Data-Driven Patient Motion Compensation in Cardiac Positron Emission Tomography
Bibliographic record
Abstract
Positron emission tomography (PET) is a molecular imaging modality that has been demonstrated to be a powerful, non-invasive, tool for the assessment and diagnosis of cardiac pathologies like coronary artery disease.The accuracy of these clinical examinations for detecting and prognosticating disease can be marred in cases where patient motion is severe.Clinical use of motion tracking/compensation tools, however, is relatively uncommon partly due to the increases in complexity and time of patient setup prior to imaging.The purpose of the work described here was to develop and evaluate new methods of patient motion detection and compensation in the context of cardiac PET imaging studies that are less complex than standard commercial options in the hope of reducing barriers to clinical adoption.The proposed methods are based on measuring and tracking the motion of a low-activity radioactive marker placed on patients using the positron emission tracking (PeTrack) algorithm.Motion information was employed to compensate and/or correct for either respiratory or whole-body patient motion.The performance of PeTrack for respiratory tracking and motion compensation was evaluated in a clinical population in comparison with a commonly used commercial optical tracking device.Within a practical comparison framework PeTrack was shown to perform comparably to the commercial system.From this comparison shortcomings of both PeTrack and the commercial system were identified; knowledge of the former can inform future development and improvement.A method for whole-body patient motion correction (WBMC) in static cardiac perfusion studies using PeTrack was developed.Motion corrected images demonstrated significantly While less directly involved in my work, the people with whom I spent most of my office/study time with also are deserving of my thanks.My peers, my colleagues, my friends, provided useful suggestions, necessary distractions, and companionship beyond the boundaries of study.
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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".