312.6: Model for Virtual Vascular Reconstruction in Liver Transplantation Using Computational Fluid Dynamics
Bibliographic record
Abstract
Introduction: Computational anatomy is a modern research method that has become available using computer technology and computational fluid dynamics. It is promising to use it for perioperative planning and modeling, including organ transplantation. Aim of study is to prepare investigational model of blood flow in liver during liver transplantation. Matherials and methods: Contrast research was done among 53 men who were patients in surgery departments at Krasnoyarsk regional hospital #1, Russia (KRH). Estimations were made using 3D models of portal system (working stations GE Advantage Workstation, Siemens singo.via), based on multi-slices computed tomography of abdominal cavity with bolus contrasting with help of medication “Ultravist-370”. The volume of the used contrasting stuff made 100 ml, the speed of infiltration was 4 ml per second, and the average radial strain was 11,3 m3v. DICOM data segmentation was performed using Dragonfly software (Object Research Systems, Canada) at the Innovation Technology Management Resources of the Reaviz University. Using series with arterial and vein contrast, we performed the segmentation of contrasting vessels, and obtained three-dimensional data topology in.obj format. Then we processed the obtained models using scripts prepared for the pythonOCC framework. We built the central lines of the vessels and formed the branching tree. Methods of computational hemodynamics were implemented using the Visual-CFD application for OpenFOAM environment (ESI, France). Mesh example of portal vein shown in figure 1.Results: With the use of created three-dimensional computer model, the blood flow in the portal vein and liver arteries at various variants of its structure was simulated. It was obtained that, in the presence of the main type of structure with predominance of blood flow along the splenic vein, the blood flow turbulence and risk of thrombosis development are higher. At the same time, with virtual thrombosis of the portal vein trunk, the pressure gradient is 1.4 times higher than with the bulk type, which is more favorable for the proposed reconstruction. Thus, these data can be used for preoperative planning in surgical treatment of portal vein thrombosis in liver transplantation. Conclusion: The proposed model of blood flow is promising and allows you to create an interactive tool for calculating the hemodynamic situation in various reconstructions during liver transplantation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".