# Patient Specific Haemodynamic Modeling after Occlusion Treatment in Leg

## Summary (2 min read)

### 1. Introduction

- Revascularization is the conventional method of surgical treatment which increases lifetime and life quality for many patients.
- So far no method has been developed for presurgical assessment of revascularization impact to the postsurgical blood flow.
- It is based on personalized 1D core network reconstruction algorithm, presurgical ultrasound patient specific data fitting and the 1D blood flow simulations.
- The authors briefly introduce the mathematical model in section 2.1.

### 2.1.1. Core model

- As a core model for the closed blood circulation the authors used 1D network dynamical model [15–17] accounting for arterial and venous parts of the thigh sub-network.
- The model considers the flow of viscous incompressible fluid in the network of elastic tubes/vessels.
- The system of equations (2.4)-(2.5) is closed by finite difference approximation of compatibility conditions along outgoing characteristics.
- The order of the respective junction system can be reduced from 2M+1 to M equations and effectively solved by Newton method [15,17].
- Details on numerical implementation of this model are discussed in [14,15,17].

### 2.1.2. Boundary conditions

- Since the authors consider a local region of the thigh vasculature, they should address the boundary conditions at the vascular network inlet and outlet.
- Patient specific data for this node were unavailable for us and the authors set the inlet boundary conditions as u(t, 0)S(t, 0) = αQH (t) , (2.8) where QH (t) is heart ejection profile simulated by four-chamber dynamical heart model [15].
- Moreover, the outlet boundaries are placed downstream and blood flow may be changed significantly due to surgical intervention.
- The authors connect the averaged venous network of the same structure to the terminal outlet points of the considered arterial region.

### 2.2. Reconstruction of patient specific 1D core network

- 1D blood flow network simulations require 1D core network reconstruction.
- The overall algorithm of reconstruction is divided into the following steps: 1) 3D volume segmentation of vascular structure, 2) meshing and centerlines extraction, 3) centerlines merging, and 4) network reconstruction.
- These centerlines should be merged and segmented with junction points.
- The other centerlines are checked for the intersection with the root centerline and branching points are determined for every intersection.
- In this case only one node is added to the set of graph nodes.

### 2.3. Patient specific model fitting

- The authors apply the above approach to 1D core network reconstruction to predict changes of local haemodynamics in thigh vasculature due to the occlusion treatment in the femoral artery.
- Geometrical parameters of the network (vessels lengths and diameters) are defined by their 1D core network reconstruction algorithm and correspond to the patient specific morphology.
- The authors assess their acceptable ranges basing on general physiological and anatomical data [7,11–13], as well as patient parameters such as height, weight, medical history and the following assumptions: 1. Vessels 1, 3, 4, 5, 7 and 9 are the parts of the femoral artery and its descendant popliteal artery.
- These main leg arteries have relatively high stiffness ck and low resistance Rk since they carry large bulk of blood.
- The authors assume these parameters to remain the same after the occlusion treatment since according to numerical evidence the Reynolds number is not changed significantly in the most vessels.

### 3. Results

- Two series of numerical experiments have been performed.
- They simulate haemodynamics in large thigh arteries before and after femoral artery occlusion treatment.
- The authors note that even better matching can be achieved by further adjustment of the vessels parameters shifting them to non-physiological ranges.
- The authors compare the computed model results and measured values presented in the column postsurgical in Table 2.
- The largest error is observed in the distal part of the superficial femoral 95 “Ivanov˙mmnp2014˙6” —.

### 4. Discussion

- The proposed method of 1D core network reconstruction is competitive to the methods implemented in the well-known commercial software [20].
- Formally, boundary conditions at the junction nodes should be derived from limiting ratios in (2.1), (2.2) that reduce to the total pressure conservation in pseudo-steady approximation.
- This approach was discussed in more detail in [15].
- The authors have to fit them in physiological range to conform with Doppler ultrasound data.
- In this work the authors focused on the haemodynamic analysis of the atherosclerotic occlusion treatment in the femoral artery.

Did you find this useful? Give us your feedback

##### Citations

140 citations

### Cites background from "Patient Specific Haemodynamic Model..."

...The network can be generated on the basis of general anatomical data such as handbook of anatomical charts [21], anatomy 3D models [17] or patient-specific data [55]....

[...]

37 citations

23 citations

16 citations

13 citations

##### References

4,051 citations

1,174 citations

526 citations

### "Patient Specific Haemodynamic Model..." refers background in this paper

...For other works in this field we refer to [1, 5, 9, 10]....

[...]

242 citations

### "Patient Specific Haemodynamic Model..." refers methods in this paper

...Discussion The proposed method of 1D core network reconstruction is competitive to the methods implemented in the well-known commercial software [3]....

[...]

213 citations

### "Patient Specific Haemodynamic Model..." refers background in this paper

...For other works in this field we refer to [1, 5, 9, 10]....

[...]

##### Related Papers (5)

##### Frequently Asked Questions (2)

###### Q2. What are the future works mentioned in the paper "Patient specific haemodynamic modeling after occlusion treatment in leg" ?

The same method can be applied to the other parts of the vascular system and other angiosurgical procedures such as cava filter implantation and artificial embolisation of arterial-venous malformations that will be parts of their future work.