Collimator-detector response compensation in quantitative SPECT reconstruction
Bibliographic record
Abstract
The purpose of this study was to investigate the importance of collimator-detector response compensation in single photon emission computed tomography (SPECT). The compensation methods included in this study addressed three important degrading factors, which are attenuation, scatter and collimator-detector response (CDR). Geometric response (GR) and septal penetration (SP) are two most important components of collimator-detector response. The compensation work has been divided into two general categories: one is GR compensation based reconstruction (GR-RECON), which includes attenuation, scatter and geometric response compensations, and the other one is GR-SP compensation based reconstruction (GR-SP-RECON), which consists of attenuation, scatter geometric response and septal penetration compensations. Ordered-subset expectation maximization (OS-EM) method is applied as the reconstruction framework. SIMIND Monte Carlo (MC) code is incorporated in the forward projection. Multiple projection sampling convolution-based forced detection (MP-CFD) is used here to accelerate the MC code. The convolution kernels in CFD are generated by the ray-tracing (RT) method. In order to further increase the convergence speed, the models of attenuation and collimator-detector response are included in the backprojection step. In order to assess the quantitative accuracy of the collimator-detector response compensation, the reconstruction images of cylinder, four different size spheres in various medium, and NURBS-based cardiac-torso (NCAT) phantom are evaluated using 1-131 and high energy general resolution (HEGR) collimators. The reconstruction images including GR compensation appear very obvious collimator dependent Gibbs ringing artifact. The quantitative estimation of the spheres and NURBS-based cardiac-torso (NCAT) phantom has denoted the high accuracy of GR-SP-RECON, whereas, a lot of quantitative activity will be mistakenly estimated in the background using GR-RECON.
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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 | 0.000 |
| 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".