning system and target volume delineation. 4D PET/CT scans were acquired of the phantom with different respiratory motion patterns and gating windows. A measured 2:1 18F-FDG SUV ratio between inner void and outer volume matched the 3D printed design. Conclusion: The novel 3D printed phantom mimics variable PET tracer uptake typical of tumors. Obtained 4D PET/CT scans are suitable for segmentation, treatment planning and delivery in SIB gated treatments of NSCLC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".