A comparison of MR-based attenuation correction in PET versus SPECT
Bibliographic record
Abstract
Attenuation correction (AC) is a critical step in the reconstruction of quantitatively accurate positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. Several groups have proposed magnetic resonance (MR)-based AC algorithms for application in hybrid PET/MR systems. However, none of these approaches have been tested on SPECT data. Since SPECT/MR systems are under active development, it is important to ascertain whether MR-based AC algorithms validated for PET can be applied to SPECT. To investigate this issue, two imaging experiments were performed: one with an anthropomorphic chest phantom and one with two groups of canines. Both groups of canines were imaged from neck to abdomen, one with PET/CT and MR (n = 4) and the other with SPECT/CT and MR (n = 4), while the phantom was imaged with all modalities. The quality of the nuclear medicine reconstructions using MR-based attenuation maps was compared between PET and SPECT on global and local scales. In addition, the sensitivity of these reconstructions to variations in the attenuation map was ascertained. On both scales, it was found that the SPECT reconstructions were of higher fidelity than the PET reconstructions. Further, they were less sensitive to changes to the MR-based attenuation map. Thus, MR-based AC algorithms that have been designed for PET/MR can be expected to demonstrate improved performance when used for SPECT/MR.
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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 | 0.000 | 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".