Metallic artifact mitigation and organ-constrained tissue assignment for Monte Carlo calculations of permanent implant lung brachytherapy
Bibliographic record
Abstract
PURPOSE: To investigate methods of generating accurate patient-specific computational phantoms for the Monte Carlo calculation of lung brachytherapy patient dose distributions. METHODS: Four metallic artifact mitigation methods are applied to six lung brachytherapy patient computed tomography (CT) images: simple threshold replacement (STR) identifies high CT values in the vicinity of the seeds and replaces them with estimated true values; fan beam virtual sinogram replaces artifact-affected values in a virtual sinogram and performs a filtered back-projection to generate a corrected image; 3D median filter replaces voxel values that differ from the median value in a region of interest surrounding the voxel and then applies a second filter to reduce noise; and a combination of fan beam virtual sinogram and STR. Computational phantoms are generated from artifact-corrected and uncorrected images using several tissue assignment schemes: both lung-contour constrained and unconstrained global schemes are considered. Voxel mass densities are assigned based on voxel CT number or using the nominal tissue mass densities. Dose distributions are calculated using the EGSnrc user-code BrachyDose for (125)I, (103)Pd, and (131)Cs seeds and are compared directly as well as through dose volume histograms and dose metrics for target volumes surrounding surgical sutures. RESULTS: Metallic artifact mitigation techniques vary in ability to reduce artifacts while preserving tissue detail. Notably, images corrected with the fan beam virtual sinogram have reduced artifacts but residual artifacts near sources remain requiring additional use of STR; the 3D median filter removes artifacts but simultaneously removes detail in lung and bone. Doses vary considerably between computational phantoms with the largest differences arising from artifact-affected voxels assigned to bone in the vicinity of the seeds. Consequently, when metallic artifact reduction and constrained tissue assignment within lung contours are employed in generated phantoms, this erroneous assignment is reduced, generally resulting in higher doses. Lung-constrained tissue assignment also results in increased doses in regions of interest due to a reduction in the erroneous assignment of adipose to voxels within lung contours. Differences in dose metrics calculated for different computational phantoms are sensitive to radionuclide photon spectra with the largest differences for (103)Pd seeds and smallest but still considerable differences for (131)Cs seeds. CONCLUSIONS: Despite producing differences in CT images, dose metrics calculated using the STR, fan beam + STR, and 3D median filter techniques produce similar dose metrics. Results suggest that the accuracy of dose distributions for permanent implant lung brachytherapy is improved by applying lung-constrained tissue assignment schemes to metallic artifact corrected images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".