Toward Fluoro-Free Interventions: Using Radial Intracardiac Ultrasound for Vascular Navigation
Bibliographic record
Abstract
Transcatheter cardiovascular interventions have the advantage of patient safety, reduced surgery time and minimal trauma to the patient's body. Transcathether interventions, which are performed percutaneously, are limited by the lack of direct line of sight with the procedural tools and the patient anatomy. Therefore, such interventional procedures rely heavily on image guidance for navigating toward and delivering therapy at the target site. Vascular navigation via the inferior vena cava, from the groin to the heart, is an imperative part of most transcatheter cardiovascular interventions including heart valve repair surgeries and ablation therapy. Traditionally, the inferior vena cava is navigated using fluoroscopic techniques such as venography and computed tomography venography. These X-ray-based techniques can have detrimental effects on the patient as well as the surgical team, causing increased radiation exposure, leading to risk of cancer, fetal defects and eye cataracts. The use of a heavy lead apron has also been reported to cause back pain and spine issues, thus leading to interventionalist's disc disease. We propose the use of a catheter-based ultrasound augmented with electromagnetic tracking technology to generate a vascular roadmap in real time and perform navigation without harmful radiation. In this pilot study, we used spatially tracked intracardiac echocardiography to reconstruct a vessel from a phantom in a 3-D virtual environment. We illustrate how the proposed ultrasound-based navigation will appear in a virtual environment, by navigating a tracked guidewire within the vessels in the phantom without any radiation-based imaging. The geometric accuracy is assessed using a computed tomography scan of the phantom, with a Dice coefficient of 0.79. The average distance between the surfaces of the two models comes out to be 1.7 ± 1.12 mm.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".