Patient‐specific estimation of spatially variant image noise for a pinhole cardiac<scp>SPECT</scp>camera
Bibliographic record
Abstract
Purpose New single photon emission computed tomography (SPECT) cameras using fixed pinhole collimation are increasingly popular. Pinhole collimators are known to have variable sensitivity with distance and angle from the pinhole aperture. It follows that pinholeSPECTsystems will also have spatially variant sensitivity and hence spatially variant image noise. The objective of this study was to develop and validate a rapid method for analytically estimating a map of the noise magnitude in a reconstructed image using data from a single clinical acquisition. Methods The projected voxel (PV) noise estimation method uses a modified forward projector with attenuation effects to estimate the number of photons detected from each voxel in the field‐of‐view. We approximate the noise for each voxel as the standard deviation of a Poisson distribution with a mean equal to the number of detected photons. An empirical formula is used to address scaling discrepancies caused by image reconstruction. Calibration coefficients are determined for thePVmethod by comparing it with noise measured from a nonparametrically bootstrapped set of images of a spherical uniformly filled Tc‐99m water phantom. Validation studies comparePVnoise estimates with bootstrapped measured noise for 31 patient images (5 min, 340 MBq,99mTc‐tetrofosmin rest study). Results Bland–Altman analysis showsR2correlations ≥70% between thePV‐estimated and ‐measured image noise. For the 31 patient cardiac images, thePVnoise estimate has an average bias of 0.1% compared to bootstrapped noise and have a coefficient of variation (CV) ≤ 17%. The bootstrap approach to noise measurement requires 5 h of computation for each image, whereas thePVnoise estimate requires only 64 s. In cardiac images, image noise due to attenuation and camera sensitivity varies on average from 4% at the apex to 9% in the basal posterior region of the heart. The standard deviation between 15 healthy patient study images (including physiological variability in the population) ranges from 6% to 16.5% over the length of the heart. Conclusions ThePVmethod provides a rapid estimate for spatially variant patient‐specific image noise magnitude in a pinhole‐collimated dedicated cardiacSPECTcamera with a bias of −0.3% and better than 83% precision.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".