A method for patient dose reduction in dynamic contrast enhanced CT study
Bibliographic record
Abstract
PURPOSE: In dynamic contrast enhanced CT (DCE-CT) study, prolonged CT scanning with high temporal resolution is required to give accurate and precise estimates of kinetic parameters. However, such scanning protocol could lead to substantial radiation dose to the patient. A novel method is proposed to reduce radiation dose to patient, while maintaining high accuracy for kinetic parameter estimates in DCE-CT study. METHODS: The method is based on a previous investigation that the arterial impulse response (AIR) in DCE-CT study can be predicted using a population-based scheme. In the proposed method, DCE-CT scanning is performed with relatively low temporal resolution, hence, giving rise to reduction in patient dose. A novel method is proposed to estimate the arterial input function (AIF) based on the coarsely sampled AIF. By using the estimated AIF in the tracer kinetic analysis of the coarsely sampled DCE-CT study, the calculated kinetic parameters are able to achieve a high degree of accuracy. The method was tested on a DCE-CT data set of 48 patients with cervical cancer scanned at high temporal resolution. A random cohort of 34 patients was chosen to construct the orthonormal bases of the AIRs via singular value decomposition method. The determined set of orthonormal bases was used to fit the AIFs in the second cohort (14 patients) at varying levels of down sampling. For each dataset in the second cohort, the estimated AIF was used for kinetic analyses of the modified Tofts and adiabatic tissue homogeneity models for each of the down-sampling schemes between intervals from 2 to 15 s. The results were compared with analyses done with the "raw" down-sampled AIF. RESULTS: In the first group of 34 patients, there were 11 orthonormal bases identified to describe the AIRs. The AIFs in the second group were estimated in high accuracy based on the 11 orthonormal bases established in the first group along with down-sampled AIFs. Using the 11 orthonormal bases, the estimated AIFs for the second group were found to have an averaged maximal percentage error of 3.4% ± 7.5% in all sampling schemes up to 15 s. The results of kinetic analysis with the proposed method compared with down sampling alone showed that the proposed method is superior in maintaining the accuracy in volume transfer constant (K(trans) ) after 9 s down-sampling interval, blood volume (v(b) ) for almost all down-sampling intervals, and blood flow (F) after 11 s down-sampling interval. The preliminary results suggested that the proposed method is able to support scanning intervals of 10-15 s at a cost of 6.2%-10.0% loss in accuracy of K(trans) and 10.9%-19.4% in v(b), and the scanning intervals of 12-15 s at a cost of 9.7%-14.6% for F in DEC-CT studies for patients with cervix cancer. CONCLUSIONS: The proposed method of AIF estimation allows low scanning frequency in DCE-CT study to reduce radiation dose to patient, while maintaining relatively high accuracy in the kinetic parameter estimates. The initial results suggested that the method is applicable for DCE-CT studies for patients with cervical cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".