Technical Note: Comparison of megavoltage, dual‐energy, and single‐energy CT‐based μ‐maps for a four‐channel breast coil in PET/MRI
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe and evaluate methods for calculating a megavoltage computed tomography (MVCT)-derived MR hardware attenuation map (μ-map) and dual-energy CT (DECT) for 511 keV photons. METHODS: Phantom measurements were acquired on a whole-body hybrid PET/MRI system, using a four-channel receive-only MR radiofrequency (RF) breast coil. Two acquisitions were performed: with the phantoms positioned in the four-channel RF breast coil, and without the breast coil. PET attenuation from the breast coil was corrected using three different CT-derived hardware μ-maps: (a) Single-energy CT (SECT), (b) DECT, and (c) MVCT. Each attenuation-corrected (AC) PET volume was evaluated and compared with the acquisition performed without the breast coil. RESULTS: The breast coil attenuated PET photons by 10% overall. Threshold values were applied to the SECT μ-map to reduce the effects of metal artifacts, but overcorrection occurred in more highly attenuated regions. The DECT-derived virtual monochromatic image reduced beam-hardening artifacts, but other metal artifacts remained. Despite the remaining metal artifacts in the DECT image, it led to an improvement in the more attenuated regions. The MVCT images appear to be free from metal artifacts leading to an artifact-free μ-map and a further improvement AC-PET images. CONCLUSIONS: Our MVCT-based approach for creating μ-maps for MR RF coils greatly reduces artifacts produced by metal in a SECT approach. This eliminates the need for other artifact reduction methods, including the application of a threshold of narrow beam attenuation coefficients, or disassembling hardware to remove high-Z components before imaging with a kilovoltage source.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".