| Waldmann, Moritz; Rüttgers, Mario; Lintermann, Andreas; Schröder, Wolfgang Virtual Surgeries of Nasal Cavities Using a Coupled Lattice-Boltzmann–Level-Set Approach Artikel In: Journal of Engineering and Science in Medical Diagnostics and Therapy, Bd. 5, Ausg. 3, 2022, ISSN: 2572-7958. @article{Waldmann2022,
title = {Virtual Surgeries of Nasal Cavities Using a Coupled Lattice-Boltzmann–Level-Set Approach},
author = {Waldmann, Moritz and Rüttgers, Mario and Lintermann, Andreas and Schröder, Wolfgang},
url = {https://asmedigitalcollection.asme.org/medicaldiagnostics/article/doi/10.1115/1.4054042/1139371/Virtual-Surgeries-of-Nasal-Cavities-Using-a},
doi = {10.1115/1.4054042},
issn = {2572-7958},
year = {2022},
date = {2022-03-31},
urldate = {2022-03-31},
journal = {Journal of Engineering and Science in Medical Diagnostics and Therapy},
volume = {5},
issue = {3},
abstract = {Fluid mechanical properties of respiratory flow such as pressure loss, temperature distribution, or wall-shear stress characterize the physics of a nasal cavity. Simulations based on computational fluid dynamics (CFD) methods are able to deliver in-depth details on respiration. Integrating such tools into virtual surgery environments may support physicians in their decision-making process. In this study, a lattice-Boltzmann (LB) flow solver is coupled to a level-set (LS) method to modify the shape of a nasal cavity at simulation run time in a virtual surgery. The geometry of a presurgical nasal cavity obtained from computer tomography (CT) datasets is smoothly adapted toward a postsurgical geometry given by the surgeon using an interpolation approach based on a LS method. The influence of the modification on the respiratory flow is analyzed in silico. The methods are evaluated by simulating a virtual surgery of a stenotic pipe and juxtaposing the results to cases using static geometries and by comparing them to literature findings. The results for both the stenotic pipe and the nasal cavity are in perfect agreement with the expected outcomes. For the nasal cavity, a shape is found that reduces the nasal resistance by 25.3% for inspiration at a volumetric flow rate of V˙=250 ml/s. The heating capability is retained despite the geometry modification. The simulation results support the surgeon in evaluating a planned surgery and in finding an improved surgery for the patient.},
keywords = {CFD Applications, Geometry, Lattice-Boltzmann method, Medizin, nasal cavity, Pipes, Pressure, Respiratory Flow Computation, Strömungssimulation, surgical indication},
pubstate = {published},
tppubtype = {article}
}
Fluid mechanical properties of respiratory flow such as pressure loss, temperature distribution, or wall-shear stress characterize the physics of a nasal cavity. Simulations based on computational fluid dynamics (CFD) methods are able to deliver in-depth details on respiration. Integrating such tools into virtual surgery environments may support physicians in their decision-making process. In this study, a lattice-Boltzmann (LB) flow solver is coupled to a level-set (LS) method to modify the shape of a nasal cavity at simulation run time in a virtual surgery. The geometry of a presurgical nasal cavity obtained from computer tomography (CT) datasets is smoothly adapted toward a postsurgical geometry given by the surgeon using an interpolation approach based on a LS method. The influence of the modification on the respiratory flow is analyzed in silico. The methods are evaluated by simulating a virtual surgery of a stenotic pipe and juxtaposing the results to cases using static geometries and by comparing them to literature findings. The results for both the stenotic pipe and the nasal cavity are in perfect agreement with the expected outcomes. For the nasal cavity, a shape is found that reduces the nasal resistance by 25.3% for inspiration at a volumetric flow rate of V˙=250 ml/s. The heating capability is retained despite the geometry modification. The simulation results support the surgeon in evaluating a planned surgery and in finding an improved surgery for the patient. |
| Lehner, Matthias; Benda, Odo Machine Learning based Image Segmentation with Convolutional Neural Networks Sonstige EMBEC Abstract Book Contribution, 2020, ISBN: 978-961-243-411-3. @misc{Leh20,
title = {Machine Learning based Image Segmentation with Convolutional Neural Networks},
author = {Lehner, Matthias and Benda, Odo },
editor = {Tomaž Jarm, Samo Mahnič-Kalamiza, Aleksandra Cvetkoska, Damijan Miklavčič},
url = {https://www.embec2020.org/wp-content/uploads/2020/11/EMBEC2020_Book_of_Abstracts.pdf},
isbn = {978-961-243-411-3},
year = {2020},
date = {2020-11-30},
abstract = {In this work, results are presented for a CNN architecture which was developed specifically to classify different segments in CT images of the nasal cavity and paranasal sinuses automatically and accurately. This approach makes it possible to generate high quality segmentations of the frontal, maxillary, and sphenoid sinuses on both sides of the body, the oral and nasal cavities, bone, tissues, as well as the air outside the head surrounding the patient’s body in order to remove the latter conveniently.},
howpublished = {EMBEC Abstract Book Contribution},
keywords = {3D Model Generation, Convolutional Neural Networks, CT Images, nasal cavity},
pubstate = {published},
tppubtype = {misc}
}
In this work, results are presented for a CNN architecture which was developed specifically to classify different segments in CT images of the nasal cavity and paranasal sinuses automatically and accurately. This approach makes it possible to generate high quality segmentations of the frontal, maxillary, and sphenoid sinuses on both sides of the body, the oral and nasal cavities, bone, tissues, as well as the air outside the head surrounding the patient’s body in order to remove the latter conveniently. |
| Vogt, Klaus; Bachmann-Harildstad, Gregor; Lintermann, Andreas; Nechyporenko, Alina; Peters, Franz; Wernecke, Klaus-Dieter The new agreement of the international RIGA consensus conference on nasal airway function tests Artikel In: Rhinology, Bd. 56, 2018. @article{vogtriga18,
title = {The new agreement of the international RIGA consensus conference on nasal airway function tests},
author = {Klaus Vogt and Gregor Bachmann-Harildstad and Andreas Lintermann and Alina Nechyporenko and Franz Peters and Klaus-Dieter Wernecke
},
editor = {Rhinology International},
url = {http://rhinodiagnost.eu/wp-content/uploads/2018/01/Rhinology_manuscript_1777.pdf, The new agreement of the international RIGA consensus conference on nasal airway function tests},
doi = {https://doi.org/10.4193/Rhino17.084},
year = {2018},
date = {2018-01-23},
journal = {Rhinology},
volume = {56},
abstract = {The report reflects an agreement based on the consensus conference of the International Standardization Committee on the Objective Assessment of the Nasal Airway in Riga, 2nd Nov. 2016.
The aim of the conference was to address the existing nasal airway function tests and to take into account physical, mathematical and technical correctness as a base of international standardization as well as the requirements of the Council Directive 93/42/EEC of 14 June 1993 concerning medical devices.
Rhinomanometry, acoustic rhinometry, peak nasal inspiratory flow, Odiosoft-Rhino, optical rhinometry, 24-h measurements, computational fluid dynamics, nasometry and the mirrow test were evaluated for important diagnostic criteria, which are the precision of the equipment including calibration and the software applied; validity with sensitivity, specificity, positive and negative predictive values, reliability with intra-individual and inter-individual reproducibility and responsiveness in clinical studies.
For rhinomanometry, the logarithmic effective resistance was set as the parameter of high diagnostic relevance. In acoustic rhinometry, the area of interest for the minimal cross-sectional area will need further standardization. Peak nasal inspiratory flow is a reproducible and fast test, which showed a high range of mean values in different studies. The state of the art with computational fluid dynamics for the simulation of the airway still depends on high performance computing hardware and will, after standardization of the software and both the software and hardware for imaging protocols, certainly deliver a better understanding of the nasal airway flux.},
keywords = {diagnosis, nasal cavity, nasal mucosa, nasal septum, physiology},
pubstate = {published},
tppubtype = {article}
}
The report reflects an agreement based on the consensus conference of the International Standardization Committee on the Objective Assessment of the Nasal Airway in Riga, 2nd Nov. 2016.
The aim of the conference was to address the existing nasal airway function tests and to take into account physical, mathematical and technical correctness as a base of international standardization as well as the requirements of the Council Directive 93/42/EEC of 14 June 1993 concerning medical devices.
Rhinomanometry, acoustic rhinometry, peak nasal inspiratory flow, Odiosoft-Rhino, optical rhinometry, 24-h measurements, computational fluid dynamics, nasometry and the mirrow test were evaluated for important diagnostic criteria, which are the precision of the equipment including calibration and the software applied; validity with sensitivity, specificity, positive and negative predictive values, reliability with intra-individual and inter-individual reproducibility and responsiveness in clinical studies.
For rhinomanometry, the logarithmic effective resistance was set as the parameter of high diagnostic relevance. In acoustic rhinometry, the area of interest for the minimal cross-sectional area will need further standardization. Peak nasal inspiratory flow is a reproducible and fast test, which showed a high range of mean values in different studies. The state of the art with computational fluid dynamics for the simulation of the airway still depends on high performance computing hardware and will, after standardization of the software and both the software and hardware for imaging protocols, certainly deliver a better understanding of the nasal airway flux. |
