Comparing model based iterative reconstruction to hybrid based iterative reconstruction in stenosis detection during ECG-gated coronary CTA
Bibliographic record
Abstract
Abstract Purpose: To compare the quantitative and qualitative image quality of hybrid (HBIR) and model based (MBIR) iterative reconstruction during coronary Computed Tomography Angiography (CTA). Materials and Methods: Institutional review board approved this retrospective study. Patients (n=200) underwent a single coronary CTA with two iterative reconstruction techniques. Group A employed HBIR and group B employed MBIR. Quantitative and qualitative image quality was compared for each group. The mean attenuation values and signal-to-noise ratio (SNR) of each group were compared. Visual grading characteristics (VGC) and Cohen’s Kappa methodology were measured employing an image quality scoring system for coronary CTA. Receiver operating (JAFROC) and stenosis severity were compared with conventional coronary angiography. A p-value <0.05 was considered statistically significant. Results: Mean attenuation values (HU) in the HBIR group were significantly greater in the cusp (564.18±118.71) and left coronary (517.59±118.63) whilst in the MBIR group, the right coronary (531.67±138.93), left anterior descending (529.82±120.6) and left circumflex (538.32±132.94) arteries were significantly higher (p<0.001). The SNR was significantly greater in MBIR (5.32±1.1) compared to HBIR (3.64±0.8) (p<0.0001), with MBIR being superior to HBIR in the total and individual segments of the coronary arteries. VGC image quality assessment demonstrated that readers preferred HBIR over MBIR (p<0.001). Analysis of JAFROC data demonstrated a significant difference in detection of coronary stenosis in RCA (p<0.021), LCA (p<0.0001) and LD (p<0.0001) with HBIR showing overall smaller variability range compared to MBIR. Conclusion: When comparing quantitative and qualitative image quality, MBIR was superior in the former, whilst HBIR was superior in the later. Coronary artery stenosis assessment demonstrated less variability in diagnosis when using HBIR compared to MBIR. This highlights the need for careful attention when employing iterative reconstruction in order not to impact clinical outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".