A 1st generation scatter CT algorithm for electron density breast imaging which accounts for bound incoherent, coherent and multiple scatter: A Monte Carlo study
Bibliographic record
Abstract
Breast CT is an emerging modality that reconstructs 3D linear attenuation coefficient (μ) images of the breast. Its tomographic nature reduces the overlap of structures and may improve tissue visualization. Current prototype systems produce large levels of scatter that could be used to reconstruct electron density (ρ $ _{e}$ ) images. This could potentially enhance diagnosis. We are developing a first generation bench top CT system to investigate the benefits of simultaneous imaging μ and ρ $ _{e}$ of the intact breast. The system uses an algorithm capable of reconstructing ρ $_{e}$ images from single Klein-Nishina scatter. It has been suggested that this algorithm may be impractical since measurements include coherent, bound incoherent and multiple scatter. To investigate this, the EGSnrc Monte Carlo (MC) code was used to simulate scans using a first generation system. These simulations were used to quantify the dose per scan, to provide raw data for the ρ $ _{e}$ reconstructions and to investigate corrections for multiple and coherent scatter since these can not be directly related to ρ $ _{e}$ . MC simulations show that the dose coefficients are similar to those of cone beam breast CT. Coherent scatter is only ∼9% concentrated in scattering angles < 8°. Electron binding reduces the number of incoherently scattered photons but this reduction can be included in the quantification of scatter measured by the system. Multiple scatter was found to be the major source of errors and, if not corrected for, can result in an overestimation of ρ $ _{e}$ by more than a factor of two. Empirical corrections, based on breast thickness or radiological path, can be used to reconstruct images where the variance in ρ $ _{e}$ error is half of that found in images derived from primary photons only. Although some practical challenges remain in creating a laboratory system, this work has shown that it is possible to reconstruct scatter images of the breast with a 4 mGy dose and further experimental evaluation of this technique is warranted.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".