Reducing bladder artifacts in clinical pelvic SPECT images.
Bibliographic record
Abstract
UNLABELLED: SPECT imaging of the pelvis is hampered by the presence of bladder artifacts, which render up to 20% of the images unreadable. The artifacts are caused by the high level of activity in the bladder and by the change in activity level as the bladder fills during data acquisition. The changing activity, together with the inhomogeneous attenuation of the pelvis, leads to inconsistencies in the projections and consequently artifacts when the data are reconstructed with filtered backprojection (FBP). dSPECT is an iterative algorithm that permits the reconstruction of dynamic SPECT images from a single, slow-rotation SPECT data acquisition. The reconstruction algorithm incorporates attenuation correction (AC) and changing tracer distributions and has been shown to reduce bladder artifacts in simulated data. In this study, we showed that dSPECT is effective at removing bladder artifacts from clinically acquired pelvic bone SPECT images. METHODS: Data from 20 patient volunteers were reconstructed using FBP, rescaled block-iterative reconstruction (RBI) without AC, RBI with AC, and dSPECT. AC was based on patient-specific attenuation maps acquired with a (153)Gd scanning line-source transmission system. For dSPECT, 16 time frames (4 projections/head/frame) were reconstructed and then summed to produce the final image. Artifact-to-bone contrast was compared, and image quality was subjectively assessed. RESULTS: Compared with FBP, RBI without AC significantly reduced (P = 0.008) the streak artifact. Both dSPECT and RBI with AC further significantly reduced (P < 0.001) the streak artifact and also improved the uniformity and symmetry of bone tracer-uptake. RBI with AC and dSPECT produced equivalent images if the change in bladder activity during acquisition was modest; however, with large changes in the activity (>100%), RBI with AC did not completely remove the artifact. In that situation, dSPECT produced additional reductions in streak-to-bone contrast. CONCLUSION: Of the methods considered, dSPECT is the most effective at removing bladder artifacts in clinical pelvic SPECT.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".