Abstract
Accurately computing the flow in the nasal cavity with computational fluid dynamics (CFD) simulations requires highly resolved computational meshes based on anatomically realistic geometries. Such geometries can only be obtained from computer tomography (CT) data with high spatial resolution, i.e., featuring a ≤1mm slice thickness. In practice, CT images are, however, recorded at a lower resolution to not expose patients to high radiation and to reduce the overall costs. To overcome this problem and to provide patients with a detailed physics-based diagnosis, e.g., for surgery planning, the potential of super-resolution networks (SRNs) to increase the CT resolution is analyzed. Therefore, an SRN is developed and trained on CT data. Its predictive performance is improved by an automated hyperparameter optimization technique. The training time is further reduced without predictive accuracy degradation by oversampling images with challenging regions. The performance of the SRN is assessed by an analysis of the reconstructed 3D surfaces of the human upper airway and by comparing results of CFD simulations. That is, surfaces and simulation results based on SRN-generated CT data at 1mm resolution are compared to those obtained from unmodified CT data-sets at low (3mm) and high (1mm) resolution, as well as from CT data interpolated to a 1mm resolution from coarse data. The findings reveal the SRN-based approach to have the lowest deviations in the surfaces and CFD results when compared to those based on the original high-resolution data. The pressure loss between the inflow (nostrils) and outflow (pharynx) regions averaged for three test patients differs by only 1.3%, compared to 8.7% and 8.8% in the coarse and interpolated cases. It is concluded that the SRN-based method is a promising tool to enhance underresolved CT data to yield reliable numerical results of respiratory flows.
| Original language | English |
|---|---|
| Pages (from-to) | 474-488 |
| Number of pages | 15 |
| Journal | Future Generation Computer Systems |
| Volume | 159 |
| DOIs | |
| State | Published - Oct 2024 |
Funding
The research leading to these results has been conducted in the Joint Laboratory for Extreme Scale Computing (JLESC) project: Architecture and Hyperparameter Search for Super-Resolution Networks Operating on Medical Images. Furthermore, the research has been performed in the CoE RAISE project, which receives funding from the European Union’s Horizon 2020 – Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733 . The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on the JARA Partition part of the supercomputer JURECA [56] at Forschungszentrum Jülich. This work was performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE). The research leading to these results has been conducted in the Joint Laboratory for Extreme Scale Computing (JLESC) project: Architecture and Hyperparameter Search for Super-Resolution Networks Operating on Medical Images. Furthermore, the research has been performed in the CoE RAISE project, which receives funding from the European Union's Horizon 2020 – Research and Innovation Framework Programme H2020-INFRAEDI-2019-1 under grant agreement no. 951733. The authors gratefully acknowledge the computing time granted by the JARA Vergabegremium and provided on the JARA Partition part of the supercomputer JURECA [56] at Forschungszentrum Jülich. This work was performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE).
Keywords
- Computational fluid dynamics
- Data efficient training
- Hyperparameter optimization
- Machine learning
- Respiratory flows
- Super-resolution