Journal Article

FZJ-2021-05692

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology

Waldmann, M. (Corresponding author)RWTH* ; Grosch, A.FZJ* ; Witzler, C.FZJ* ; Lehner, M. ; Benda, O. ; Koch, W. ; Vogt, K. ; Kohn, C. ; Schröder, W.RWTH* ; Göbbert, J. H.FZJ* ; Lintermann, A.FZJ*

2022
Springer Heidelberg

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Please use a persistent id in citations: http://hdl.handle.net/2128/30603  doi:10.1007/s11517-021-02446-3

Abstract: Physics-based analyses have the potential to consolidate and substantiate medical diagnoses in rhinology. Such methods are frequently subject to intense investigations in research. However, they are not used in clinical applications, yet. One issue preventing their direct integration is that these methods are commonly developed as isolated solutions which do not consider the whole chain of data processing from initial medical to higher valued data. This manuscript presents a workflow that incorporates the whole data processing pipeline based on a environment. Therefore, medical image data are fully automatically pre-processed by machine learning algorithms. The resulting geometries employed for the simulations on high-performance computing systems reach an accuracy of up to 99.5% compared to manually segmented geometries. Additionally, the user is enabled to upload and visualize 4-phase rhinomanometry data. Subsequent analysis and visualization of the simulation outcome extend the results of standardized diagnostic methods by a physically sound interpretation. Along with a detailed presentation of the methodologies, the capabilities of the workflow are demonstrated by evaluating an exemplary medical case. The pipeline output is compared to 4-phase rhinomanometry data. The comparison underlines the functionality of the pipeline. However, it also illustrates the influence of mucosa swelling on the simulation.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
3. Analysis of Respiratory and Cerebrospinal Flows by a Coupled Lattice-Boltzmann Method and Machine Learning Approach (jhpc54_20190501) (jhpc54_20190501)
4. Rhinodiagnost (jhpc54_20180501) (jhpc54_20180501)

Appears in the scientific report 2022

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Database coverage:
; ; ; BIOSIS Previews ; Current Contents - Engineering, Computing and Technology ; Current Contents - Life Sciences ; Ebsco Academic Search ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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