Simultaneous <sup>99m</sup>Tc/<sup>111</sup>In SPECT image reconstruction using convolution based forced detection
Bibliographic record
Abstract
Simultaneous multi-isotope SPECT imaging has a number of applications, for example, cardiac, brain and cancer imaging. The major concern in simultaneous multi-isotope is the significant crosstalk contamination between the different isotopes used. The current study focuses on a method of crosstalk compensation between two isotopes in simultaneous dual isotope SPECT acquisition applied to cancer imaging using99mTc/111In. Monte Carlo (MC), which is thought to offer the most realistic crosstalk and scatter compensation modeling, in typical implementations, has inherent long calculation times (often several hours or days) associated with it. This makes MC unsuitable for clinical applications. We have previously incorporated convolution based forced detection into SIMIND Monte Carlo program which have made MC feasible to use in clinical time frames. In order to evaluate the accuracy of our accelerated MC program a number of point source simulation results were compared to experimentally acquired data in terms of spatial resolution and detector sensitivity. We have developed an iterative MC-based image reconstruction technique that simulates the photon downscatter from one isotope into the acquisition window of a second isotope. The MC based estimation of scatter contamination contained in projection views is then used to compensate for the photon contamination during iterative reconstruction. We use a modified ordered subset-expectation maximization (OS-EM), named as simultaneous ordered subset-expectation maximization (Sim-OSEM), to perform this step. We have undertaken a number of simulation tests and phantom studies to verify this approach. The proposed reconstruction technique also evaluated by reconstruction of experimentally acquired projection phantom data. Reconstruction using Sim-OSEM showed very promising results in terms of crosstalk and scatter compensation and uniformity of background compared to analytical attenuation based reconstruction after triple energy window (TEW) based scatter correction of projection data. In our case images obtained using Sim-OSEM showed better scatter compensation and more uniform background when compared to the images reconstructed for separately acquired projection data using analytical attenuation based reconstruction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".