Assessing the impact of a magnetic field generator on fluoroscopic image quality
Bibliographic record
Abstract
Many spinal operations are performed using fluoroscopic guidance due to its excellent visualization of osseous structures and surgical instrumentation in real-time, however, its efficacy is conditional on accurate needle placement. Image-guided surgical navigation systems allow for intraoperative and continuous localization of surgical tools with respect to patient anatomy, leading to significantly improved needle placement accuracy. Magnetic navigation systems require a field generator (FG) whose placement must be near the patient and may partially obstruct the x-ray beam, causing image artifacts and degraded image quality. Northern Digital Inc. has developed a radiolucent FG (RLFG) prototype to reduce image artifacts, however, the X-ray photon scatter interactions from the RLFG may reduce image contrast, add noise and decrease spatial resolution. These scatter interactions can be assessed in terms of the scatter-to-primary ratio (SPR) and its effect on image quality can be described using the modulation transfer function (MTF) and the generalized detective quantum efficiency (DQE). SPR measurements of a 20 cm water phantom and surgical table were taken with and without the RLFG using a slanted-edge technique as described by Garland and Cunningham, as well as the SPR measurements of the isolated RLFG and isolated water phantom. MTF and generalized DQE measurements of the imaging system were taken with and without the RLFG using the commercially available DQEPro (DQE Instruments, Ontario, Canada ). SPR measurments demonstrated an 8% average increase when the RLFG was added underneath the surgical table, and the SPR of the water phantom was on average 5 times larger than the SPR of the RLFG. Therefore, the photon scatter interactions within the RLFG would likely cause minimal image quality deterioration, especially in comparison to a patient-representing water phantom. Introducing the RLFG in the imaging system demonstrates no practically significant difference in MTF, and a 9% average decrease in generalized DQE. The decreased DQE may be due in part to increased scatter on the exposure sensor relative to the image detector, and further experimentation is needed to validate this hypothesis. This work demonstrates the minimal effects on radiograph image quality with the introduction of a RLFG into a fluoroscopic imaging system, moving towards the seamless integration of magnetic tracking systems for fluoroscopy-guided interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".