Spatial and temporal registration of CT and SPECT images: development and validation of a technique for in vivo three-dimensional semiquantitative analysis of bone.
Bibliographic record
Abstract
UNLABELLED: The combined use of postoperative 3-dimensional CT and SPECT imaging provides a means of relating anatomy and physiology for the semiquantitative in vivo analysis of bone. This study focuses on the development and validation of a technique that accomplishes this through the registration of SPECT data to a 3-dimensional volume of interest (VOI) interactively defined on CT images. METHODS: Five human cadaver heads served as anthropomorphic models for all experiments. Four cranial defects were created in each specimen with inlay and onlay split-skull bone grafts reconstructed to skull and malar recipient sites. To acquire all images, each specimen was landmarked with 1.6-mm ball bearings and CT scanned. Bone surfaces were coated with 99mTc-doped paint. The locations of the ball bearings were marked with paint doped with 111In. Separate SPECT scans were acquired using the energy windows of 99mTc and 111In. RESULTS: Serial SPECT images aligned with an average root-mean-square (RMS) error of 3.8 mm (i.e., <1 pixel). CT-to-SPECT volume matching aligned with an RMS error of 7.8 mm. Total counts in CT-defined VOIs applied to SPECT data showed a strong linear correlation (r2 = 0.86) with true counts obtained from a dose calibrator. CONCLUSION: The capability of this multimodality registration technique to anatomically localize and quantify radiotracer uptake is sufficiently accurate to warrant further assessment in an in vivo trial.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".