Attenuation correction for PET/MRI to measure tracer activity surrounding total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: Positron emission tomography (PET) in combination with magnetic resonance imaging (MRI) could allow inflammatory complications near total knee arthroplasty (TKA) to be studied early in their development. However, attenuation of the PET signal by the metal TKA implants imparts substantial error into measurements of tracer activity, and conventional MR-based attenuation correction (AC) methods have large signal voids in the vicinity of metal implants. PURPOSE: To evaluate a segmentation-based AC approach to measure tracer uptake from PET/MRI scans near TKA implants. METHODS: F]fluorodeoxyglucose with known activity concentration (4.68 MBq total, 0.76 MBq/mL) and inserted into the knee. Images of the knee were acquired using a 3T PET/MRI system (Biograph mMR, Siemens Healthcare, Erlangen, Germany). Models of the implant components were registered to the MR data using rigid-body transformations and the other tissue classes were manually segmented. These segments were used to create the segmentation-based map and complete the AC. Percentage error of the resulting measured activities was calculated by comparing the measured and known amounts of activity in each vial. RESULTS: The original AC resulted in a percentage error of 64.1% from the known total activity. Errors in the individual vial activities ranged from 40.2 to 82.7%. Using the new segmentation-based AC, the percentage error of the total activity decreased to 3.55%. Errors in the individual vials were less than 15%. CONCLUSIONS: The segmentation-based AC technique dramatically reduced the error in activity measurements that result from PET signal attenuation by the metal TKA implant. This approach may be useful to enhance the reliability of PET/MRI measurements for numerous applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".