Interior reconstruction using local inverse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Truncated projections can arise from a detector with a limited field of view (LFOV). Truncation artifacts can be reduced through extrapolation methods; however the reconstructed images with extrapolation are often over-corrected or under-corrected. Recently, an iterative reconstruction-reprojection algorithm was developed, which incorporated extrapolation with iterative algorithm. It gave the possibility to better reduce truncation artifacts compared to using the extrapolation method alone. This article builds a theoretic foundation for the above iterative reconstruction-reprojection algorithm. The theoretic foundation is suitable to parallel-beam, fan-beam and cone-beam computed tomography(CT). Two assumptions are summarized from the CT system. Then, a truncation-artifact-free solution for the problem of LFOV is derived from these assumptions. The solution contains a "local inverse" of matrix. The local inverse of a matrix is defined using the sub-matrix and its general inverse. The solution can be approximately implemented as the iterative reconstruction-reprojection algorithm which is just the algorithm mentioned above.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it