Optimization of SPECT Measurement of Myocardial Blood Flow with Corrections for Attenuation, Motion, and Blood Binding Compared with PET
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 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 | 0.000 |
| 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 it