Fast PET image reconstruction based on SVD decomposition of the system matrix
Bibliographic record
Abstract
Data filtering based on matrix pseudo-inverse is a well known but not yet appreciated means of tomographic image reconstruction. Matrix pseudo-inversion step is very demanding in terms of numerical precision and necessary computing power. Ill conditioning of the system matrix in positron emission tomography (PET) results in solutions highly sensitive to noise in the experimental data. In the present work, the feasibility of image reconstruction based on singular value decomposition (SVD) of the system matrix for animal 2-D PET is demonstrated. Analytic detector response accounting for the non-invariant spatial system response is explicitly included into the system matrix. Regularization of the SVD-based solution with the singular spectrum truncation (TSVD solution) derived from spatial resolution analysis is proposed. TSVD reconstruction is fast except for the matrix decomposition step, which is performed once for a given scanner geometry. Reconstructed image quality and quantitation are compared to those obtained with filtered backprojection (FBP) and iterative maximum likelihood technique. TSVD image reconstruction may be a viable alternative to FBP for routine clinical applications.
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.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".