Toward truly combined PET/CT imaging using PET detectors and photon counting CT with iterative reconstruction implementing physical detector response
Bibliographic record
Abstract
PURPOSE: This paper intends to demonstrate the feasibility of truly combined PET/CT imaging and addresses some of the major challenges raised by this dual modality approach. A method is proposed to retrieve maximum accuracy out of limited resolution computed tomography (CT) scans acquired with positron emission tomography (PET) detectors. METHODS: A PET/CT simulator was built using the LabPET™ detectors and front-end electronics. Acquisitions of energy-binned data sets were made using this low spatial resolution CT system in photon counting mode. To overcome the limitations of the filtered back-projection technique, an iterative reconstruction library was developed and tested for the counting mode CT. Construction of the system matrix is based on a preregistered raster scan from which the experimental detector response is obtained. PET data were obtained sequentially with CT in a conventional manner. RESULTS: A meticulous description of the system geometry and misalignment corrections is imperative and was incorporated into the matrix definition to achieve good image quality. Using this method, no sinogram precorrection or interpolation is necessary and measured projections can be used as raw input data for the iterative reconstruction algorithm. Genuine dual modality PET/CT images of phantoms and animals were obtained for the first time using the same detection platform. CONCLUSIONS: CT and fused PET/CT images show that LabPET™ detectors can be successfully used as individual X-ray photon counting devices for low-dose CT imaging of the anatomy in a molecular PET imaging context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".