Deriving concentrations of oxygen and carbon in human tissues using single- and dual-energy CT for ion therapy applications
Bibliographic record
Abstract
Dedicated methods of in-vivo verification of ion treatment based on the detection of secondary emitted radiation, such as positron-emission-tomography and prompt gamma detection require high accuracy in the assignment of the elemental composition. This especially concerns the content in carbon and oxygen, which are the most abundant elements of human tissue. The standard single-energy computed tomography (SECT) approach to carbon and oxygen concentration determination has been shown to introduce significant discrepancies in the carbon and oxygen content of tissues. We propose a dual-energy CT (DECT)-based approach for carbon and oxygen content assignment and investigate the accuracy gains of the method. SECT and DECT Hounsfield units (HU) were calculated using the stoichiometric calibration procedure for a comprehensive set of human tissues. Fit parameters for the stoichiometric calibration were obtained from phantom scans. Gaussian distributions with standard deviations equal to those derived from phantom scans were subsequently generated for each tissue for several values of the computed tomography dose index (CTDIvol). The assignment of %weight carbon and oxygen (%wC,%wO) was performed based on SECT and DECT. The SECT scheme employed a HU versus %wC,O approach while for DECT we explored a Zeff versus %wC,O approach and a (Zeff, ρe) space approach. The accuracy of each scheme was estimated by calculating the root mean square (RMS) error on %wC,O derived from the input Gaussian distribution of HU for each tissue and also for the noiseless case as a limiting case. The (Zeff, ρe) space approach was also compared to SECT by comparing RMS error for hydrogen and nitrogen (%wH,%wN). Systematic shifts were applied to the tissue HU distributions to assess the robustness of the method against systematic uncertainties in the stoichiometric calibration procedure. In the absence of noise the (Zeff, ρe) space approach showed more accurate %wC,O assignment (largest error of 2%) than the Zeff versus %wC,O and HU versus %wC,O approaches (largest errors of 15% and 30%, respectively). When noise was present, the accuracy of the (Zeff, ρe) space (DECT approach) was decreased but the RMS error over all tissues was lower than the HU versus %wC,O (SECT approach) (5.8%wC versus 7.5%wC at CTDIvol = 20 mGy). The DECT approach showed decreasing RMS error with decreasing image noise (or increasing CTDIvol). At CTDIvol = 80 mGy the RMS error over all tissues was 3.7% for DECT and 6.2% for SECT approaches. However, systematic shifts greater than ±5HU undermined the accuracy gains afforded by DECT at any dose level. DECT provides more accurate %wC,O assignment than SECT when imaging noise and systematic uncertainties in HU values are not considered. The presence of imaging noise degrades the DECT accuracy on %wC,O assignment but it remains superior to SECT. However, DECT was found to be sensitive to systematic shifts of human tissue HU.
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".