Gated cardiac SPECT/CT: Slow CT or fast CT?
Bibliographic record
Abstract
257 Objectives: Manufacturers have taken two approaches to SPECT/CT. The “slow rotation” CT design has the CT complete a full rotation in 15 seconds, resulting in an attenuation map with breathing artifacts due to sinogram inconsistencies. The “fast rotation” design has the CT complete a rotation in 0.6 seconds to provide a breath-hold attenuation map. Methods: A digital phantom with physiologic motion (4DNCAT) and a Monte Carlo simulator (SimSET) were used to create ten respiratory-gated SPECT data sets, and slow- and fast-CT acquisitions, approximating two popular clinical products. (Cardiac motion not modeled.) SPECT data was reconstructed using slow- and fast-CT derived attenuation maps (fast-CT at end tidal volume), and compared with SPECT data reconstructed using a gated-CT derived attenuation map (gold-standard). Polar plots were calculated for each reconstruction, and root mean squared (RMS) error was calculated relative to the gold-standard on a pixel-by-pixel basis for each respiratory phase. Results: RMS errors were dependent upon the phase of the respiratory cycle for SPECT reconstruction using either slow- or fast-CT attenuation maps. Compared with Slow CT, the Fast CT approach quantitatively improved SPECT image contrast and RMS error averaged over all respiratory phases from 0.043 to 0.033. Conclusions: Since each SPECT projection is acquired over all phases of the respiratory cycle, whereas CT is not, both slow and fast CT approaches result in SPECT artifacts. We found that SPECT scans corrected for attenuation using fast-CT at end tidal volume had better quantitative accuracy than attenuation correction based on slow-CT.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".