Real-time endovascular guidewire position simulation using shortest path algorithms

Schäfer, Sebastian; Singh, Vikas; Noël, Peter B.; Walczak, Alan M.; Xu, Jinhui; Hoffmann, Kenneth R.

doi:10.1007/s11548-009-0385-z

Cited by 16 publications

(34 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each point i in G r , the closest point j in G s is determined as (4). when the corresponding points between G s and G r are established, we use the RMS distance to measure the difference between them. [14], we achieved 87 and 80 % improvements for the first and third phantoms under the same conditions, respectively. Furthermore, we obtained 91 % improvements for the second phantom under the condition with reduced graph construction complexity (i.e., fewer meshes).…”

Section: Resultsmentioning

confidence: 62%

“…It generalizes quite natural results in 3D and provides high spatial resolution within a short time frame. Compared with previous studies [13,14,17], our graph construction requires markedly less time. Compared to [14], we achieved 87 and 80 % improvements for the first and third phantoms under the same conditions, respectively.…”

Section: Introductionmentioning

confidence: 91%

“…Compared with previous studies [13,14,17], our graph construction requires markedly less time. Compared to [14], we achieved 87 and 80 % improvements for the first and third phantoms under the same conditions, respectively. Furthermore, 91 % improvements were obtained for the second phantom under the condition with reduced graph construction complexity.…”

Section: Introductionmentioning

confidence: 91%

“…Given a smooth parameterized planar curve s, the bending energy is defined as E b = s K 2 ds , where K is the smooth curve's curvature [4] at point s, i.e., the inverse of its minimum curvature radius. In this situation, we adopt the approximate definition of the bending energy given in [14]. For any three consecutive sam- …”

Section: Energy Functionmentioning

confidence: 99%

“…However, this analytical model based on graph theory could be derived only by iteratively minimizing the energy function. Schafer et al [13,14,17] made use of the analytical modeling ideas proposed in [9] and beam-like of FEM approaches [6,11,15] and reformulated the problem into a single-source shortest path problem on Graph. However, the proposed algorithms cannot avoid the high computational cost of FEM, as previously mentioned.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An improved real-time endovascular guidewire position simulation using shortest path algorithm

Qiu

Qiu³

et al. 2015

Med Biol Eng Comput

View full text Add to dashboard Cite

In this study, we propose a new graph-theoretical method to simulate guidewire paths inside the carotid artery. The minimum energy guidewire path can be obtained by applying the shortest path algorithm, such as Dijkstra's algorithm for graphs, based on the principle of the minimal total energy. Compared to previous results, experiments of three phantoms were validated, revealing that the first and second phantoms overlap completely between simulated and real guidewires. In addition, 95 % of the third phantom overlaps completely, and the remaining 5 % closely coincides. The results demonstrate that our method achieves 87 and 80 % improvements for the first and third phantoms under the same conditions, respectively. Furthermore, 91 % improvements were obtained for the second phantom under the condition with reduced graph construction complexity.

show abstract

Section: Resultsmentioning

confidence: 62%

Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 91%

Section: Energy Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An improved real-time endovascular guidewire position simulation using shortest path algorithm

Qiu

Qiu³

et al. 2015

Med Biol Eng Comput

View full text Add to dashboard Cite

show abstract

Patient‐specific simulation of guidewire deformation during transcatheter aortic valve implantation

Auffret

Castro

et al. 2018

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Transcatheter aortic valve implantation is a recent mini-invasive procedure to implant an aortic valve prosthesis. Prosthesis positioning in transcatheter aortic valve implantation appears as an important aspect for the success of the intervention. Accordingly, we developed a patient-specific finite element framework to predict the insertion of the stiff guidewire, used to position the aortic valve. We simulated the guidewire insertion for 2 patients based on their pre-operative CT scans. The model was designed to primarily predict the position and the angle of the guidewires in the aortic valve, and the results were successfully compared with intraoperative images. The present paper describes extensively the numerical model, which was solved by using the ANSYS software with an implicit resolution scheme, as well as the stabilization techniques which were used to overcome numerical instabilities. We performed sensitivity analysis on the properties of the guidewire (curvature angle, curvature radius, and stiffness) and the conditions of insertion (insertion force and orientation). We also explored the influence of the model parameters. The accuracy of the model was quantitatively evaluated as the distance and the angle difference between the simulated guidewires and the intraoperative ones. A good agreement was obtained between the model predictions and intraoperative views available for 2 patient cases. In conclusion, we showed that the shape of the guidewire in the aortic valve was mainly determined by the geometry of the patient's aorta and by the conditions of insertion (insertion force and orientation).

show abstract

Patient‐specific computational modeling of endovascular aneurysm repair: State of the art and future directions

Avril

Gee

Hemmler

et al. 2021

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Endovascular aortic repair (EVAR) has become the preferred intervention option for aortic aneurysms and dissections. This is because EVAR is much less invasive than the alternative open surgery repair. While in‐hospital mortality rates are smaller for EVAR than open repair (1%–2% vs. 3%–5%), the early benefits of EVAR are lost after 3 years due to larger rates of complications in the EVAR group. Clinicians follow instructions for use (IFU) when possible, but are left with personal experience on how to best proceed and what choices to make with respect to stent‐graft (SG) model choice, sizing, procedural options, and their implications on long‐term outcomes. Computational modeling of SG deployment in EVAR and tissue remodeling after intervention offers an alternative way of testing SG designs in silico, in a personalized way before intervention, to ultimately select the strategies leading to better outcomes. Further, computational modeling can be used in the optimal design of SGs in cases of complex geometries. In this review, we address some of the difficulties and successes associated with computational modeling of EVAR procedures. There is still work to be done in all areas of EVAR in silico modeling, including model validation, before models can be applied in the clinic, but much progress has already been made. Critical to clinical implementation are current efforts focusing on developing fast algorithms that can achieve (near) real‐time solutions, as well as ways of dealing with inherent uncertainties related to patient aortic wall degradation on an individualized basis. We are optimistic that EVAR modeling in the clinic will soon become a reality to help clinicians optimize EVAR interventions and ultimately reduce EVAR‐associated complications.

show abstract

Real-time endovascular guidewire position simulation using shortest path algorithms

Abstract: The results indicate that the proposed technique yields reproducible and accurate guidewire positions within a short, clinically relevant time frame. These calculated positions may be useful in facilitating neurovascular interventions.

Cited by 16 publications

References 24 publications

An improved real-time endovascular guidewire position simulation using shortest path algorithm

An improved real-time endovascular guidewire position simulation using shortest path algorithm

Patient‐specific simulation of guidewire deformation during transcatheter aortic valve implantation

Patient‐specific computational modeling of endovascular aneurysm repair: State of the art and future directions

Contact Info

Product

Resources

About