Dual Spillover Correction for SPECT Myocardial Blood Flow Measurement
Bibliographic record
Abstract
Myocardial blood flow (MBF) measurement is possible using stationary cardiac SPECT cameras. Limited spatial resolution and blood vessels in the myocardium produce mixing of the blood and myocardial time-activity curves used for kinetic analysis. Our purpose was to evaluate the effect of correction for both spillover into the myocardium from blood and into the blood from myocardium on the accuracy of SPECT MBF estimation. Images from 31 patients who had dynamic one-day rest-stress myocardial SPECT studies using99mTc-tetrofosmin were retrospectively processed both with attenuation correction (AC) and without (NC), and using a single spillover correction (SSO = blood to myocardium) and a dual spillover correction (DSO). Kl values from kinetic analysis with a 1-tissue compartment model were fit to a Renkin-Crone extraction fraction model (EF=1-exp(-α-β/MBF)) using 50 repeats of 2-fold cross validation to determine the method-specific K1-to-MBFconversion function. True MBF was measured using the clinical standard of PET imaging, with either82Rb or13N-ammonia, in the same patients. The model fit of SPECT K1 to PET MBF was not improved with dual spillover correction. With SSO, the Renkin-Crone model parameters were α=0.140, β =0.282 (AC, R2=0.71) and α = 0.187, β = 0.400 (NC, R2=0.75). With DSO, the parameters were α = 0.110, β = 0.265 (AC, R2=0.63) and α = 0.161, β = 0.352 (NC, R2=0.67). The limits of greement for the global relative MBF difference (SPECT - PET MBF/mean) were similar for DSO and SSO, both with AC ([-0.74 - 0.77] vs [-0.62- 0.64]) and without ([-0.67 - 0.71] vs [-.55 - 0.59]). Dual spillover correction alters the K1-to-MBF conversion function but does not improve the accuracy or precision of SPECT MBF measurements.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".