Visualization of hepatic arteries with 3D ultrasound during intra-arterial therapies
Bibliographic record
Abstract
Liver cancer represents the second most common cause of cancer-related mortality worldwide. The prognosis is poor with an overall mortality of 95%. Moreover, most hepatic tumors are unresectable due to their advanced stage at discovery or poor underlying liver function. Tumor embolization by intra-arterial approaches is the current standard of care for advanced cases of hepatocellular carcinoma. These therapies rely on the fact that the blood supply of primary hepatic tumors is predominantly arterial. Feedback on blood flow velocities in the hepatic arteries is crucial to ensure maximal treatment efficacy on the targeted masses. Based on these velocities, the intra-arterial injection rate is modulated for optimal infusion of the chemotherapeutic drugs into the tumorous tissue. While Doppler ultrasound is a well-documented technique for the assessment of blood flow, 3D visualization of vascular anatomy with ultrasound remains challenging. In this paper we present an image-guidance pipeline that enables the localization of the hepatic arterial branches within a 3D ultrasound image of the liver. A diagnostic Magnetic resonance angiography (MRA) is first processed to automatically segment the hepatic arteries. A non-rigid registration method is then applied on the portal phase of the MRA volume with a 3D ultrasound to enable the visualization of the 3D mesh of the hepatic arteries in the Doppler images. To evaluate the performance of the proposed workflow, we present initial results from porcine models and patient images.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".