Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations Article Swipe
Xin Liu
,
Mario Rüttgers
,
Alessio Quercia
,
Romain Égelé
,
Elisabeth Pfaehler
,
Rushikesh Shende
,
Marcel Aach
,
Wolfgang Schröder
,
Prasanna Balaprakash
,
Andreas Lintermann
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.future.2024.05.020
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.future.2024.05.020
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, CTs 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 physics-based 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.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.future.2024.05.020
- OA Status
- hybrid
- Cited By
- 7
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4397001435
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4397001435Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.future.2024.05.020Digital Object Identifier
- Title
-
Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-17Full publication date if available
- Authors
-
Xin Liu, Mario Rüttgers, Alessio Quercia, Romain Égelé, Elisabeth Pfaehler, Rushikesh Shende, Marcel Aach, Wolfgang Schröder, Prasanna Balaprakash, Andreas LintermannList of authors in order
- Landing page
-
https://doi.org/10.1016/j.future.2024.05.020Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.future.2024.05.020Direct OA link when available
- Concepts
-
Computer science, Computational fluid dynamics, Image resolution, Tomography, Polygon mesh, Temporal resolution, Resolution (logic), Algorithm, Artificial intelligence, Optics, Computer graphics (images), Physics, MechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7Per-year citation counts (last 5 years)
- References (count)
-
68Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4397001435 |
|---|---|
| doi | https://doi.org/10.1016/j.future.2024.05.020 |
| ids.doi | https://doi.org/10.1016/j.future.2024.05.020 |
| ids.openalex | https://openalex.org/W4397001435 |
| fwci | 3.71110292 |
| type | article |
| title | Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations |
| biblio.issue | |
| biblio.volume | 159 |
| biblio.last_page | 488 |
| biblio.first_page | 474 |
| topics[0].id | https://openalex.org/T11105 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9891999959945679 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Advanced Image Processing Techniques |
| topics[1].id | https://openalex.org/T11008 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9843999743461609 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2202 |
| topics[1].subfield.display_name | Aerospace Engineering |
| topics[1].display_name | Aerodynamics and Acoustics in Jet Flows |
| topics[2].id | https://openalex.org/T10271 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.9728000164031982 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1908 |
| topics[2].subfield.display_name | Geophysics |
| topics[2].display_name | Seismic Imaging and Inversion Techniques |
| is_xpac | False |
| apc_list.value | 3340 |
| apc_list.currency | USD |
| apc_list.value_usd | 3340 |
| apc_paid.value | 3340 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 3340 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7136690616607666 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C1633027 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6607421040534973 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q815820 |
| concepts[1].display_name | Computational fluid dynamics |
| concepts[2].id | https://openalex.org/C205372480 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5682672262191772 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q210521 |
| concepts[2].display_name | Image resolution |
| concepts[3].id | https://openalex.org/C163716698 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5582453608512878 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q841267 |
| concepts[3].display_name | Tomography |
| concepts[4].id | https://openalex.org/C31487907 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5508978366851807 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1154597 |
| concepts[4].display_name | Polygon mesh |
| concepts[5].id | https://openalex.org/C119666444 |
| concepts[5].level | 2 |
| concepts[5].score | 0.463248610496521 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5977280 |
| concepts[5].display_name | Temporal resolution |
| concepts[6].id | https://openalex.org/C138268822 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4261389970779419 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1051925 |
| concepts[6].display_name | Resolution (logic) |
| concepts[7].id | https://openalex.org/C11413529 |
| concepts[7].level | 1 |
| concepts[7].score | 0.39613935351371765 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[7].display_name | Algorithm |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.31550610065460205 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C120665830 |
| concepts[9].level | 1 |
| concepts[9].score | 0.17633408308029175 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[9].display_name | Optics |
| concepts[10].id | https://openalex.org/C121684516 |
| concepts[10].level | 1 |
| concepts[10].score | 0.11812636256217957 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7600677 |
| concepts[10].display_name | Computer graphics (images) |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.11619582772254944 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C57879066 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q41217 |
| concepts[12].display_name | Mechanics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7136690616607666 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/computational-fluid-dynamics |
| keywords[1].score | 0.6607421040534973 |
| keywords[1].display_name | Computational fluid dynamics |
| keywords[2].id | https://openalex.org/keywords/image-resolution |
| keywords[2].score | 0.5682672262191772 |
| keywords[2].display_name | Image resolution |
| keywords[3].id | https://openalex.org/keywords/tomography |
| keywords[3].score | 0.5582453608512878 |
| keywords[3].display_name | Tomography |
| keywords[4].id | https://openalex.org/keywords/polygon-mesh |
| keywords[4].score | 0.5508978366851807 |
| keywords[4].display_name | Polygon mesh |
| keywords[5].id | https://openalex.org/keywords/temporal-resolution |
| keywords[5].score | 0.463248610496521 |
| keywords[5].display_name | Temporal resolution |
| keywords[6].id | https://openalex.org/keywords/resolution |
| keywords[6].score | 0.4261389970779419 |
| keywords[6].display_name | Resolution (logic) |
| keywords[7].id | https://openalex.org/keywords/algorithm |
| keywords[7].score | 0.39613935351371765 |
| keywords[7].display_name | Algorithm |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.31550610065460205 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/optics |
| keywords[9].score | 0.17633408308029175 |
| keywords[9].display_name | Optics |
| keywords[10].id | https://openalex.org/keywords/computer-graphics |
| keywords[10].score | 0.11812636256217957 |
| keywords[10].display_name | Computer graphics (images) |
| keywords[11].id | https://openalex.org/keywords/physics |
| keywords[11].score | 0.11619582772254944 |
| keywords[11].display_name | Physics |
| language | en |
| locations[0].id | doi:10.1016/j.future.2024.05.020 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S186357190 |
| locations[0].source.issn | 0167-739X, 1872-7115 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0167-739X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Future Generation Computer Systems |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by-nc |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Future Generation Computer Systems |
| locations[0].landing_page_url | https://doi.org/10.1016/j.future.2024.05.020 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5079944768 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6912-3047 |
| authorships[0].author.display_name | Xin Liu |
| authorships[0].countries | DE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I171892758 |
| authorships[0].affiliations[0].raw_affiliation_string | Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Germany |
| authorships[0].institutions[0].id | https://openalex.org/I171892758 |
| authorships[0].institutions[0].ror | https://ror.org/02nv7yv05 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758 |
| authorships[0].institutions[0].country_code | DE |
| authorships[0].institutions[0].display_name | Forschungszentrum Jülich |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xin Liu |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Germany |
| authorships[1].author.id | https://openalex.org/A5017441603 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3917-8407 |
| authorships[1].author.display_name | Mario Rüttgers |
| authorships[1].countries | DE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210133912 |
| authorships[1].affiliations[0].raw_affiliation_string | Jülich Aachen Research Alliance - Center for Simulation and Data Science (JARA-CSD), Germany |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I887968799 |
| authorships[1].affiliations[1].raw_affiliation_string | Institute of Aerodynamics and Chair of Fluid Mechanics (AIA), RWTH Aachen University, Germany |
| authorships[1].affiliations[2].institution_ids | https://openalex.org/I171892758 |
| authorships[1].affiliations[2].raw_affiliation_string | Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Germany |
| authorships[1].institutions[0].id | https://openalex.org/I171892758 |
| authorships[1].institutions[0].ror | https://ror.org/02nv7yv05 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758 |
| authorships[1].institutions[0].country_code | DE |
| authorships[1].institutions[0].display_name | Forschungszentrum Jülich |
| authorships[1].institutions[1].id | https://openalex.org/I4210133912 |
| authorships[1].institutions[1].ror | https://ror.org/02r0e4r58 |
| authorships[1].institutions[1].type | facility |
| authorships[1].institutions[1].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758, https://openalex.org/I4210133912 |
| authorships[1].institutions[1].country_code | DE |
| authorships[1].institutions[1].display_name | Jülich Aachen Research Alliance |
| authorships[1].institutions[2].id | https://openalex.org/I887968799 |
| authorships[1].institutions[2].ror | https://ror.org/04xfq0f34 |
| authorships[1].institutions[2].type | education |
| authorships[1].institutions[2].lineage | https://openalex.org/I887968799 |
| authorships[1].institutions[2].country_code | DE |
| authorships[1].institutions[2].display_name | RWTH Aachen University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Mario Rüttgers |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Institute of Aerodynamics and Chair of Fluid Mechanics (AIA), RWTH Aachen University, Germany, Jülich Aachen Research Alliance - Center for Simulation and Data Science (JARA-CSD), Germany, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Germany |
| authorships[2].author.id | https://openalex.org/A5012035871 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Alessio Quercia |
| authorships[2].countries | DE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I171892758 |
| authorships[2].affiliations[0].raw_affiliation_string | Institute of Advanced Simulations 8 (IAS-8), Forschungszentrum Jülich GmbH, Germany |
| authorships[2].institutions[0].id | https://openalex.org/I171892758 |
| authorships[2].institutions[0].ror | https://ror.org/02nv7yv05 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758 |
| authorships[2].institutions[0].country_code | DE |
| authorships[2].institutions[0].display_name | Forschungszentrum Jülich |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Alessio Quercia |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Institute of Advanced Simulations 8 (IAS-8), Forschungszentrum Jülich GmbH, Germany |
| authorships[3].author.id | https://openalex.org/A5006450724 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8992-8192 |
| authorships[3].author.display_name | Romain Égelé |
| authorships[3].countries | FR, US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I277688954 |
| authorships[3].affiliations[0].raw_affiliation_string | Université Paris-Saclay, France |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I1282105669 |
| authorships[3].affiliations[1].raw_affiliation_string | Argonne National Laboratory, United States of America |
| authorships[3].institutions[0].id | https://openalex.org/I277688954 |
| authorships[3].institutions[0].ror | https://ror.org/03xjwb503 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I277688954 |
| authorships[3].institutions[0].country_code | FR |
| authorships[3].institutions[0].display_name | Université Paris-Saclay |
| authorships[3].institutions[1].id | https://openalex.org/I1282105669 |
| authorships[3].institutions[1].ror | https://ror.org/05gvnxz63 |
| authorships[3].institutions[1].type | facility |
| authorships[3].institutions[1].lineage | https://openalex.org/I1282105669, https://openalex.org/I1330989302, https://openalex.org/I39565521, https://openalex.org/I40347166 |
| authorships[3].institutions[1].country_code | US |
| authorships[3].institutions[1].display_name | Argonne National Laboratory |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Romain Egele |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Argonne National Laboratory, United States of America, Université Paris-Saclay, France |
| authorships[4].author.id | https://openalex.org/A5026685751 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6160-3011 |
| authorships[4].author.display_name | Elisabeth Pfaehler |
| authorships[4].countries | DE |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I171892758 |
| authorships[4].affiliations[0].raw_affiliation_string | Institute of Advanced Simulations 8 (IAS-8), Forschungszentrum Jülich GmbH, Germany |
| authorships[4].institutions[0].id | https://openalex.org/I171892758 |
| authorships[4].institutions[0].ror | https://ror.org/02nv7yv05 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758 |
| authorships[4].institutions[0].country_code | DE |
| authorships[4].institutions[0].display_name | Forschungszentrum Jülich |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Elisabeth Pfaehler |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Institute of Advanced Simulations 8 (IAS-8), Forschungszentrum Jülich GmbH, Germany |
| authorships[5].author.id | https://openalex.org/A5113210978 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Rushikesh Shende |
| authorships[5].countries | DE |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I887968799 |
| authorships[5].affiliations[0].raw_affiliation_string | Institute of Aerodynamics and Chair of Fluid Mechanics (AIA), RWTH Aachen University, Germany |
| authorships[5].institutions[0].id | https://openalex.org/I887968799 |
| authorships[5].institutions[0].ror | https://ror.org/04xfq0f34 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I887968799 |
| authorships[5].institutions[0].country_code | DE |
| authorships[5].institutions[0].display_name | RWTH Aachen University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Rushikesh Shende |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Institute of Aerodynamics and Chair of Fluid Mechanics (AIA), RWTH Aachen University, Germany |
| authorships[6].author.id | https://openalex.org/A5070119351 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-7861-0672 |
| authorships[6].author.display_name | Marcel Aach |
| authorships[6].countries | DE, IS |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I171892758 |
| authorships[6].affiliations[0].raw_affiliation_string | Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Germany |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I165368041 |
| authorships[6].affiliations[1].raw_affiliation_string | School of Engineering and Natural Sciences, University of Iceland, Iceland |
| authorships[6].institutions[0].id | https://openalex.org/I171892758 |
| authorships[6].institutions[0].ror | https://ror.org/02nv7yv05 |
| authorships[6].institutions[0].type | facility |
| authorships[6].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758 |
| authorships[6].institutions[0].country_code | DE |
| authorships[6].institutions[0].display_name | Forschungszentrum Jülich |
| authorships[6].institutions[1].id | https://openalex.org/I165368041 |
| authorships[6].institutions[1].ror | https://ror.org/01db6h964 |
| authorships[6].institutions[1].type | education |
| authorships[6].institutions[1].lineage | https://openalex.org/I165368041 |
| authorships[6].institutions[1].country_code | IS |
| authorships[6].institutions[1].display_name | University of Iceland |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Marcel Aach |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Germany, School of Engineering and Natural Sciences, University of Iceland, Iceland |
| authorships[7].author.id | https://openalex.org/A5043578209 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-3472-1813 |
| authorships[7].author.display_name | Wolfgang Schröder |
| authorships[7].countries | DE |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210133912 |
| authorships[7].affiliations[0].raw_affiliation_string | Jülich Aachen Research Alliance - Center for Simulation and Data Science (JARA-CSD), Germany |
| authorships[7].affiliations[1].institution_ids | https://openalex.org/I887968799 |
| authorships[7].affiliations[1].raw_affiliation_string | Institute of Aerodynamics and Chair of Fluid Mechanics (AIA), RWTH Aachen University, Germany |
| authorships[7].institutions[0].id | https://openalex.org/I4210133912 |
| authorships[7].institutions[0].ror | https://ror.org/02r0e4r58 |
| authorships[7].institutions[0].type | facility |
| authorships[7].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758, https://openalex.org/I4210133912 |
| authorships[7].institutions[0].country_code | DE |
| authorships[7].institutions[0].display_name | Jülich Aachen Research Alliance |
| authorships[7].institutions[1].id | https://openalex.org/I887968799 |
| authorships[7].institutions[1].ror | https://ror.org/04xfq0f34 |
| authorships[7].institutions[1].type | education |
| authorships[7].institutions[1].lineage | https://openalex.org/I887968799 |
| authorships[7].institutions[1].country_code | DE |
| authorships[7].institutions[1].display_name | RWTH Aachen University |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Wolfgang Schröder |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Institute of Aerodynamics and Chair of Fluid Mechanics (AIA), RWTH Aachen University, Germany, Jülich Aachen Research Alliance - Center for Simulation and Data Science (JARA-CSD), Germany |
| authorships[8].author.id | https://openalex.org/A5068721920 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-0292-5715 |
| authorships[8].author.display_name | Prasanna Balaprakash |
| authorships[8].countries | US |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I1282105669 |
| authorships[8].affiliations[0].raw_affiliation_string | Argonne National Laboratory, United States of America |
| authorships[8].affiliations[1].institution_ids | https://openalex.org/I1289243028 |
| authorships[8].affiliations[1].raw_affiliation_string | Oak Ridge National Laboratory, United States of America |
| authorships[8].institutions[0].id | https://openalex.org/I1282105669 |
| authorships[8].institutions[0].ror | https://ror.org/05gvnxz63 |
| authorships[8].institutions[0].type | facility |
| authorships[8].institutions[0].lineage | https://openalex.org/I1282105669, https://openalex.org/I1330989302, https://openalex.org/I39565521, https://openalex.org/I40347166 |
| authorships[8].institutions[0].country_code | US |
| authorships[8].institutions[0].display_name | Argonne National Laboratory |
| authorships[8].institutions[1].id | https://openalex.org/I1289243028 |
| authorships[8].institutions[1].ror | https://ror.org/01qz5mb56 |
| authorships[8].institutions[1].type | facility |
| authorships[8].institutions[1].lineage | https://openalex.org/I1289243028, https://openalex.org/I1330989302, https://openalex.org/I39565521, https://openalex.org/I4210159294 |
| authorships[8].institutions[1].country_code | US |
| authorships[8].institutions[1].display_name | Oak Ridge National Laboratory |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Prasanna Balaprakash |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Argonne National Laboratory, United States of America, Oak Ridge National Laboratory, United States of America |
| authorships[9].author.id | https://openalex.org/A5012635400 |
| authorships[9].author.orcid | https://orcid.org/0000-0003-3321-6599 |
| authorships[9].author.display_name | Andreas Lintermann |
| authorships[9].countries | DE |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I4210133912 |
| authorships[9].affiliations[0].raw_affiliation_string | Jülich Aachen Research Alliance - Center for Simulation and Data Science (JARA-CSD), Germany |
| authorships[9].affiliations[1].institution_ids | https://openalex.org/I171892758 |
| authorships[9].affiliations[1].raw_affiliation_string | Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Germany |
| authorships[9].institutions[0].id | https://openalex.org/I171892758 |
| authorships[9].institutions[0].ror | https://ror.org/02nv7yv05 |
| authorships[9].institutions[0].type | facility |
| authorships[9].institutions[0].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758 |
| authorships[9].institutions[0].country_code | DE |
| authorships[9].institutions[0].display_name | Forschungszentrum Jülich |
| authorships[9].institutions[1].id | https://openalex.org/I4210133912 |
| authorships[9].institutions[1].ror | https://ror.org/02r0e4r58 |
| authorships[9].institutions[1].type | facility |
| authorships[9].institutions[1].lineage | https://openalex.org/I1305996414, https://openalex.org/I171892758, https://openalex.org/I4210133912 |
| authorships[9].institutions[1].country_code | DE |
| authorships[9].institutions[1].display_name | Jülich Aachen Research Alliance |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | Andreas Lintermann |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Jülich Aachen Research Alliance - Center for Simulation and Data Science (JARA-CSD), Germany, Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Germany |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.future.2024.05.020 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11105 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9891999959945679 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Image Processing Techniques |
| related_works | https://openalex.org/W1966472837, https://openalex.org/W2071207587, https://openalex.org/W4320807967, https://openalex.org/W2039246943, https://openalex.org/W2128760873, https://openalex.org/W4249245269, https://openalex.org/W2162127469, https://openalex.org/W2047961797, https://openalex.org/W1976426447, https://openalex.org/W2733512255 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 7 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1016/j.future.2024.05.020 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S186357190 |
| best_oa_location.source.issn | 0167-739X, 1872-7115 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0167-739X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Future Generation Computer Systems |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by-nc |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Future Generation Computer Systems |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.future.2024.05.020 |
| primary_location.id | doi:10.1016/j.future.2024.05.020 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S186357190 |
| primary_location.source.issn | 0167-739X, 1872-7115 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0167-739X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Future Generation Computer Systems |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by-nc |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Future Generation Computer Systems |
| primary_location.landing_page_url | https://doi.org/10.1016/j.future.2024.05.020 |
| publication_date | 2024-05-17 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2153859429, https://openalex.org/W4205992428, https://openalex.org/W2099305408, https://openalex.org/W2562439620, https://openalex.org/W6653926859, https://openalex.org/W6676233579, https://openalex.org/W2067042811, https://openalex.org/W6602211262, https://openalex.org/W1885185971, https://openalex.org/W2741137940, https://openalex.org/W6754405603, https://openalex.org/W6840201148, https://openalex.org/W6639824700, https://openalex.org/W2725258829, https://openalex.org/W6773965314, https://openalex.org/W2809226111, https://openalex.org/W6758601462, https://openalex.org/W6768844273, https://openalex.org/W3096947210, https://openalex.org/W3006169930, https://openalex.org/W2774320778, https://openalex.org/W6847307170, https://openalex.org/W6674385629, https://openalex.org/W6808183544, https://openalex.org/W3011458629, https://openalex.org/W2202174789, https://openalex.org/W6638667902, https://openalex.org/W6631943919, https://openalex.org/W6766978945, https://openalex.org/W6602526659, https://openalex.org/W6678664249, https://openalex.org/W6634542218, https://openalex.org/W6774926797, https://openalex.org/W6758225634, https://openalex.org/W1994616650, https://openalex.org/W6861231585, https://openalex.org/W2069811085, https://openalex.org/W2015350864, https://openalex.org/W6639087915, https://openalex.org/W2156077349, https://openalex.org/W2062900651, https://openalex.org/W1992773065, https://openalex.org/W2161560170, https://openalex.org/W2095470085, https://openalex.org/W6603979581, https://openalex.org/W2883183514, https://openalex.org/W3069245681, https://openalex.org/W1967282533, https://openalex.org/W3112557529, https://openalex.org/W2620296437, https://openalex.org/W6798400614, https://openalex.org/W2966284335, https://openalex.org/W4286977192, https://openalex.org/W4247026124, https://openalex.org/W2097998348, https://openalex.org/W4387805957, https://openalex.org/W2963470893, https://openalex.org/W3179950556, https://openalex.org/W4211116959, https://openalex.org/W3002636976, https://openalex.org/W2247875277, https://openalex.org/W2798122215, https://openalex.org/W3037833767, https://openalex.org/W2913954081, https://openalex.org/W1522301498, https://openalex.org/W4255344359, https://openalex.org/W2772124661, https://openalex.org/W4249811194 |
| referenced_works_count | 68 |
| abstract_inverted_index.a | 41, 52, 77, 200, 271 |
| abstract_inverted_index.3D | 148 |
| abstract_inverted_index.CT | 94, 106, 171, 183, 196, 277 |
| abstract_inverted_index.In | 45 |
| abstract_inverted_index.It | 263 |
| abstract_inverted_index.To | 68 |
| abstract_inverted_index.an | 99, 114, 143 |
| abstract_inverted_index.as | 192, 194 |
| abstract_inverted_index.at | 51, 173, 185 |
| abstract_inverted_index.be | 28 |
| abstract_inverted_index.by | 113, 129, 142, 156, 249 |
| abstract_inverted_index.in | 4, 217, 257 |
| abstract_inverted_index.is | 96, 101, 111, 122, 140, 264, 270 |
| abstract_inverted_index.of | 87, 137, 145, 150, 159, 284 |
| abstract_inverted_index.on | 20, 105, 169, 227 |
| abstract_inverted_index.to | 55, 59, 63, 73, 91, 178, 199, 212, 224, 253, 274, 279 |
| abstract_inverted_index.1mm | 174, 201 |
| abstract_inverted_index.CFD | 160, 220 |
| abstract_inverted_index.CTs | 47 |
| abstract_inverted_index.Its | 108 |
| abstract_inverted_index.SRN | 100, 139 |
| abstract_inverted_index.The | 119, 135, 206, 232 |
| abstract_inverted_index.and | 62, 72, 103, 155, 165, 188, 239, 255, 260 |
| abstract_inverted_index.are | 176 |
| abstract_inverted_index.can | 26 |
| abstract_inverted_index.for | 82, 244 |
| abstract_inverted_index.is, | 163 |
| abstract_inverted_index.low | 186 |
| abstract_inverted_index.not | 56 |
| abstract_inverted_index.the | 2, 5, 65, 85, 93, 138, 146, 151, 209, 214, 218, 228, 236, 258, 267 |
| abstract_inverted_index.(CT) | 33 |
| abstract_inverted_index.8.7% | 254 |
| abstract_inverted_index.8.8% | 256 |
| abstract_inverted_index.Such | 24 |
| abstract_inverted_index.That | 162 |
| abstract_inverted_index.are, | 48 |
| abstract_inverted_index.data | 34, 172, 197, 278 |
| abstract_inverted_index.flow | 3 |
| abstract_inverted_index.from | 30, 181, 195, 203 |
| abstract_inverted_index.have | 213 |
| abstract_inverted_index.high | 36, 60, 189 |
| abstract_inverted_index.loss | 234 |
| abstract_inverted_index.only | 27, 250 |
| abstract_inverted_index.test | 246 |
| abstract_inverted_index.that | 266 |
| abstract_inverted_index.this | 70 |
| abstract_inverted_index.time | 121 |
| abstract_inverted_index.tool | 273 |
| abstract_inverted_index.well | 193 |
| abstract_inverted_index.when | 222 |
| abstract_inverted_index.with | 8, 35, 76, 132 |
| abstract_inverted_index.(1mm) | 190 |
| abstract_inverted_index.(3mm) | 187 |
| abstract_inverted_index.(CFD) | 12 |
| abstract_inverted_index.1.3%, | 251 |
| abstract_inverted_index.based | 19, 168, 226 |
| abstract_inverted_index.data. | 107, 205, 231 |
| abstract_inverted_index.e.g., | 81 |
| abstract_inverted_index.fluid | 10 |
| abstract_inverted_index.human | 152 |
| abstract_inverted_index.i.e., | 39 |
| abstract_inverted_index.lower | 53 |
| abstract_inverted_index.nasal | 6 |
| abstract_inverted_index.slice | 43 |
| abstract_inverted_index.those | 179, 225 |
| abstract_inverted_index.three | 245 |
| abstract_inverted_index.upper | 153 |
| abstract_inverted_index.yield | 280 |
| abstract_inverted_index.(SRNs) | 90 |
| abstract_inverted_index.airway | 154 |
| abstract_inverted_index.cases. | 262 |
| abstract_inverted_index.cavity | 7 |
| abstract_inverted_index.coarse | 204, 259 |
| abstract_inverted_index.costs. | 67 |
| abstract_inverted_index.expose | 57 |
| abstract_inverted_index.flows. | 286 |
| abstract_inverted_index.highly | 15 |
| abstract_inverted_index.images | 131 |
| abstract_inverted_index.inflow | 237 |
| abstract_inverted_index.lowest | 215 |
| abstract_inverted_index.meshes | 18 |
| abstract_inverted_index.method | 269 |
| abstract_inverted_index.reduce | 64 |
| abstract_inverted_index.reveal | 208 |
| abstract_inverted_index.≤1mm | 42 |
| abstract_inverted_index.between | 235 |
| abstract_inverted_index.differs | 248 |
| abstract_inverted_index.enhance | 275 |
| abstract_inverted_index.further | 123 |
| abstract_inverted_index.outflow | 240 |
| abstract_inverted_index.overall | 66 |
| abstract_inverted_index.problem | 71 |
| abstract_inverted_index.provide | 74 |
| abstract_inverted_index.reduced | 124 |
| abstract_inverted_index.regions | 242 |
| abstract_inverted_index.results | 158, 167, 221, 283 |
| abstract_inverted_index.spatial | 37 |
| abstract_inverted_index.surgery | 83 |
| abstract_inverted_index.trained | 104 |
| abstract_inverted_index.without | 125 |
| abstract_inverted_index.accuracy | 127 |
| abstract_inverted_index.analysis | 144 |
| abstract_inverted_index.approach | 211 |
| abstract_inverted_index.assessed | 141 |
| abstract_inverted_index.averaged | 243 |
| abstract_inverted_index.compared | 177, 223, 252 |
| abstract_inverted_index.computer | 31 |
| abstract_inverted_index.detailed | 78 |
| abstract_inverted_index.dynamics | 11 |
| abstract_inverted_index.findings | 207 |
| abstract_inverted_index.however, | 49 |
| abstract_inverted_index.improved | 112 |
| abstract_inverted_index.increase | 92 |
| abstract_inverted_index.networks | 89 |
| abstract_inverted_index.obtained | 29, 180 |
| abstract_inverted_index.original | 229 |
| abstract_inverted_index.overcome | 69 |
| abstract_inverted_index.patients | 58, 75, 247 |
| abstract_inverted_index.pressure | 233 |
| abstract_inverted_index.recorded | 50 |
| abstract_inverted_index.regions. | 134 |
| abstract_inverted_index.reliable | 281 |
| abstract_inverted_index.requires | 14 |
| abstract_inverted_index.resolved | 16 |
| abstract_inverted_index.surfaces | 149, 164 |
| abstract_inverted_index.training | 120 |
| abstract_inverted_index.(pharynx) | 241 |
| abstract_inverted_index.SRN-based | 210, 268 |
| abstract_inverted_index.analyzed. | 97 |
| abstract_inverted_index.automated | 115 |
| abstract_inverted_index.comparing | 157 |
| abstract_inverted_index.computing | 1 |
| abstract_inverted_index.concluded | 265 |
| abstract_inverted_index.data-sets | 184 |
| abstract_inverted_index.developed | 102 |
| abstract_inverted_index.featuring | 40 |
| abstract_inverted_index.numerical | 282 |
| abstract_inverted_index.planning, | 84 |
| abstract_inverted_index.potential | 86 |
| abstract_inverted_index.practice, | 46 |
| abstract_inverted_index.promising | 272 |
| abstract_inverted_index.radiation | 61 |
| abstract_inverted_index.realistic | 22 |
| abstract_inverted_index.(nostrils) | 238 |
| abstract_inverted_index.Accurately | 0 |
| abstract_inverted_index.Therefore, | 98 |
| abstract_inverted_index.deviations | 216 |
| abstract_inverted_index.diagnosis, | 80 |
| abstract_inverted_index.geometries | 25 |
| abstract_inverted_index.predictive | 109, 126 |
| abstract_inverted_index.resolution | 54, 95, 175, 202 |
| abstract_inverted_index.simulation | 166 |
| abstract_inverted_index.technique. | 118 |
| abstract_inverted_index.thickness. | 44 |
| abstract_inverted_index.tomography | 32 |
| abstract_inverted_index.unmodified | 182 |
| abstract_inverted_index.challenging | 133 |
| abstract_inverted_index.degradation | 128 |
| abstract_inverted_index.geometries. | 23 |
| abstract_inverted_index.performance | 110, 136 |
| abstract_inverted_index.resolution, | 38, 191 |
| abstract_inverted_index.respiratory | 285 |
| abstract_inverted_index.simulations | 13 |
| abstract_inverted_index.anatomically | 21 |
| abstract_inverted_index.interpolated | 198, 261 |
| abstract_inverted_index.optimization | 117 |
| abstract_inverted_index.oversampling | 130 |
| abstract_inverted_index.simulations. | 161 |
| abstract_inverted_index.SRN-generated | 170 |
| abstract_inverted_index.computational | 9, 17 |
| abstract_inverted_index.physics-based | 79, 219 |
| abstract_inverted_index.reconstructed | 147 |
| abstract_inverted_index.underresolved | 276 |
| abstract_inverted_index.hyperparameter | 116 |
| abstract_inverted_index.high-resolution | 230 |
| abstract_inverted_index.super-resolution | 88 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5079944768, https://openalex.org/A5017441603 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I171892758, https://openalex.org/I4210133912, https://openalex.org/I887968799 |
| citation_normalized_percentile.value | 0.89819688 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |