Improved tissue assignment using dual-energy computed tomography in low-dose rate prostate brachytherapy for Monte Carlo dose calculation
Bibliographic record
Abstract
PURPOSE: An improvement in tissue assignment for low-dose rate brachytherapy (LDRB) patients using more accurate Monte Carlo (MC) dose calculation was accomplished with a metallic artifact reduction (MAR) method specific to dual-energy computed tomography (DECT). METHODS: The proposed MAR algorithm followed a four-step procedure. The first step involved applying a weighted blend of both DECT scans (I H/L) to generate a new image (I Mix). This action minimized Hounsfield unit (HU) variations surrounding the brachytherapy seeds. In the second step, the mean HU of the prostate in I Mix was calculated and shifted toward the mean HU of the two original DECT images (I H/L). The third step involved smoothing the newly shifted I Mix and the two original I H/L, followed by a subtraction of both, generating an image that represented the metallic artifact (I A,(H/L)) of reduced noise levels. The final step consisted of subtracting the original I H/L from the newly generated I A,(H/L) and obtaining a final image corrected for metallic artifacts. Following the completion of the algorithm, a DECT stoichiometric method was used to extract the relative electronic density (ρe) and effective atomic number (Z eff) at each voxel of the corrected scans. Tissue assignment could then be determined with these two newly acquired physical parameters. Each voxel was assigned the tissue bearing the closest resemblance in terms of ρe and Z eff, comparing with values from the ICRU 42 database. A MC study was then performed to compare the dosimetric impacts of alternative MAR algorithms. RESULTS: An improvement in tissue assignment was observed with the DECT MAR algorithm, compared to the single-energy computed tomography (SECT) approach. In a phantom study, tissue misassignment was found to reach 0.05% of voxels using the DECT approach, compared with 0.40% using the SECT method. Comparison of the DECT and SECT D 90 dose parameter (volume receiving 90% of the dose) indicated that D 90 could be underestimated by up to 2.3% using the SECT method. CONCLUSIONS: The DECT MAR approach is a simple alternative to reduce metallic artifacts found in LDRB patient scans. Images can be processed quickly and do not require the determination of x-ray spectra. Substantial information on density and atomic number can also be obtained. Furthermore, calcifications within the prostate are detected by the tissue assignment algorithm. This enables more accurate, patient-specific MC dose calculations.
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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".