Evidence map›Paper›PMID 34077817›Full record

ArticleComputers in biology and medicine2021

Fast virtual coiling algorithm for intracranial aneurysms using pre-shape path planning.

Palak Patel, Seyyed Mostafa Mousavi Janbeh Sarayi, Danyang Chen, Adam L Hammond, Robert J Damiano, Jason M Davies, Jinhui Xu, Hui Meng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Palak PatelCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Seyyed Mostafa Mousavi Janbeh SarayiCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Danyang ChenDepartment of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Adam L HammondDepartment of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Robert J DamianoCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Jason M DaviesCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA; Department of Neurosurgery, University at Buffalo, State University of New York, Buffalo, NY, USA; Department of Radiology, University at Buffalo, State University of New York, Buffalo, NY, USA.
Jinhui XuDepartment of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA.
Hui MengCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA; Department of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, USA. Electronic address: huimeng@buffalo.edu.

Funding

Virtual Intervention of Intracranial AneurysmsR01NS091075 · NINDS · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI MENG, HUI · 2015 to 2019
$1.7M
NINDS NIH HHS R01 NS091075
6 · The paper itself

Abstract

To aid in predicting and improving treatment outcome of endovascular coiling of intracranial aneurysms, simulation of patient-specific coil deployment should be both accurate and fast. We developed a fast virtual coiling algorithm called Pre-shape Path Planning (P3). It captures the mechanical propensity of a released coil to restore its pre-shape for bending energy minimization, producing coils without unrealistic kinks and bends. A coil is discretized into finite-length segments and extruded from the delivery catheter segment-by-segment following a generic coil pre-shape. With the release of each segment, coil-wall and coil-coil collisions are detected and resolved. Modeling of each case took seconds to minutes. To test the algorithm, we evaluated its output against the literature, experiments, and patient angiograms. The periphery-to-core ratio of coils deployed by P3 decreased with increasing coil packing density, consistent with observations in the literature. Coils deployed by P3 compared well with in vitro experiments, free from unphysical kinks and loops that arose from previous virtual coiling algorithms. Simulations of coiling in four patient-specific aneurysms agreed well with the patient angiograms. To test the influence of coil pre-shape on P3, we performed hemodynamic simulations in aneurysms with coils deployed by P3 using the generic pre-shape, P3 using a coil-specific pre-shape, and full finite-element-method simulation. We found that the generic pre-shape was sufficient to produce results comparable to virtual coiling by finite element modeling. Based on these findings, P3 can rapidly simulate coiling in patient-specific aneurysms with good accuracy and is thus a potential candidate for clinical treatment planning.

Indexed as

Embolization, TherapeuticIntracranial AneurysmAlgorithmsComputer SimulationHemodynamicsHumansTreatment OutcomeCerebral aneurysmCoil simulationEndovascular interventionExperimental validationHemodynamicsVirtual coiling

Identifiers

PMID34077817
PMCPMC9372906

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.