The Impact of Simulated Image Acquisition Time Reduction on Image Quality and Parameters of Myocardial Perfusion Imaging Using a Dedicated Cardiac Camera
Bibliographic record
Abstract
Heart disease is the second leading cause of death in Canada, highlighting the crucial role of early diagnosis in disease management. Myocardial perfusion imaging (MPI), is widely employed for this purpose, involves injecting a radiopharmaceutical into the body, imaging its distribution with a gamma camera, and revealing cardiac blood flow patterns. A significant challenge in MPI is the lengthy 8 to 10 minutes required for stress and rest imaging, potentially causing patient discomfort and compromising image quality due to movement. In a clinical study with 26 patients, post-processed data manipulation simulated a reduction in MPI imaging time. The findings indicated that MPI imaging time can be effectively shortened to 4 and 5 minutes for stress and rest imaging, respectively, maintaining clinical interpretation quality in our cohort. This promising outcome prompts further exploration of timely, comfortable MPI imaging feasibility for managing ischemic heart disease in a broader and diverse patient population.
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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 | 0.002 | 0.013 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".