Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT Article Swipe
Wenjun Xia
,
Yongyi Shi
,
Chuang Niu
,
Wenxiang Cong
,
Ge Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.06949
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.06949
Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image quality. To address this challenge, here we introduce an iterative reconstruction algorithm regularized by a diffusion prior. Drawing on the exceptional imaging prowess of the denoising diffusion probabilistic model (DDPM), we merge it with a reconstruction procedure that prioritizes data fidelity. This fusion capitalizes on the merits of both techniques, delivering exceptional reconstruction results in an unsupervised framework. To further enhance the efficiency of the reconstruction process, we incorporate the Nesterov momentum acceleration technique. This enhancement facilitates superior diffusion sampling in fewer steps. As demonstrated in our experiments, our method offers a potential pathway to high-definition CT image reconstruction with minimized radiation.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/37873003
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387595863
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387595863Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2310.06949Digital Object Identifier
- Title
-
Diffusion Prior Regularized Iterative Reconstruction for Low-dose CTWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-10Full publication date if available
- Authors
-
Wenjun Xia, Yongyi Shi, Chuang Niu, Wenxiang Cong, Ge WangList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/37873003Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2310.06949Direct OA link when available
- Concepts
-
Iterative reconstruction, Computer science, Algorithm, Artificial intelligence, Fidelity, Diffusion map, Image quality, Tomography, Computer vision, Physics, Image (mathematics), Optics, Dimensionality reduction, Telecommunications, Nonlinear dimensionality reductionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387595863 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2310.06949 |
| ids.doi | https://doi.org/10.48550/arxiv.2310.06949 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37873003 |
| ids.openalex | https://openalex.org/W4387595863 |
| fwci | |
| type | preprint |
| title | Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10522 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9995999932289124 |
| 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 | Medical Imaging Techniques and Applications |
| topics[1].id | https://openalex.org/T11205 |
| topics[1].field.id | https://openalex.org/fields/26 |
| topics[1].field.display_name | Mathematics |
| topics[1].score | 0.9855999946594238 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2610 |
| topics[1].subfield.display_name | Mathematical Physics |
| topics[1].display_name | Numerical methods in inverse problems |
| topics[2].id | https://openalex.org/T10378 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.984499990940094 |
| 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 MRI Techniques and Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C141379421 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6658257246017456 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q6094427 |
| concepts[0].display_name | Iterative reconstruction |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5618396401405334 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C11413529 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5162996649742126 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[2].display_name | Algorithm |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4967039227485657 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C2776459999 |
| concepts[4].level | 2 |
| concepts[4].score | 0.45685580372810364 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2119376 |
| concepts[4].display_name | Fidelity |
| concepts[5].id | https://openalex.org/C55128770 |
| concepts[5].level | 4 |
| concepts[5].score | 0.43352892994880676 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5275440 |
| concepts[5].display_name | Diffusion map |
| concepts[6].id | https://openalex.org/C55020928 |
| concepts[6].level | 3 |
| concepts[6].score | 0.41310763359069824 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q3813865 |
| concepts[6].display_name | Image quality |
| concepts[7].id | https://openalex.org/C163716698 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4110536277294159 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q841267 |
| concepts[7].display_name | Tomography |
| concepts[8].id | https://openalex.org/C31972630 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4076705276966095 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[8].display_name | Computer vision |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.2413376271724701 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C115961682 |
| concepts[10].level | 2 |
| concepts[10].score | 0.22453930974006653 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[10].display_name | Image (mathematics) |
| concepts[11].id | https://openalex.org/C120665830 |
| concepts[11].level | 1 |
| concepts[11].score | 0.17806518077850342 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[11].display_name | Optics |
| concepts[12].id | https://openalex.org/C70518039 |
| concepts[12].level | 2 |
| concepts[12].score | 0.1293106973171234 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q16000077 |
| concepts[12].display_name | Dimensionality reduction |
| concepts[13].id | https://openalex.org/C76155785 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[13].display_name | Telecommunications |
| concepts[14].id | https://openalex.org/C151876577 |
| concepts[14].level | 3 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7049464 |
| concepts[14].display_name | Nonlinear dimensionality reduction |
| keywords[0].id | https://openalex.org/keywords/iterative-reconstruction |
| keywords[0].score | 0.6658257246017456 |
| keywords[0].display_name | Iterative reconstruction |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5618396401405334 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/algorithm |
| keywords[2].score | 0.5162996649742126 |
| keywords[2].display_name | Algorithm |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.4967039227485657 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/fidelity |
| keywords[4].score | 0.45685580372810364 |
| keywords[4].display_name | Fidelity |
| keywords[5].id | https://openalex.org/keywords/diffusion-map |
| keywords[5].score | 0.43352892994880676 |
| keywords[5].display_name | Diffusion map |
| keywords[6].id | https://openalex.org/keywords/image-quality |
| keywords[6].score | 0.41310763359069824 |
| keywords[6].display_name | Image quality |
| keywords[7].id | https://openalex.org/keywords/tomography |
| keywords[7].score | 0.4110536277294159 |
| keywords[7].display_name | Tomography |
| keywords[8].id | https://openalex.org/keywords/computer-vision |
| keywords[8].score | 0.4076705276966095 |
| keywords[8].display_name | Computer vision |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.2413376271724701 |
| keywords[9].display_name | Physics |
| keywords[10].id | https://openalex.org/keywords/image |
| keywords[10].score | 0.22453930974006653 |
| keywords[10].display_name | Image (mathematics) |
| keywords[11].id | https://openalex.org/keywords/optics |
| keywords[11].score | 0.17806518077850342 |
| keywords[11].display_name | Optics |
| keywords[12].id | https://openalex.org/keywords/dimensionality-reduction |
| keywords[12].score | 0.1293106973171234 |
| keywords[12].display_name | Dimensionality reduction |
| language | en |
| locations[0].id | pmid:37873003 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S4306525036 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | PubMed |
| locations[0].source.host_organization | https://openalex.org/I1299303238 |
| locations[0].source.host_organization_name | National Institutes of Health |
| locations[0].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | ArXiv |
| locations[0].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37873003 |
| locations[1].id | pmh:oai:arXiv.org:2310.06949 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | https://arxiv.org/pdf/2310.06949 |
| locations[1].version | submittedVersion |
| locations[1].raw_type | text |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | http://arxiv.org/abs/2310.06949 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:10593071 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| 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 | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | ArXiv |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10593071 |
| locations[3].id | doi:10.48550/arxiv.2310.06949 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400194 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | True |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | arXiv (Cornell University) |
| locations[3].source.host_organization | https://openalex.org/I205783295 |
| locations[3].source.host_organization_name | Cornell University |
| locations[3].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | |
| locations[3].raw_type | article |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | https://doi.org/10.48550/arxiv.2310.06949 |
| indexed_in | arxiv, datacite, pubmed |
| authorships[0].author.id | https://openalex.org/A5045882773 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0428-1490 |
| authorships[0].author.display_name | Wenjun Xia |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xia, Wenjun |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5041502507 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3751-9450 |
| authorships[1].author.display_name | Yongyi Shi |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Shi, Yongyi |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5065193592 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3310-7803 |
| authorships[2].author.display_name | Chuang Niu |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Niu, Chuang |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5002629143 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2908-1759 |
| authorships[3].author.display_name | Wenxiang Cong |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Cong, Wenxiang |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5100400458 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-2656-7705 |
| authorships[4].author.display_name | Ge Wang |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Wang, Ge |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2310.06949 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10522 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9995999932289124 |
| 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 | Medical Imaging Techniques and Applications |
| related_works | https://openalex.org/W2757389719, https://openalex.org/W2951714568, https://openalex.org/W1963814553, https://openalex.org/W2037595954, https://openalex.org/W1988158806, https://openalex.org/W2507293823, https://openalex.org/W2386146599, https://openalex.org/W2018015402, https://openalex.org/W2144778520, https://openalex.org/W2801815023 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 4 |
| best_oa_location.id | pmh:oai:arXiv.org:2310.06949 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2310.06949 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2310.06949 |
| primary_location.id | pmid:37873003 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S4306525036 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | PubMed |
| primary_location.source.host_organization | https://openalex.org/I1299303238 |
| primary_location.source.host_organization_name | National Institutes of Health |
| primary_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | ArXiv |
| primary_location.landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37873003 |
| publication_date | 2023-10-10 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 4, 49, 69, 126 |
| abstract_inverted_index.As | 118 |
| abstract_inverted_index.CT | 131 |
| abstract_inverted_index.To | 10, 36, 93 |
| abstract_inverted_index.an | 43, 90 |
| abstract_inverted_index.by | 48 |
| abstract_inverted_index.in | 89, 115, 120 |
| abstract_inverted_index.it | 67 |
| abstract_inverted_index.of | 29, 58, 82, 98 |
| abstract_inverted_index.on | 53, 79 |
| abstract_inverted_index.or | 23 |
| abstract_inverted_index.to | 7, 129 |
| abstract_inverted_index.we | 15, 41, 65, 102 |
| abstract_inverted_index.can | 16 |
| abstract_inverted_index.our | 121, 123 |
| abstract_inverted_index.the | 12, 19, 30, 54, 59, 80, 96, 99, 104 |
| abstract_inverted_index.(CT) | 2 |
| abstract_inverted_index.This | 76, 109 |
| abstract_inverted_index.both | 83 |
| abstract_inverted_index.data | 74 |
| abstract_inverted_index.here | 40 |
| abstract_inverted_index.that | 72 |
| abstract_inverted_index.this | 38 |
| abstract_inverted_index.ways | 31 |
| abstract_inverted_index.with | 68, 134 |
| abstract_inverted_index.X-ray | 20 |
| abstract_inverted_index.count | 22 |
| abstract_inverted_index.dose, | 14 |
| abstract_inverted_index.fewer | 116 |
| abstract_inverted_index.image | 34, 132 |
| abstract_inverted_index.lower | 18 |
| abstract_inverted_index.merge | 66 |
| abstract_inverted_index.model | 63 |
| abstract_inverted_index.often | 32 |
| abstract_inverted_index.either | 17, 28 |
| abstract_inverted_index.fusion | 77 |
| abstract_inverted_index.merits | 81 |
| abstract_inverted_index.method | 124 |
| abstract_inverted_index.offers | 125 |
| abstract_inverted_index.photon | 21 |
| abstract_inverted_index.prior. | 51 |
| abstract_inverted_index.reduce | 11 |
| abstract_inverted_index.steps. | 117 |
| abstract_inverted_index.views. | 26 |
| abstract_inverted_index.(DDPM), | 64 |
| abstract_inverted_index.Drawing | 52 |
| abstract_inverted_index.address | 37 |
| abstract_inverted_index.enhance | 95 |
| abstract_inverted_index.further | 94 |
| abstract_inverted_index.imaging | 56 |
| abstract_inverted_index.pathway | 128 |
| abstract_inverted_index.prowess | 57 |
| abstract_inverted_index.results | 88 |
| abstract_inverted_index.Computed | 0 |
| abstract_inverted_index.However, | 27 |
| abstract_inverted_index.Nesterov | 105 |
| abstract_inverted_index.exposure | 6 |
| abstract_inverted_index.involves | 3 |
| abstract_inverted_index.ionizing | 8 |
| abstract_inverted_index.momentum | 106 |
| abstract_inverted_index.process, | 101 |
| abstract_inverted_index.quality. | 35 |
| abstract_inverted_index.sampling | 114 |
| abstract_inverted_index.superior | 112 |
| abstract_inverted_index.algorithm | 46 |
| abstract_inverted_index.denoising | 60 |
| abstract_inverted_index.diffusion | 50, 61, 113 |
| abstract_inverted_index.fidelity. | 75 |
| abstract_inverted_index.introduce | 42 |
| abstract_inverted_index.iterative | 44 |
| abstract_inverted_index.minimized | 135 |
| abstract_inverted_index.patient's | 5 |
| abstract_inverted_index.potential | 127 |
| abstract_inverted_index.procedure | 71 |
| abstract_inverted_index.radiation | 13 |
| abstract_inverted_index.challenge, | 39 |
| abstract_inverted_index.delivering | 85 |
| abstract_inverted_index.efficiency | 97 |
| abstract_inverted_index.framework. | 92 |
| abstract_inverted_index.projection | 25 |
| abstract_inverted_index.radiation. | 9, 136 |
| abstract_inverted_index.technique. | 108 |
| abstract_inverted_index.tomography | 1 |
| abstract_inverted_index.capitalizes | 78 |
| abstract_inverted_index.compromises | 33 |
| abstract_inverted_index.down-sample | 24 |
| abstract_inverted_index.enhancement | 110 |
| abstract_inverted_index.exceptional | 55, 86 |
| abstract_inverted_index.facilitates | 111 |
| abstract_inverted_index.incorporate | 103 |
| abstract_inverted_index.prioritizes | 73 |
| abstract_inverted_index.regularized | 47 |
| abstract_inverted_index.techniques, | 84 |
| abstract_inverted_index.acceleration | 107 |
| abstract_inverted_index.demonstrated | 119 |
| abstract_inverted_index.experiments, | 122 |
| abstract_inverted_index.unsupervised | 91 |
| abstract_inverted_index.probabilistic | 62 |
| abstract_inverted_index.reconstruction | 45, 70, 87, 100, 133 |
| abstract_inverted_index.high-definition | 130 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |