Parametrization of multi-energy CT projection data with eigentissue decomposition
Bibliographic record
Abstract
Abstract The purpose of this work is, firstly, to propose an optimized parametrization of the attenuation coefficient to describe human tissues in the context of projection-based material characterization with multi-energy CT. The approach is based on eigentissue decomposition (ETD). Secondly, to evaluate its benefits in terms of accuracy and precision of radiotherapy-related parameters against established parametrizations. The attenuation coefficient is parametrized as a linear combination of virtual materials, eigentissues, obtained by performing principal component analysis on a set of reference tissues in order to optimally represent human tissue composition. Two implementations of ETD are compared with other pre-reconstruction formalisms established for dual-energy and photon-counting CT in a simulation framework. The first implementation uses a single set of eigentissues to describe all human tissues, while the second uses different sets of eigentissues to characterize soft tissues and bones, and includes a post-reconstruction classification step. The simulation framework evaluates the reconstruction accuracy of various radiotherapy-related quantities over a range of 71 human tissues for various noise levels. Compared to conventional parametrizations, the first implementation of ETD reduces the mean error and root-mean-square error (RMSE) in two radiotherapy-related quantities (the proton stopping power and the mass energy absorption coefficient of 21 keV photons from 103 Pd seeds used in brachytherapy) for all noise levels and modalities investigated. This illustrates that a decomposition basis selected with principal component analysis is superior to an arbitrary pair of materials to describe human tissues. The mean error on radiotherapy-related parameters can be further reduced with the classification-based approach. In the context of pre-reconstruction material characterization with multi-energy CT, parametrizing the attenuation coefficient with eigentissues provides a more accurate and precise evaluation of human tissues properties for radiotherapy. Accurate quantification can thus be achieved without the need to parametrize tissues using unphysical parameters, such as the energy-dependent effective atomic number.
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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".