Evidence map›Paper›PMID 32130645›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

Lattice-Boltzmann interactive blood flow simulation pipeline.

Sahar S Esfahani, Xiaojun Zhai, Minsi Chen, Abbes Amira, Faycal Bensaali, Julien AbiNahed, Sarada Dakua, Georges Younes, Abdulla Baobeid, Robin A Richardson and 1 more

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Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact, top 98% of its field
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

20 citing papers in PubMed, 37 citations in OpenAlex.

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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

11 authors at 6 institutions in 2 countries.

Sahar S EsfahaniCollege of Engineering, Qatar University, Doha, Qatar.
Xiaojun ZhaiSchool of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
Minsi ChenDepartment of Computer Science, University of Huddersfield, Huddersfield, UK.
Abbes AmiraFaculty of Computing, Engineering and Media, De Montfort University, Leicester, UK. abbes.amira@dmu.ac.uk.
Faycal BensaaliCollege of Engineering, Qatar University, Doha, Qatar.
Julien AbiNahedDepartment of Surgery, Hamad Medical Corporation, Doha, Qatar.
Sarada DakuaDepartment of Surgery, Hamad Medical Corporation, Doha, Qatar.
Georges YounesDepartment of Surgery, Hamad Medical Corporation, Doha, Qatar.
Abdulla BaobeidDepartment of Surgery, Hamad Medical Corporation, Doha, Qatar.
Robin A RichardsonCentre for Computational Science, University College London, London, UK.
Peter V CoveneyCentre for Computational Science, University College London, London, UK.
Hamad Medical Corporation · QAQatar University · QAUniversity College London · GBDe Montfort University · GBUniversity of Essex · GBUniversity of Huddersfield · GB

Funding

Qatar National Research Fund (QA) 5-792-2-328
6 · The paper itself

Abstract

purposeCerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering.

methodsWe have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores.

resultsThe proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second.

conclusionA reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information.

Indexed as

Cerebrovascular CirculationComputer SimulationHemodynamicsHumansImaging, Three-DimensionalIntracranial AneurysmModels, NeurologicalCerebral aneurysmGPULattice-BoltzmannPipelineVisualization

Identifiers

PMID32130645
OpenAlexW3005687511

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.