SU‐E‐I‐41: The DCE Tool: A Freeware Analysis Tool for DCE CT and MR Studies
Bibliographic record
Abstract
Purpose: To develop a freeware tool for analysis of dynamic contrast enhanced (DCE) CT and MR studies.Methods: The DCE Tool was developed as FREEWARE to work as a plugin for the the ClearCanvas 2.0SP1 framework. It combined the NET Platform and the MATLAB MCR to provide robust DCE analysis. The software was designed to support CT and MR studies with display capability of DCE data. The analysis included simple metrics on time‐intensity curves as well as on compartmental modeling analysis. Easy export of results to spreadsheet file was also a design requirement.Results: The DCE Tool has been developed to process DCE CT and MR studies. DICOM images from CT and MR studies can be loaded to the software and a 4D time browser is available to view the images. Region of interest tool is available which produces time intensity curves, for which the DCE Tool provides analysis on area under curve, initial slope and peak etc. It also provides analysis using the 1‐compartment model, 2‐compartment model, the ATH (adiabatic tissue homogeneity) model for CT, as well as the Toftˈs model for MR study. Pixel‐by‐pixel analysis is available and functional maps can be generated. Numerical and graphic results can readily be exported to EXCEL files. The tool has been used by a number of researchers for phantom and pre‐clinical studies. The DCE Tool is available for free download at www.thedcetool.com Conclusions: The DCE Tool has been developed for DCE CT and MR studies. It provides analysis based on density intensity curves as well as tracer kinetic modeling. The DCE Tool is potentially an invaluable research tool for CT and MR perfusion studies. The project is partly funded by Ontario Institute of Cancer Research
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.043 |
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".