Dynamic volume vs respiratory correlated 4DCT for motion assessment in radiation therapy simulation
Bibliographic record
Abstract
PURPOSE: Conventional (i.e., respiratory-correlated) 4DCT exploits the repetitive nature of breathing to provide an estimate of motion; however, it has limitations due to binning artifacts and irregular breathing in actual patient breathing patterns. The aim of this work was to evaluate the accuracy and image quality of a dynamic volume, CT approach (4D(vol)) using a 320-slice CT scanner to minimize these limitations, wherein entire image volumes are acquired dynamically without couch movement. This will be compared to the conventional respiratory-correlated 4DCT approach (RCCT). METHODS: 4D(vol) CT was performed and characterized on an in-house, programmable respiratory motion phantom containing multiple geometric and morphological "tumor" objects over a range of regular and irregular patient breathing traces obtained from 3D fluoroscopy and compared to RCCT. The accuracy of volumetric capture and breathing displacement were evaluated and compared with the ground truth values and with the results reported using RCCT. A motion model was investigated to validate the number of motion samples needed to obtain accurate motion probability density functions (PDF). The impact of 4D image quality on this accuracy was then investigated. Dose measurements using volumetric and conventional scan techniques were also performed and compared. RESULTS: Both conventional and dynamic volume 4DCT methods were capable of estimating the programmed displacement of sinusoidal motion, but patient breathing is known to not be regular, and obvious differences were seen for realistic, irregular motion. The mean RCCT amplitude error averaged at 4 mm (max. 7.8 mm) whereas the 4D(vol) CT error stayed below 0.5 mm. Similarly, the average absolute volume error was lower with 4D(vol) CT. Under irregular breathing, the 4D(vol) CT method provides a close description of the motion PDF (cross-correlation 0.99) and is able to track each object, whereas the RCCT method results in a significantly different PDF from the ground truth, especially for smaller tumors (cross-correlation ranging between 0.04 and 0.69). For the protocols studied, the dose measurements were higher in the 4D(vol) CT method (40%), but it was shown that significant mAs reductions can be achieved by a factor of 4-5 while maintaining image quality and accuracy. CONCLUSIONS: 4D(vol) CT using a scanner with a large cone-angle is a promising alternative for improving the accuracy with which respiration-induced motion can be characterized, particularly for patients with irregular breathing motion. This approach also generates 4DCT image data with a reduced total scan time compared to a RCCT scan, without the need for image binning or external respiration signals within the 16 cm scan length. Scan dose can be made comparable to RCCT by optimization of the scan parameters. In addition, it provides the possibility of measuring breathing motion for more than one breathing cycle to assess stability and obtain a more accurate motion PDF, which is currently not feasible with the conventional RCCT approach.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".