Reducing dynamic bladder artifact in pelvic bone SPECT: an assessment of lesion detectability using numerical and human observers
Bibliographic record
Abstract
In pelvic bone SPECT using Tc-99m labelled compounds, the accumulation of activity into the bladder during the data acquisition process often results in data inconsistencies which, when reconstructed with filtered backprojection, results in streak artifacts. If the uptake rate is sufficient, these streaks may be significant enough to impair lesion detection. We have investigated various reconstruction methods in an effort to reduce this artifact. Pelvic SPECT imaging was simulated using the Zubal voxelized phantom, with provisions for a changing activity distribution within the bladder. Reconstructions were performed using filtered backprojection, ordered subset-expectation maximization and dynamic expectation maximization. Each method was first optimized for postreconstruction smoothing parameters using a channelized, nonprewhitening (CNPW) numerical observer model. The numerical observer used is based on human observer LROC methodology whereby both a likely lesion location and a confidence rating is supplied by the observer for each image. Based on the results of the CNPW observer, a human LROC observer study was performed in order to assess the various reconstruction methods in terms of lesion detectability. Three human observers were used in this test. The results of this test indicate that filtered backprojection performs significantly worse than static OSEM iterative reconstruction with attenuation correction when assessed using the area under the LROC curve (A/sub LROC/=0.47 vs 0.71). Results comparing OSEM with dEM indicate that the dEM algorithm is able to further reduce streak artifacts compared to OSEM, but this improvement was not reflected in improved A/sub LROC/ scores. In fact, detectability actually decreased slightly when using dEM (A/sub LROC/=0.71 vs 0.66), although this reduction was not seen to be statistically significant. It is possible that the slightly reduced performance of the dEM algorithm may be due, in part, to not performing an optimization in the number of reconstruction iterations as was performed for the OSEM method.
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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 | 0.016 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".