Image reconstruction in cardiac interventions using a small flat-panel detector
Bibliographic record
Abstract
C-arm CT is used in neurovascular interventions where a large flat panel detector is used to acquire cone-beam projection data. In this case, data truncation problems due to the limited detector size are mild. When the cone beam CT method is applied to cardiac interventions severe data truncation artifacts reduce the clinical utility of the reconstructions. However, accurate reconstruction is still possible given a priori knowledge of the reconstruction values within a small region inside the FOV. Several groups have studied the case of the interior problem where data is truncated from all views. In this paper, we applied these new mathematical discoveries to C-arm cardiac cone-beam CT to demonstrate that accurate image reconstruction may be achieved for cardiac interventions. The method is applied to iteratively reconstruct the image volume such that it satisfies several physical conditions. In this work, the algorithm is applied to data from in-vivo cardiac canine studies collected using a clinical C-arm system. It is demonstrated that the algorithm converges well to the reconstruction values of non-truncated data reconstructed using the FDK algorithm. Furthermore, proper convergence is achieved by using only an estimate of the average value within a subregion as a priori information (i.e. the exact value at each pixel in the a priori region need not be known). Two methods for obtaining a priori information are compared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".