18F-FDG-PET and MRI in patients with malignancies of the liver and pancreas
Bibliographic record
Abstract
PURPOSE: To evaluate the accuracy of retrospective rigid image registration and fusion between F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI) of the upper abdomen. PATIENTS, MATERIAL, METHODS: Image fusion of PET and MRI was performed in 30 patients with suspected malignancy of the liver or pancreas. Using a commercially available image fusion tool capable of rigid manual point-based registration, PET-Images were retrospectively registered and fused by matching eight homologous points in the 3D spoiled gradient echo (GRE) MRI sequences acquired in portal venous phase and in the CT-component of PET/CT. Two separate observers (R1, R2) assessed accuracy of image registration by determining the distances in the x-, y- and z-axis as well as the absolute distance between anatomical landmarks which differed from the landmarks chosen for registration. Quality of fusion was graded using a three point grading scale (1 poorly fused; 2 satisfactory fused; 3 correctly fused) and compared to hybrid PET/CT fusion. RESULTS: Mean time of registration per patient was less than 2 minutes. Objective registration assessment showed errors between 2.4-6.3 mm in x-axis: mean 3.6 mm (R1); 4.6 mm (R2), 2.3-9.3 mm in y-axis (mean 5.1 mm; 5.5 mm) and 3.3-12.0 mm in z-axis (mean 5.9 mm; 5.9 mm.) The mean error in absolute distance between points was 6.0-16.8 mm (mean 9.9 mm; 10.6 mm). In visual assessment, most fusions were graded to be satisfactory or correctly fused: R1, R2: grade 3, 11/30 (36.7%), 22/30 (73.3%); grade 2, 13/30 (43.3%), 8/30 (26.7%); grade 1, 6/30 (20%), 0/30 (0%). Fusions were mostly comparable to hybrid PET/CT fusions. All of the fusions were defined as diagnostically relevant by both observers. CONCLUSION: Retrospective rigid image fusion of FDG-PET and MRI of the upper abdomen using the CT-component of PET/CT for registration is feasible without adaptation in image acquisition protocols and shows sub-centimeter registration errors in most cases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".