The Use of Cone Beam Computed Tomography for Image Guided Gamma Knife Stereotactic Radiosurgery: Initial Clinical Evaluation
Bibliographic record
Abstract
PurposeThe present study used cone beam computed tomography (CBCT) to measure the inter- and intrafraction uncertainties for intracranial stereotactic radiosurgery (SRS) using the Leksell Gamma Knife (GK).Methods and MaterialsUsing a novel CBCT system adapted to the GK radiosurgery treatment unit, CBCT images were acquired immediately before and after treatment for each treatment session within the context of a research ethics board–approved prospective clinical trial. Patients were immobilized in the Leksell coordinate frame (LCF) for both volumetric CBCT imaging and GK-SRS delivery. The relative displacement of the patient's skull to the stereotactic reference (interfraction motion) was measured for each CBCT scan. Differences between the pre- and post-treatment CBCT scans were used to determine the intrafraction motion.ResultsWe analyzed 20 pre- and 17 post-treatment CBCT scans in 20 LCF patients treated with SRS. The mean translational pretreatment setup error ± standard deviation in the left-right, anteroposterior, and craniocaudal directions was −0.19 ± 0.32, 0.06 ± 0.27, and −0.23 ± 0.2 mm, with a maximum of −0.74, −0.53, and −0.68 mm, respectively. After an average time between the pre- and post-treatment CBCT scans of 82 minutes (range 27-170), the mean intrafraction error ± standard deviation for the LCF was −0.03 ± 0.05, −0.03 ± 0.18, and −0.03 ± 0.12 mm in the left-right, anteroposterior, and craniocaudual direction, respectively.ConclusionsUsing CBCT on a prototype image guided GK Perfexion unit, we were able to measure the inter- and intrafraction positional changes for GK-SRS using the invasive frame. In the era of image guided radiation therapy, the use of CBCT image guidance for both frame- and non–frame-based immobilization systems could serve as a useful quality assurance tool. Our preliminary measurements can guide the application of achievable thresholds for inter- and intrafraction discrepancy when moving to a frameless approach. The present study used cone beam computed tomography (CBCT) to measure the inter- and intrafraction uncertainties for intracranial stereotactic radiosurgery (SRS) using the Leksell Gamma Knife (GK). Using a novel CBCT system adapted to the GK radiosurgery treatment unit, CBCT images were acquired immediately before and after treatment for each treatment session within the context of a research ethics board–approved prospective clinical trial. Patients were immobilized in the Leksell coordinate frame (LCF) for both volumetric CBCT imaging and GK-SRS delivery. The relative displacement of the patient's skull to the stereotactic reference (interfraction motion) was measured for each CBCT scan. Differences between the pre- and post-treatment CBCT scans were used to determine the intrafraction motion. We analyzed 20 pre- and 17 post-treatment CBCT scans in 20 LCF patients treated with SRS. The mean translational pretreatment setup error ± standard deviation in the left-right, anteroposterior, and craniocaudal directions was −0.19 ± 0.32, 0.06 ± 0.27, and −0.23 ± 0.2 mm, with a maximum of −0.74, −0.53, and −0.68 mm, respectively. After an average time between the pre- and post-treatment CBCT scans of 82 minutes (range 27-170), the mean intrafraction error ± standard deviation for the LCF was −0.03 ± 0.05, −0.03 ± 0.18, and −0.03 ± 0.12 mm in the left-right, anteroposterior, and craniocaudual direction, respectively. Using CBCT on a prototype image guided GK Perfexion unit, we were able to measure the inter- and intrafraction positional changes for GK-SRS using the invasive frame. In the era of image guided radiation therapy, the use of CBCT image guidance for both frame- and non–frame-based immobilization systems could serve as a useful quality assurance tool. Our preliminary measurements can guide the application of achievable thresholds for inter- and intrafraction discrepancy when moving to a frameless 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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".