Registration of preoperative CT to intraoperative ultrasound via a statistical wrist model for scaphoid fracture fixation
Bibliographic record
Abstract
Scaphoid fracture is the most probable outcome of wrist injury and it often occurs due to sudden fall on an outstretched arm. To fix an acute non-displaced fracture, a volar percutaneous surgical procedure is highly recommended as it provides faster healing and better biomechanical outcome to the recovered wrist. Conventionally, this surgical procedure is performed under X-ray based fluoroscopic guidance, where surgeons need to mentally determine a trajectory of the drilling path based on a series of 2D projection images. In addition to challenges associated with mapping 2D information to a 3D space, the process involves exposure to ionizing radiation. Ultrasound (US) has been suggested as an alternate; US has many advantages including its non-ionizing nature and real-time 3D acquisition capability. US images are, however, difficult to interpret as they are often corrupted by significant amounts of noise or artifact, in addition, the appearance of the bone surfaces in an US image contains only a limited view of the true surfaces. In this thesis, I propose techniques to enable ultrasound guidance in scaphoid fracture fixation by augmenting intraoperative US images with preoperative computed tomography (CT) images via a statistical anatomical model of the wrist. One of the major contributions is the development of a multi-object statistical wrist shape+scale+pose model from a group of subjects at wide range of wrist positions. The developed model is then used to register with the preoperative CT to obtain the shapes and sizes of the wrist bones. The intraoperative procedure starts with a novel US bone enhancement technique that takes advantage of an adaptive wavelet filter bank to accurately highlight the bone responses in US. The improved bone enhancement in turn enables a registration of the statistical pose model to intraoperative US to estimate the optimal scaphoid screw axis for guiding the surgical procedure. In addition to this sequential registration technique, I propose a joint registration technique that allows a simultaneous fusion of the US and CT data for an improved registration output. We conduct a cadaver experiment to determine the accuracy of the registration process, and compare the results with the ground truth.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".