Evaluation of an MRI-Guided PET Image Reconstruction Approach With Adaptive Penalization Strength
Bibliographic record
Abstract
MRI-guided (MRIg) positron emission tomography (PET) reconstruction can potentially reduce noise and increase spatial resolution compared to standard clinical ordered-subsets expectation-maximization (OSEM) image quality. However, to adjust for the desired image quality, the balance between measured data and prior information usually requires manual tuning. This work presents an adaptive method to automatically control the influence of the magnetic resonance imaging (MRI) information on the PET emission data using maximum a posteriori (MAP) image reconstruction, robust against a wide range of counts. The method was evaluated on different static brain PET datasets using [18F]-FDG, [18F]-Florbetapir and [11C]-PiB, acquired in a simultaneous PET/MRI scanner and a PET/CT scanner, followed by an MRI scan. Noise in gray and white matter was measured for a wide range of statistics. Furthermore, noise and quantification accuracy were analyzed in different cortical and subcortical brain regions with different levels of tracer uptake, and at different levels of counts. Results demonstrated consistent improved image quality in terms of noise and spatial resolution with MRI-guided MAP PET (MRIg-MAP) reconstruction compared to OSEM. Additionally, it was shown that the number of collected counts could be reduced by ~1.6– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.3\times $ </tex-math></inline-formula> using MRIg-MAP reconstruction compared to OSEM, without increasing the noise significantly, either by reducing the scan time or injected activity. In conclusion, we presented a novel method to adaptively balance the influence of the anatomical information on the emission data for MRIg-MAP reconstruction, which showed image quality improvements compared to OSEM for different radiotracers, at different levels of counts, and applicable to simultaneous and sequential PET-MRI scans.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".