A Monte Carlo-based assessment of a SPECT/CT system with a single photon counting detector: a feasibility study
Bibliographic record
Abstract
Photon-counting computed tomography (CT) systems are recent advancements in imaging technology that enable the possibility of material-specific imaging. Since single-photon emission computed tomography (SPECT) traditionally utilizes photon-counting principles, performing the entire imaging procedure with a singular photon-counting detector (PCD) may be feasible. This has the potential to reduce system costs, eliminate co-registration issues, and decrease procedure times. Our study aims to determine the limits of visibility of a technetium-99m for SPECT/CT using a singular PCD system through a fixed focus collimator. Our methodology used the TOPAS and Geant4 simulation toolkit to model a CdTe photon-counting SPECT/CT system based on the performance of an XCounter Thor device with charge cloud width and charge collection efficiency taken into account. The SPECT source was a Tc-99m source with 25 MBq of activity. A 2D focused collimator composed of tungsten was designed for dual SPECT/CT imaging. The grid ratio varied from 20:1 to 60:1, and the optimal grid ratio was determined by the modulation transfer function (MTF) and the contrast-to-noise ratio (CNR). The optimal collimator ratio occurred at 50:1 or 40:1, according to the MTF and the CNR, respectively. There was a 1% difference in spatial resolution between a grid ratio of 50:1 and 60:1, suggesting that grids more aggressive than 50:1 are unnecessary for SPECT imaging. The CNR had a maximum value of 14.8+/-0.2 at a grid ratio of 40:1. These results were also qualitatively supported by the produced SPECT/CT images. In conclusion, our study confirms the feasibility of SPECT/CT with a singular photon-counting CT system. We found that a grid ratio of 40:1 maximized the CNR while maintaining a spatial resolution of 0.2 cycles/mm at an MTF value of 0.1. These findings provide a solid foundation for the future development and implementation of more efficient and cost-effective imaging systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".