Multimodality Imaging for Precise Localization of Craniofacial Osteomyelitis
Bibliographic record
Abstract
Functional imaging identifies areas of abnormal bone turnover, providing a useful adjunct in the treatment of osteomyelitis and bone tumors. The low resolution and lack of anatomical detail limit the application of bone scans in craniofacial surgery, however. Multimodality image registration addresses this problem by fusing functional images (single photon emission computed tomography [SPECT]) to high-resolution structural images (computed tomography [CT]) for precise anatomical delineation of bone activity. This article describes a technique for spatial registration of CT and SPECT images to provide precise anatomical delineation of abnormal bone turnover, thereby guiding the extent of resection in the management of craniofacial osteomyelitis. Standard CT and SPECT imaging protocols were used in imaging the skull from the vertex to the mentum. Image data were imported into Analyze (Biomedical Imaging Resource; Mayo Foundation, Rochester, MN) on a dedicated Windows NT (Microsoft Corporation, Redmond, WA) workstation. Using the CT data, the craniofacial skeleton, osteotomy segments, and bone grafts were interactively mapped out. Consecutive axial slices were then reconstructed to form a three-dimensional volume of interest. The CT-derived volume of interest was registered to the technetium Tc 99m-methylene diphosphonate SPECT scan using the Analyze program to provide a fused multimodality image. The imaging technique was used to localize osteomyelitis in a complex craniofacial reconstruction. The fused images guided the extent of resection during surgery, and postoperative microbiological and histological testing confirmed the diagnosis. Multimodality image registration provides a readily available method to relate facial skeletal anatomy and physiology. This technique is valuable in planning and monitoring therapeutic interventions in clinical conditions in which bone turnover is abnormal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".