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.
The goal of this work was to integrate in situ possibilities into the general-purpose code-coupling library PDI [1]. This is done using the simulation code Alya as an example. Here, an open design is taken into account to later create possibilities to extend this to other simulation codes, that are using PDI.Here, an in transit solution was chosen to separate the simulation as much as possible from the analysis and visualization. To implement this, ADIOS2 is used for data transport. However, to prevent too strong a commitment to one tool, SENSEI is interposed between simulation and ADIOS2 as well as in the in-transit endpoint between ADIOS2 and the visualization software. This allows a user who wants a different solution to easily implement it. However, the visualization with ParaView Catalyst was chosen as default for the time being.
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