Delineation of moving targets with slow MVCT scans: implications for adaptive non-gated lung tomotherapy
Bibliographic record
Abstract
Accurate imaging is a prerequisite for adaptive radiation therapy of mobile tumours. We present an evaluation of the performance of slow computed tomography (CT) for mapping and delineating the excursion boundary of a moving object using a tumour phantom scanned with the helical MVCT scanner of a tomotherapy unit. A spherical test object driven by sinusoidal motion in both the lateral and cranial-caudal directions was used to determine how well MVCT images depict the true envelope of the motion. Such information is useful in interpreting the CT images relative to the static object case when radiotherapy gating is to be used or in determining the internal target volume (ITV) when beam gating is not possible. A computer simulation of the CT imaging process was developed which incorporates the third generation fan beam geometry and helical acquisition technique of the tomotherapy MVCT system. Motion artefacts are mainly characterized by the parameter alpha=Tgantry/Trespiration which is interpreted as the period of the gantry rotation (Tgantry) in units of the respiratory period (Trespiration). Experimental tests were performed using a fixed gantry period of 10 s per full rotation and respiratory period ranging from 4.0 (alpha=2.5) to 1.0 (alpha=10) s. These cases represent typical clinical imaging conditions on the tomotherapy unit, as well as an extreme test case where the gantry period is intentionally set to be much greater than the respiratory period (termed an 'ultra-slow' scan). The accuracy of target (ITV) delineation is evaluated by comparing volumes generated using iso-density contours on the MVCT images to the true motion envelope, known a priori in this phantom study. As expected, motion artefacts are present in clinical MVCT images and they are not averaged over the slow gantry period of rotation. Furthermore, artefacts are not significantly affected by scanning with different helical pitch values. Greater distortions from the true density distribution are observed for lateral motion compared to cranial-caudal motion. Volumes generated by iso-density contours yield better agreement with the motion envelope for scans performed under ultra-slow conditions (alpha=10) compared to typical clinical imaging conditions (alpha=2.5). If the MVCT gantry cannot be rotated very quickly due to engineering constraints in order to achieve ultra-fast CT, we suggest an opposite approach as an interim measure for mapping the ITV. Adjusting MVCT scan conditions to a very slow acquisition (alpha=10) may be a good compromise for determining the ITV for non-gated adaptive tomotherapy of moving lung tumours.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".