Poster — Wed Eve—46: Quantitative Evaluation on the Accuracy of Image Registration Methods in SPECT Guided Radiation Therapy for Lung Cancer Patients
Bibliographic record
Abstract
Purpose: To quantitatively evaluate the accuracy of several SPECT/CT image registration methods in recent studies and its impact on the functional lung volume segmentation in SPECT guided radiation therapy (RT) treatment planning. Methods and Materials: Five lung cancer patients were consented to have a perfusion SPECT scan with 99mTc‐macroaggregated albumin. During the scan, a low resolution CT image was acquired using the SPECT/CT scanner. This CT scan was co‐registered to the patient's planning CT scan through four rigid and deformable image registration programs (rigid registration, skin/lung control points based registration and B‐spline deformable registration). After the CT to CT co‐registration, original SPECT reconstructions were warped and co‐registered to the planning CT scan. The functional lung volumes were segmented from each deformed SPECT using 10, 20, …, 90% of maximum pixel value as a threshold. The differences in the size and contours of each functional volume were calculated. Results: Based on the evaluation of registered CT images, the result from B‐spline registration demonstrated the smallest intensity difference. Using the warped SPECT images obtained from this registration method as a reference, the smallest difference in the size and contour of functional volumes was found using rigid registration. In the point‐based registrations, a better result was found when the control points were placed on lung volume instead of body contour. Conclusion: Apply B‐spline based image registration method in SPECT‐guided RT studies was shown to be accurate. Point‐based image registration using skin markers with a standalone SPECT scanner was found least accurate.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".