Optimization of SPECT Measurement of Myocardial Blood Flow with Corrections for Attenuation, Motion, and Blood Binding Compared with PET
Bibliographic record
Abstract
Myocardial blood flow (MBF) and myocardial flow reserve (MFR) measured with PET have clinical value. SPECT cameras with solid-state detectors can obtain dynamic images for measurement of MBF and MFR. In this study, SPECT measurements of MBF made using <sup>99m</sup>Tc-tetrofosmin were compared with PET in the same patients. <b>Methods:</b> Thirty-one patients underwent PET MBF rest–stress studies performed with <sup>82</sup>Rb or <sup>13</sup>N-ammonia within 1 mo of their SPECT study. Dynamic rest–stress measurements were made using a SPECT camera. Kinetic parameters were calculated using a 1-tissue-compartment model and converted to MBF and MFR. Processing with and without corrections for attenuation (+AC and −AC), patient body motion (+MC and −MC), and binding of the tracer to red blood cells (+BB and −BB) was evaluated. <b>Results:</b> Both +BB and +MC improved the accuracy and precision of global SPECT MBF compared with PET MBF, resulting in an average difference of 0.06 ± 0.37 mL/min/g. Global MBF and detection of abnormal MFR were not significantly improved with +AC. Global SPECT MFR with +MC and +BB had an area under the receiver-operating curve of 0.90 (+AC) to 0.95 (−AC) for detecting abnormal PET MFR less than 2.0. Regional analysis produced similar results with an area under the receiver-operating curve of 0.84 (+AC) to 0.87 (−AC). <b>Conclusion:</b> Solid-state SPECT provides global MBF and MFR measurements that differ from PET by 2% ± 32% (MBF) and 2% ± 28% (MFR).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".