System matrix modelling of externally tracked motion
Bibliographic record
Abstract
BACKGROUND AND AIM: In high-resolution emission tomography imaging, even small patient movements can considerably degrade image quality. The aim of this work was to develop a general approach to motion-corrected reconstruction of motion-contaminated data in the case of rigid motion (particularly brain imaging) which would be applicable to any PET scanner in the field, without specialized data-acquisition requirements. METHODS: Assuming the ability to externally track subject motion during scanning (e.g., using the Polaris camera), we proposed to incorporate the measured rigid motion information into the system matrix of the expectation maximization reconstruction algorithm. Furthermore, we noted and developed a framework to incorporate the additional effect of motion on modifying the attenuation factors. A new mathematical brain phantom was developed and used along with elaborate combined Simset/GATE simulations to compare the proposed framework with the cases of no motion correction. RESULTS AND CONCLUSION: Clear qualitative and quantitative improvements were observed when incorporating the proposed framework. The method is very practical to implement for any scanner in the field, not requiring any hardware modifications or access to the list-mode acquisition capability.
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 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 | 0.000 | 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".