Direct 3D PET image reconstruction into MR image space
Bibliographic record
Abstract
A method which includes both the motion correction and image registration transformation parameters from PET image space to MR image space within the system matrix of the MLEM algorithm is presented. This approach can be of particular significance in the fields of neuroscience and psychiatry, whereby PET is used to investigate differences in activation patterns between groups of participants (such as healthy controls and patients). This requires all images to be registered in a common spatial atlas. Currently, image registration is performed post-reconstruction. This introduces interpolation effects in the final image and causes image resolution degradation. Furthermore, motion correction introduces a further level of interpolation and possible resolution degradation. To include the transformation parameters (both for motion correction and registration) within the iterative PET reconstruction framework (through iterative use of actual software packages routinely applied after reconstruction) should reduce these interpolation effects and thus improve image resolution. Furthermore, it opens the possibility of direct reconstruction of the PET data into standardized stereotaxic atlases, e.g. ICBM152. To validate the proposed method, this work investigates registration, using 2D and 3D simulations based on the HRRT scanner geometry, between different image spaces using rigid body transformation parameters calculated using the mutual information similarity criterion. The quality of reconstruction was assessed using bias-variance and mean absolute error analyses to quantify differences with current post-reconstruction registration methods. We demonstrate a reduction in bias and in mean absolute error in reconstructed mean ROI activity when using the proposed method.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".