Assessing the Sensitivity of Dual-Energy Computed Tomography 3-Material Decomposition for the Detection of Gout
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to assess the accuracy and precision of a novel application of 3-material decomposition (3MD) with virtual monochromatic images (VMIs) in the dual-energy computed tomography (DECT) assessment of monosodium urate (MSU) and hydroxyapatite (HA) phantoms compared with a commercial 2-material decomposition (2MD) and dual-thresholding (DT) material decomposition methods. MATERIALS AND METHODS: Monosodium urate (0.0, 3.4, 13.3, 28.3, and 65.2 mg/dL tubes) and HA (100, 400, and 800 mg/cm 3 tubes) phantoms were DECT scanned individually and together in the presence of the foot and ankle of 15 subjects. The raw data were decomposed with 3MD-VMI, 2MD, and DT to produce MSU-only and HA-only images. Mean values of 10 × 10 × 10-voxel volumes of interest (244 μm 3 ) placed in each MSU and HA phantom well were obtained and compared with their known concentrations and across measurements with subjects' extremities to obtain accuracy and precision measures. A statistical difference was considered significant if P < 0.05. RESULTS: Compared with known phantom standards, 3MD-VMI was accurate for the detection of MSU concentrations as low as 3.4 mg/dL ( P = 0.75). In comparison, 2MD was limited to 13.3 mg/dL ( P = 0.06) and DT was unable to detect MSU concentrations below 65.2 mg/L ( P = 0.16). For the HA phantom, 3MD-VMI and 2MD were accurate for all concentrations including the lowest at 100 mg/cm 3 ( P = 0.63 and P = 0.55, respectively). Dual-thresholding was not useful for the decomposition of HA phantom. Precision was high for both 3MD-VMI and 2MD measurements for both MSU and HA phantoms. Qualitatively, 3MD-VMI MSU-only images demonstrated reduced beam-hardening artifact and voxel misclassification, compared with 2MD and DT. CONCLUSIONS: Three-material decomposition-VMI DECT is accurate for quantification of MSU and HA concentrations in phantoms and accurately detects a lower concentration of MSU than either 2MD or DT. For concentration measurements of both MSU and HA phantoms, 3MD-VMI and 2MD have high precision, but DT had limitations. Clinical implementation of 3MD-VMI DECT promises to improve the performance of this imaging modality for diagnosis and treatment monitoring of gout.
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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 | 0.001 | 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 | 0.001 |
| 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".