Kernel-Based PET Image Reconstruction using Dynamic PET and MR Anatomical Information
Bibliographic record
Abstract
Positron emission tomography (PET) imaging is used to track biochemical processes in the human body.PET image quality is limited by noise and several methods have been implemented to improve the quality.Kernel-based image reconstruction is among the methods implemented to increase PET image quality and commonly uses a Gaussian kernel.Unfortunately, the Gaussian kernel tends to smooth details in the reconstructed image.To reduce noise without losing contrast details, a different kernel is needed.This work gives an overview of Gaussian kernel PET image reconstruction and focuses on finding substitutes for the Gaussian kernel that tackles its shortcomings.A wavelet kernel can be more efficient than the Gaussian kernel in reducing noise while keeping contrast details by better separating signal from noise and thus it does not over smooth peak values in the final reconstructed images.In this thesis, a wavelet kernel was first applied on prior information derived from dynamic PET series, and its usefulness has been evaluated using simulated brain data, physical phantom data and patient data.Reconstruction results are presented and discussed in detail comparing the wavelet kernel method with the Gaussian kernel method.In the next step, using magnetic resonance (MR) information as prior information for kernel-based PET image reconstruction, the wavelet method is improved and extended by proposing a multi-scale wavelet kernel.This method identifies the directionality in the MR image and includes that information for kernel construction as well.Methods developed in this thesis allow for higher SNR in the reconstructed PET image while preserving contrast.They also produce reconstructed PET images with higher visual quality compared to Gaussian kernel methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".