Registration of CT to 3D ultrasound using near-field fiducial localization: A feasibility study
Bibliographic record
Abstract
OBJECTIVE: Registration of ultrasound to computed tomography (CT) images is used in several image-guided procedures, including laparoscopic surgery and radiation therapy. Conventional approaches use an external tracker calibrated to the ultrasound transducer and CT system, but several calibration steps are required. Registration can also be performed by aligning image features between modalities, but differences in feature depiction make matching difficult and initial approximate alignment is often needed. Registration using fiducials is a simpler approach but is limited by the need to implant fiducials in the anatomical region of interest so they are visible to both ultrasound and CT. This paper investigates the feasibility of using fiducials near the skin surface, and whether such fiducials can be sufficiently localized in the very near field of a 3D ultrasound transducer without significantly degrading image quality. This approach can also be used as an initialization step for feature-based registration techniques. MATERIALS AND METHODS: A stand-off pad containing fiducials (n > 3) was constructed using polyvinyl chloride and steel ball fiducials that are visible in both 3D ultrasound and CT images. Experiments on phantoms were performed to assess image quality and registration errors. Controlled variables included pad thickness and ultrasound imaging parameters. Initial tests were also conducted of a potential application in partial nephrectomy surgery. RESULTS: Image quality was degraded by an average of 6-11-13% (elevational-axial-lateral) in resolution of point targets and 5% in lesion contrast. Average fiducial localization error was 1.34 mm (axial) to 2.38 mm (lateral and elevational); average fiducial registration error (FRE) was 0.46 mm (axial), 1.08 mm (lateral) and 0.90 mm (elevational); and average total registration error (TRE) was 1.84 mm (axial), 0.89 mm (lateral) and 3.31 mm (elevational). Clinical results showed a similar FRE to that in the phantom study, but with an average TRE of 14.04 mm (over three patients). Ultimate alignment of the organ boundaries was affected mainly by motion from respiration. CONCLUSIONS: The small loss of image quality from the fiducial stand-off pad and the minimal inconvenience of using the pad at the time of the CT scan may be a worthwhile trade-off for purposes of registration since the pad provides a registration accuracy of several millimeters while still allowing subsequent feature-based registration. Future research will focus on using the registration from the fiducial stand-off pad for deformable feature-based registration of 3D ultrasound to CT for tumor localization in renal surgery.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".