Eliminating computed tomography imaging artifacts through 3D printed radiotherapy head supports
Bibliographic record
Abstract
PURPOSE: The geometry of an immobilization device such as a headrest can cause undesired computed tomography (CT) artifacts that may affect both volume definition and dosimetry in radiotherapy of the brain. The purpose of this work was to reduce CT artifacts caused by a standard hard plastic hollow radiotherapy headrest. This was to be achieved through design and prototyping of a custom-made head support. METHODS: A series of CT scans were acquired of both a water phantom and an anthropomorphic head phantom which were resting on custom-made three-dimensional (3D) printed supports. All custom-made supports were made of polylactic acid (PLA) plastic filament and printed by fused deposition modeling (FDM) 3D printing technology. Initial designs were studied with a water phantom using a simplified support with straight and curved shapes both at the edges and as infill patterns. Imaging of a 3D printed clinical prototype was then compared to our standard headrest using an anthropomorphic head phantom. RESULTS: The presence of dark streaks inside both phantoms was seen on the CT images for headrests involving supports with straight shapes at the edges or as infill patterns. Such artifacts were ascribed to the exponential edge-gradient effect (EEGE). No such artifact was observed when the support was designed with a combination of curved edges and infill patterns. CONCLUSION: When developing immobilization accessories for use in CT scanners, more attention could be paid to artifact attenuating design elements. This work illustrates the usefulness of 3D printing in prototyping radiotherapy accessories and solving concrete clinical problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| 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 teacher head, 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".