The Sensitivity of Exponential Data Consistency Condition-based Attenuation Map Alignment to Mismatched Attenuation Maps
Bibliographic record
Abstract
Attenuation correction of stand-alone SPECT systems requires registration of an externallyacquired attenuation map such as from a CT scan. Exponential data consistency conditions (DCCs) can be used to assess the alignment between a SPECT activity distribution and an external CTbased attenuation map. During cardiac SPECT imaging, some patient motion is unavoidable, such as that due to cardiac contraction. Such motion can lead to small differences between the measured attenuation map and the attenuation present during SPECT acquisition. These differences may cause inaccuracies in the conversion to exponential projections and thus result in errors in the alignment of the attenuation map. ECG-gating of the emission data reduces the motion present in each gated projection and if an ECG-gated attenuation map data was available, use of the matched gates would give an accurate alignment. However, more often, only a CT from a single point in the cardiac cycle is available. In this work, we investigated the magnitude of the error in registration introduced by inconsistencies in the attenuation map and the attenuation present in the SPECT projections. An NCAT simulated ECG-gated SPECT projection dataset was generated. A DCC-based alignment method was applied to attenuation maps corresponding to the different gates and the resulting alignments were compared. Average and maximum-attenuating maps were also considered. The alignment errors were up to 4.5 mm, with attenuation maps from the gates most distal in the cardiac cycle generating the largest errors. These errors are large compared to the expected image resolution of 7mm. We conclude that care needs to be taken to choose an appropriate attenuation map when using a DCC approach to align the map with ECGgated SPECT studies.
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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 | 0.010 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".