A 3D printed modular phantom for quality assurance of image‐guided small animal irradiators: Design, imaging experiments, and Monte Carlo simulations
Bibliographic record
Abstract
PURPOSE: The goal of this work was to develop and test a cylindrical tissue-equivalent quality assurance (QA) phantom for micro computed tomography (microCT) image-guided small animal irradiators that overcomes deficiencies of existing phantoms due to its mouse-like dimensions and composition. METHODS: The 8.6-cm-long and 2.4-cm-diameter phantom was three-dimensionally (3D) printed out of Somos NeXt plastic on a stereolithography (SLA) printer. The modular phantom consisted of four sections: (a) CT number evaluation section, (b) spatial resolution with slanted edge (for the assessment of longitudinal resolution) and targeting section, (c) spatial resolution with hole pattern (for the assessment of radial direction) section, and (d) uniformity and geometry section. A Python-based graphical user interface (GUI) was developed for automated analysis of microCT images and evaluated CT number consistency, longitudinal and radial modulation transfer function (MTF), image uniformity, noise, and geometric accuracy. The phantom was placed at the imaging isocenter and scanned with the small animal radiation research platform (SARRP) in the pancake geometry (long axis of the phantom perpendicular to the axis of rotation) with a variety of imaging protocols. Tube voltage was set to 60 and 70 kV, tube current was set to 0.5 and 1.2 mA, voxel size was set to 200 and 275 μm, imaging times of 1, 2, and 4 min were used, and frame rates of 6 and 12 frames per second (fps) were used. The phantom was also scanned in the standard (long axis of the phantom parallel to the axis of rotation) orientation. The quality of microCT images was analyzed and compared to recommendations presented in our previous work that was derived from a multi-institutional study. Additionally, a targeting accuracy test with a film placed in the phantom was performed. MicroCT imaging of the phantom was also simulated in a modified version of the EGSnrc/DOSXYZnrc code. Images of the resolution section with the hole pattern were acquired experimentally as well as simulated in both the pancake and the standard imaging geometries. The radial spatial resolution of the experimental and simulated images was evaluated and compared to experimental data. RESULTS: For the centered phantom images acquired in the pancake geometry, all imaging protocols passed the spatial resolution criterion in the radial direction (>1.5 lp/mm @ 0.2 MTF), the geometric accuracy criterion (<200 μm), and the noise criterion (<55 HU). Only the imaging protocol with 200-μm voxel size passed the criterion for spatial resolution in the longitudinal direction (>1.5 lp/mm @ 0.2 MTF). The 70-kV tube voltage dataset failed the bone CT number consistency test (<55 HU). Due to cupping artifacts, none of the imaging protocols passed the uniformity test of <55 HU. When the phantom was scanned in the standard imaging geometry, image uniformity and longitudinal MTF were satisfactory; however, the CT number consistency failed the recommended limit. A targeting accuracy of 282 and 251 μm along the x- and z-direction was observed. Monte Carlo simulations confirmed that the radial spatial resolution for images acquired in the pancake geometry was higher than the one acquired in the standard geometry. CONCLUSIONS: The new 3D-printed phantom presents a useful tool for microCT image analysis as it closely mimics a mouse. In order to image mouse-sized animals with acceptable image quality, the standard protocol with a 200-μm voxel size should be chosen and cupping artifacts need to be resolved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".