Identifiability of spatiotemporal tissue perfusion models Article Swipe
Eve Shalom
,
S. Van Loo
,
Amirul Khan
,
Steven Sourbron
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ad4087
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6560/ad4087
Objective. Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model. Approach. For each of the two models, identifiability is explored theoretically and in-silico for three systems. Concentrations over space and time are simulated by forward propagation. Different levels of noise and temporal undersampling are added to investigate sensitivity to measurement error. Model parameters are fitted using a standard gradient descent algorithm, applied iteratively with a stepwise increasing time window. Model fitting is repeated with different initial values to probe uniqueness of the solution. Reconstruction accuracy is quantified for each parameter by comparison to the ground truth. Main results. Theoretical analysis shows that flows and volume fractions are only identifiable up to a constant, and that this degeneracy can be removed by proper choice of parameters. Simulations show that in all cases, the tissue concentrations can be reconstructed accurately. The one-compartment model shows accurate reconstruction of blood velocities and arterial input functions, independent of the initial values and robust to measurement error. The two-compartmental perfusion model was not fully identifiable, showing good reconstruction of arterial velocities and input functions, but multiple valid solutions for the perfusion parameters and venous velocities, and a strong sensitivity to measurement error in these parameters. Significance. These results support the use of one-compartment spatiotemporal flow models, but two-compartment perfusion models were not sufficiently identifiable. Future studies should investigate whether this degeneracy is resolved in more realistic 2D and 3D systems, by adding physically justified constraints, or by optimizing experimental parameters such as injection duration or temporal resolution.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6560/ad4087
- https://iopscience.iop.org/article/10.1088/1361-6560/ad4087/pdf
- OA Status
- hybrid
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394907902
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394907902Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6560/ad4087Digital Object Identifier
- Title
-
Identifiability of spatiotemporal tissue perfusion modelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-18Full publication date if available
- Authors
-
Eve Shalom, S. Van Loo, Amirul Khan, Steven SourbronList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6560/ad4087Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1361-6560/ad4087/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1361-6560/ad4087/pdfDirect OA link when available
- Concepts
-
Identifiability, Undersampling, Sensitivity (control systems), Biological system, Perfusion, Algorithm, Degeneracy (biology), Voxel, Computer science, Mathematics, Statistics, Artificial intelligence, Engineering, Biology, Electronic engineering, Medicine, Cardiology, BioinformaticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4394907902 |
|---|---|
| doi | https://doi.org/10.1088/1361-6560/ad4087 |
| ids.doi | https://doi.org/10.1088/1361-6560/ad4087 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/38636525 |
| ids.openalex | https://openalex.org/W4394907902 |
| fwci | 0.0 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D008954 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Models, Biological |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D008279 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Magnetic Resonance Imaging |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D010477 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Perfusion |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D013997 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Time Factors |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D006801 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Humans |
| mesh[5].qualifier_ui | Q000379 |
| mesh[5].descriptor_ui | D007091 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | methods |
| mesh[5].descriptor_name | Image Processing, Computer-Assisted |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D062211 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Spatio-Temporal Analysis |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D008954 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Models, Biological |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D008279 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Magnetic Resonance Imaging |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D010477 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Perfusion |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D013997 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Time Factors |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D006801 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Humans |
| mesh[12].qualifier_ui | Q000379 |
| mesh[12].descriptor_ui | D007091 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | methods |
| mesh[12].descriptor_name | Image Processing, Computer-Assisted |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D062211 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Spatio-Temporal Analysis |
| type | article |
| title | Identifiability of spatiotemporal tissue perfusion models |
| awards[0].id | https://openalex.org/G3189344353 |
| awards[0].funder_id | https://openalex.org/F4320334627 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2282622 |
| awards[0].funder_display_name | Engineering and Physical Sciences Research Council |
| biblio.issue | 11 |
| biblio.volume | 69 |
| biblio.last_page | 115034 |
| biblio.first_page | 115034 |
| topics[0].id | https://openalex.org/T11885 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | MRI in cancer diagnosis |
| topics[1].id | https://openalex.org/T10378 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9998999834060669 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Advanced MRI Techniques and Applications |
| topics[2].id | https://openalex.org/T11304 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9987000226974487 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2741 |
| topics[2].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[2].display_name | Advanced Neuroimaging Techniques and Applications |
| funders[0].id | https://openalex.org/F4320334627 |
| funders[0].ror | https://ror.org/0439y7842 |
| funders[0].display_name | Engineering and Physical Sciences Research Council |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C122770356 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7453621625900269 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1656753 |
| concepts[0].display_name | Identifiability |
| concepts[1].id | https://openalex.org/C136536468 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7221664786338806 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1225894 |
| concepts[1].display_name | Undersampling |
| concepts[2].id | https://openalex.org/C21200559 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5306381583213806 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7451068 |
| concepts[2].display_name | Sensitivity (control systems) |
| concepts[3].id | https://openalex.org/C186060115 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5017375946044922 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q30336093 |
| concepts[3].display_name | Biological system |
| concepts[4].id | https://openalex.org/C146957229 |
| concepts[4].level | 2 |
| concepts[4].score | 0.482297420501709 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1266915 |
| concepts[4].display_name | Perfusion |
| concepts[5].id | https://openalex.org/C11413529 |
| concepts[5].level | 1 |
| concepts[5].score | 0.48206716775894165 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[5].display_name | Algorithm |
| concepts[6].id | https://openalex.org/C2777727622 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4693041145801544 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5251772 |
| concepts[6].display_name | Degeneracy (biology) |
| concepts[7].id | https://openalex.org/C54170458 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4150250256061554 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q663554 |
| concepts[7].display_name | Voxel |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.40885013341903687 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.3836404085159302 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C105795698 |
| concepts[10].level | 1 |
| concepts[10].score | 0.19115465879440308 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[10].display_name | Statistics |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.18049216270446777 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C86803240 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[13].display_name | Biology |
| concepts[14].id | https://openalex.org/C24326235 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q126095 |
| concepts[14].display_name | Electronic engineering |
| concepts[15].id | https://openalex.org/C71924100 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[15].display_name | Medicine |
| concepts[16].id | https://openalex.org/C164705383 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q10379 |
| concepts[16].display_name | Cardiology |
| concepts[17].id | https://openalex.org/C60644358 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q128570 |
| concepts[17].display_name | Bioinformatics |
| keywords[0].id | https://openalex.org/keywords/identifiability |
| keywords[0].score | 0.7453621625900269 |
| keywords[0].display_name | Identifiability |
| keywords[1].id | https://openalex.org/keywords/undersampling |
| keywords[1].score | 0.7221664786338806 |
| keywords[1].display_name | Undersampling |
| keywords[2].id | https://openalex.org/keywords/sensitivity |
| keywords[2].score | 0.5306381583213806 |
| keywords[2].display_name | Sensitivity (control systems) |
| keywords[3].id | https://openalex.org/keywords/biological-system |
| keywords[3].score | 0.5017375946044922 |
| keywords[3].display_name | Biological system |
| keywords[4].id | https://openalex.org/keywords/perfusion |
| keywords[4].score | 0.482297420501709 |
| keywords[4].display_name | Perfusion |
| keywords[5].id | https://openalex.org/keywords/algorithm |
| keywords[5].score | 0.48206716775894165 |
| keywords[5].display_name | Algorithm |
| keywords[6].id | https://openalex.org/keywords/degeneracy |
| keywords[6].score | 0.4693041145801544 |
| keywords[6].display_name | Degeneracy (biology) |
| keywords[7].id | https://openalex.org/keywords/voxel |
| keywords[7].score | 0.4150250256061554 |
| keywords[7].display_name | Voxel |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.40885013341903687 |
| keywords[8].display_name | Computer science |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.3836404085159302 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/statistics |
| keywords[10].score | 0.19115465879440308 |
| keywords[10].display_name | Statistics |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.18049216270446777 |
| keywords[11].display_name | Artificial intelligence |
| language | en |
| locations[0].id | doi:10.1088/1361-6560/ad4087 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S20241394 |
| locations[0].source.issn | 0031-9155, 1361-6560 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0031-9155 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Physics in Medicine and Biology |
| locations[0].source.host_organization | https://openalex.org/P4310320083 |
| locations[0].source.host_organization_name | IOP Publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| locations[0].source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://iopscience.iop.org/article/10.1088/1361-6560/ad4087/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Physics in Medicine & Biology |
| locations[0].landing_page_url | https://doi.org/10.1088/1361-6560/ad4087 |
| locations[1].id | pmid:38636525 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Physics in medicine and biology |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/38636525 |
| locations[2].id | pmh:oai:archive.ugent.be:01JY3K5MQNXZQZEM3V8M0QE5QB |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400477 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | Ghent University Academic Bibliography (Ghent University) |
| locations[2].source.host_organization | https://openalex.org/I32597200 |
| locations[2].source.host_organization_name | Ghent University |
| locations[2].source.host_organization_lineage | https://openalex.org/I32597200 |
| locations[2].license | other-oa |
| locations[2].pdf_url | |
| locations[2].version | publishedVersion |
| locations[2].raw_type | info:eu-repo/semantics/publishedVersion |
| locations[2].license_id | https://openalex.org/licenses/other-oa |
| locations[2].is_accepted | True |
| locations[2].is_published | True |
| locations[2].raw_source_name | ISSN: 1361-6560 |
| locations[2].landing_page_url | https://biblio.ugent.be/publication/01JY3K5MQNXZQZEM3V8M0QE5QB |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5012078773 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8762-3726 |
| authorships[0].author.display_name | Eve Shalom |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I130828816 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Physics and Astronomy, University of Leeds, Sir William Henry Bragg Building, Woodhouse Lane, Leeds, West Yorkshire, LS2 9JT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND |
| authorships[0].institutions[0].id | https://openalex.org/I130828816 |
| authorships[0].institutions[0].ror | https://ror.org/024mrxd33 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I130828816 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | University of Leeds |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Eve S Shalom |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Physics and Astronomy, University of Leeds, Sir William Henry Bragg Building, Woodhouse Lane, Leeds, West Yorkshire, LS2 9JT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND |
| authorships[1].author.id | https://openalex.org/A5016065712 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4746-8500 |
| authorships[1].author.display_name | S. Van Loo |
| authorships[1].countries | BE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I32597200 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Applied Physics, University of Ghent, Faculty of engineering and architecture, Technicum blok 4, Sint Pietersnieuwstraat 41, Gent, 9000, BELGIUM |
| authorships[1].institutions[0].id | https://openalex.org/I32597200 |
| authorships[1].institutions[0].ror | https://ror.org/00cv9y106 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I32597200 |
| authorships[1].institutions[0].country_code | BE |
| authorships[1].institutions[0].display_name | Ghent University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sven Van Loo |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Applied Physics, University of Ghent, Faculty of engineering and architecture, Technicum blok 4, Sint Pietersnieuwstraat 41, Gent, 9000, BELGIUM |
| authorships[2].author.id | https://openalex.org/A5053139994 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7521-5458 |
| authorships[2].author.display_name | Amirul Khan |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I130828816 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Civil Engineering, University of Leeds, Woodhouse Lane, Woodhouse, Leeds, LS2 9LG, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND |
| authorships[2].institutions[0].id | https://openalex.org/I130828816 |
| authorships[2].institutions[0].ror | https://ror.org/024mrxd33 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I130828816 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | University of Leeds |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Amirul Khan |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Civil Engineering, University of Leeds, Woodhouse Lane, Woodhouse, Leeds, LS2 9LG, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND |
| authorships[3].author.id | https://openalex.org/A5079478282 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3374-3973 |
| authorships[3].author.display_name | Steven Sourbron |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I91136226 |
| authorships[3].affiliations[0].raw_affiliation_string | Division of Clinical Medicine, The University of Sheffield, 18 Claremont Crescent, Sheffield, S10 2TA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND |
| authorships[3].institutions[0].id | https://openalex.org/I91136226 |
| authorships[3].institutions[0].ror | https://ror.org/05krs5044 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I91136226 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | University of Sheffield |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Steven P Sourbron |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Division of Clinical Medicine, The University of Sheffield, 18 Claremont Crescent, Sheffield, S10 2TA, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://iopscience.iop.org/article/10.1088/1361-6560/ad4087/pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Identifiability of spatiotemporal tissue perfusion models |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11885 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | MRI in cancer diagnosis |
| related_works | https://openalex.org/W2109073422, https://openalex.org/W2887783772, https://openalex.org/W2101754595, https://openalex.org/W4249381695, https://openalex.org/W2534887053, https://openalex.org/W2026172757, https://openalex.org/W2080076470, https://openalex.org/W4389292014, https://openalex.org/W2394059563, https://openalex.org/W2017942469 |
| cited_by_count | 0 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1088/1361-6560/ad4087 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S20241394 |
| best_oa_location.source.issn | 0031-9155, 1361-6560 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0031-9155 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Physics in Medicine and Biology |
| best_oa_location.source.host_organization | https://openalex.org/P4310320083 |
| best_oa_location.source.host_organization_name | IOP Publishing |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| best_oa_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://iopscience.iop.org/article/10.1088/1361-6560/ad4087/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Physics in Medicine & Biology |
| best_oa_location.landing_page_url | https://doi.org/10.1088/1361-6560/ad4087 |
| primary_location.id | doi:10.1088/1361-6560/ad4087 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S20241394 |
| primary_location.source.issn | 0031-9155, 1361-6560 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0031-9155 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Physics in Medicine and Biology |
| primary_location.source.host_organization | https://openalex.org/P4310320083 |
| primary_location.source.host_organization_name | IOP Publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://iopscience.iop.org/article/10.1088/1361-6560/ad4087/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Physics in Medicine & Biology |
| primary_location.landing_page_url | https://doi.org/10.1088/1361-6560/ad4087 |
| publication_date | 2024-04-18 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2120002976, https://openalex.org/W2112728443, https://openalex.org/W2041148826, https://openalex.org/W2003789900, https://openalex.org/W3006349194, https://openalex.org/W2955194079, https://openalex.org/W1895559365, https://openalex.org/W2022365036, https://openalex.org/W2884414823, https://openalex.org/W2013468872, https://openalex.org/W6631190155, https://openalex.org/W2062495841, https://openalex.org/W3165207548, https://openalex.org/W2067448964, https://openalex.org/W2602152083, https://openalex.org/W2089513130, https://openalex.org/W2053316538, https://openalex.org/W2783740858, https://openalex.org/W4281742972, https://openalex.org/W6929958617, https://openalex.org/W6911493650, https://openalex.org/W4388420922, https://openalex.org/W4309642842, https://openalex.org/W3206980415, https://openalex.org/W2025725701, https://openalex.org/W2074145540, https://openalex.org/W2021835950, https://openalex.org/W2904808026, https://openalex.org/W2041780885, https://openalex.org/W1512888998, https://openalex.org/W4295904267, https://openalex.org/W3212344632, https://openalex.org/W3098349601, https://openalex.org/W1522301498, https://openalex.org/W4225651299 |
| referenced_works_count | 35 |
| abstract_inverted_index.a | 10, 50, 56, 106, 114, 161, 240 |
| abstract_inverted_index.2D | 280 |
| abstract_inverted_index.3D | 282 |
| abstract_inverted_index.as | 15, 295 |
| abstract_inverted_index.be | 168, 185 |
| abstract_inverted_index.by | 12, 83, 140, 170, 284, 290 |
| abstract_inverted_index.in | 7, 43, 178, 246, 277 |
| abstract_inverted_index.is | 26, 38, 68, 121, 135, 275 |
| abstract_inverted_index.it | 25 |
| abstract_inverted_index.of | 35, 63, 88, 130, 173, 194, 202, 222, 255 |
| abstract_inverted_index.or | 289, 298 |
| abstract_inverted_index.to | 39, 95, 98, 127, 142, 160, 208, 243 |
| abstract_inverted_index.up | 159 |
| abstract_inverted_index.For | 61 |
| abstract_inverted_index.The | 33, 188, 211 |
| abstract_inverted_index.aim | 34 |
| abstract_inverted_index.all | 179 |
| abstract_inverted_index.and | 55, 71, 79, 90, 153, 163, 197, 206, 225, 236, 239, 281 |
| abstract_inverted_index.are | 30, 81, 93, 103, 156 |
| abstract_inverted_index.but | 24, 228, 260 |
| abstract_inverted_index.can | 20, 167, 184 |
| abstract_inverted_index.for | 4, 73, 137, 232 |
| abstract_inverted_index.not | 216, 265 |
| abstract_inverted_index.the | 64, 131, 143, 181, 203, 233, 253 |
| abstract_inverted_index.toy | 47 |
| abstract_inverted_index.two | 65 |
| abstract_inverted_index.use | 254 |
| abstract_inverted_index.was | 215 |
| abstract_inverted_index.Main | 146 |
| abstract_inverted_index.bias | 11 |
| abstract_inverted_index.each | 62, 138 |
| abstract_inverted_index.flow | 53, 258 |
| abstract_inverted_index.good | 220 |
| abstract_inverted_index.more | 278 |
| abstract_inverted_index.only | 157 |
| abstract_inverted_index.over | 77 |
| abstract_inverted_index.show | 176 |
| abstract_inverted_index.such | 294 |
| abstract_inverted_index.that | 151, 164, 177 |
| abstract_inverted_index.they | 29 |
| abstract_inverted_index.this | 22, 36, 41, 165, 273 |
| abstract_inverted_index.time | 80, 117 |
| abstract_inverted_index.were | 264 |
| abstract_inverted_index.with | 49, 113, 123 |
| abstract_inverted_index.Model | 101, 119 |
| abstract_inverted_index.These | 250 |
| abstract_inverted_index.added | 94 |
| abstract_inverted_index.bias, | 23 |
| abstract_inverted_index.blood | 52, 195 |
| abstract_inverted_index.error | 245 |
| abstract_inverted_index.flows | 152 |
| abstract_inverted_index.fully | 217 |
| abstract_inverted_index.input | 199, 226 |
| abstract_inverted_index.model | 54, 190, 214 |
| abstract_inverted_index.noise | 89 |
| abstract_inverted_index.probe | 128 |
| abstract_inverted_index.shows | 150, 191 |
| abstract_inverted_index.space | 78 |
| abstract_inverted_index.study | 37 |
| abstract_inverted_index.these | 247 |
| abstract_inverted_index.three | 74 |
| abstract_inverted_index.using | 45, 105 |
| abstract_inverted_index.valid | 230 |
| abstract_inverted_index.Future | 268 |
| abstract_inverted_index.adding | 285 |
| abstract_inverted_index.cases, | 180 |
| abstract_inverted_index.choice | 172 |
| abstract_inverted_index.error. | 100, 210 |
| abstract_inverted_index.fitted | 104 |
| abstract_inverted_index.ground | 144 |
| abstract_inverted_index.levels | 87 |
| abstract_inverted_index.model. | 59 |
| abstract_inverted_index.models | 3, 19, 263 |
| abstract_inverted_index.proper | 171 |
| abstract_inverted_index.remove | 21 |
| abstract_inverted_index.robust | 207 |
| abstract_inverted_index.should | 270 |
| abstract_inverted_index.silico | 44 |
| abstract_inverted_index.strong | 241 |
| abstract_inverted_index.tissue | 182 |
| abstract_inverted_index.truth. | 145 |
| abstract_inverted_index.values | 126, 205 |
| abstract_inverted_index.venous | 237 |
| abstract_inverted_index.volume | 154 |
| abstract_inverted_index.voxels | 14 |
| abstract_inverted_index.DCE-MRI | 8 |
| abstract_inverted_index.applied | 111 |
| abstract_inverted_index.descent | 109 |
| abstract_inverted_index.fitting | 120 |
| abstract_inverted_index.forward | 84 |
| abstract_inverted_index.initial | 125, 204 |
| abstract_inverted_index.models, | 66, 259 |
| abstract_inverted_index.produce | 9 |
| abstract_inverted_index.removed | 169 |
| abstract_inverted_index.results | 251 |
| abstract_inverted_index.showing | 219 |
| abstract_inverted_index.studies | 269 |
| abstract_inverted_index.support | 252 |
| abstract_inverted_index.systems | 48 |
| abstract_inverted_index.unknown | 27 |
| abstract_inverted_index.whether | 28, 272 |
| abstract_inverted_index.window. | 118 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Standard | 2 |
| abstract_inverted_index.accuracy | 134 |
| abstract_inverted_index.accurate | 192 |
| abstract_inverted_index.analysis | 149 |
| abstract_inverted_index.arterial | 198, 223 |
| abstract_inverted_index.duration | 297 |
| abstract_inverted_index.explored | 69 |
| abstract_inverted_index.gradient | 108 |
| abstract_inverted_index.isolated | 16 |
| abstract_inverted_index.multiple | 229 |
| abstract_inverted_index.question | 42 |
| abstract_inverted_index.repeated | 122 |
| abstract_inverted_index.resolved | 276 |
| abstract_inverted_index.results. | 147 |
| abstract_inverted_index.standard | 107 |
| abstract_inverted_index.stepwise | 115 |
| abstract_inverted_index.systems, | 283 |
| abstract_inverted_index.systems. | 17, 75 |
| abstract_inverted_index.temporal | 91, 299 |
| abstract_inverted_index.treating | 13 |
| abstract_inverted_index.Approach. | 60 |
| abstract_inverted_index.Different | 86 |
| abstract_inverted_index.constant, | 162 |
| abstract_inverted_index.different | 124 |
| abstract_inverted_index.fractions | 155 |
| abstract_inverted_index.in-silico | 72 |
| abstract_inverted_index.injection | 296 |
| abstract_inverted_index.justified | 287 |
| abstract_inverted_index.parameter | 139 |
| abstract_inverted_index.perfusion | 5, 58, 213, 234, 262 |
| abstract_inverted_index.realistic | 279 |
| abstract_inverted_index.simulated | 82 |
| abstract_inverted_index.solution. | 132 |
| abstract_inverted_index.solutions | 231 |
| abstract_inverted_index.Objective. | 1 |
| abstract_inverted_index.algorithm, | 110 |
| abstract_inverted_index.comparison | 141 |
| abstract_inverted_index.degeneracy | 166, 274 |
| abstract_inverted_index.functions, | 200, 227 |
| abstract_inverted_index.increasing | 116 |
| abstract_inverted_index.optimizing | 291 |
| abstract_inverted_index.parameters | 102, 235, 293 |
| abstract_inverted_index.physically | 286 |
| abstract_inverted_index.quantified | 136 |
| abstract_inverted_index.uniqueness | 129 |
| abstract_inverted_index.velocities | 196, 224 |
| abstract_inverted_index.Simulations | 175 |
| abstract_inverted_index.Theoretical | 148 |
| abstract_inverted_index.accurately. | 187 |
| abstract_inverted_index.independent | 201 |
| abstract_inverted_index.investigate | 40, 96, 271 |
| abstract_inverted_index.iteratively | 112 |
| abstract_inverted_index.measurement | 99, 209, 244 |
| abstract_inverted_index.parameters. | 174, 248 |
| abstract_inverted_index.resolution. | 300 |
| abstract_inverted_index.sensitivity | 97, 242 |
| abstract_inverted_index.velocities, | 238 |
| abstract_inverted_index.constraints, | 288 |
| abstract_inverted_index.experimental | 292 |
| abstract_inverted_index.identifiable | 158 |
| abstract_inverted_index.propagation. | 85 |
| abstract_inverted_index.sufficiently | 266 |
| abstract_inverted_index.Significance. | 249 |
| abstract_inverted_index.fundamentally | 31 |
| abstract_inverted_index.identifiable, | 218 |
| abstract_inverted_index.identifiable. | 32, 267 |
| abstract_inverted_index.reconstructed | 186 |
| abstract_inverted_index.theoretically | 70 |
| abstract_inverted_index.undersampling | 92 |
| abstract_inverted_index.Concentrations | 76 |
| abstract_inverted_index.Reconstruction | 133 |
| abstract_inverted_index.Spatiotemporal | 18 |
| abstract_inverted_index.concentrations | 183 |
| abstract_inverted_index.quantification | 6 |
| abstract_inverted_index.reconstruction | 193, 221 |
| abstract_inverted_index.spatiotemporal | 257 |
| abstract_inverted_index.identifiability | 67 |
| abstract_inverted_index.one-compartment | 51, 189, 256 |
| abstract_inverted_index.one-dimensional | 46 |
| abstract_inverted_index.two-compartment | 57, 261 |
| abstract_inverted_index.two-compartmental | 212 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.0904669 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |