Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT images Article Swipe
Yongfeng Gao
,
Jiaxing Tan
,
Yongyi Shi
,
Siming Lu
,
Amit Gupta
,
Haifang Li
,
Zhengrong Liang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1117/1.jmi.7.3.032502
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1117/1.jmi.7.3.032502
Purpose: Bayesian theory provides a sound framework for ultralow-dose computed tomography (ULdCT) image reconstruction with two terms for modeling the data statistical property and incorporating a priori knowledge for the image that is to be reconstructed. We investigate the feasibility of using a machine learning (ML) strategy, particularly the convolutional neural network (CNN), to construct a tissue-specific texture prior from previous full-dose computed tomography. Approach: Our study constructs four tissue-specific texture priors, corresponding with lung, bone, fat, and muscle, and integrates the prior with the prelog shift Poisson (SP) data property for Bayesian reconstruction of ULdCT images. The Bayesian reconstruction was implemented by an algorithm called SP-CNN-T and compared with our previous Markov random field (MRF)-based tissue-specific texture prior algorithm called SP-MRF-T. Results: In addition to conventional quantitative measures, mean squared error and peak signal-to-noise ratio, structure similarity index, feature similarity, and texture Haralick features were used to measure the performance difference between SP-CNN-T and SP-MRF-T algorithms in terms of the structure and tissue texture preservation, demonstrating the feasibility and the potential of the investigated ML approach. Conclusions: Both training performance and image reconstruction results showed the feasibility of constructing CNN texture prior model and the potential of improving the structure preservation of the nodule comparing to our previous regional tissue-specific MRF texture prior model.
Related Topics
Concepts
Artificial intelligence
Markov random field
Pattern recognition (psychology)
Convolutional neural network
Texture (cosmology)
Feature (linguistics)
Similarity (geometry)
Bayesian probability
Iterative reconstruction
Maximum a posteriori estimation
Prior probability
Computer science
Medicine
Image (mathematics)
Mathematics
Image segmentation
Statistics
Philosophy
Linguistics
Maximum likelihood
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/1.jmi.7.3.032502
- OA Status
- green
- Cited By
- 7
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3007750044
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3007750044Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1117/1.jmi.7.3.032502Digital Object Identifier
- Title
-
Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-02-25Full publication date if available
- Authors
-
Yongfeng Gao, Jiaxing Tan, Yongyi Shi, Siming Lu, Amit Gupta, Haifang Li, Zhengrong LiangList of authors in order
- Landing page
-
https://doi.org/10.1117/1.jmi.7.3.032502Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/7040436Direct OA link when available
- Concepts
-
Artificial intelligence, Markov random field, Pattern recognition (psychology), Convolutional neural network, Texture (cosmology), Feature (linguistics), Similarity (geometry), Bayesian probability, Iterative reconstruction, Maximum a posteriori estimation, Prior probability, Computer science, Medicine, Image (mathematics), Mathematics, Image segmentation, Statistics, Philosophy, Linguistics, Maximum likelihoodTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 3, 2021: 2, 2020: 1Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3007750044 |
|---|---|
| doi | https://doi.org/10.1117/1.jmi.7.3.032502 |
| ids.doi | https://doi.org/10.1117/1.jmi.7.3.032502 |
| ids.mag | 3007750044 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/32118093 |
| ids.openalex | https://openalex.org/W3007750044 |
| fwci | 0.75429136 |
| type | article |
| title | Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT images |
| biblio.issue | 03 |
| biblio.volume | 7 |
| biblio.last_page | 1 |
| biblio.first_page | 1 |
| 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 | 0.9998999834060669 |
| 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 | Medical Imaging Techniques and Applications |
| topics[1].id | https://openalex.org/T12386 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9998000264167786 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2204 |
| topics[1].subfield.display_name | Biomedical Engineering |
| topics[1].display_name | Advanced X-ray and CT Imaging |
| topics[2].id | https://openalex.org/T10844 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9993000030517578 |
| 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 | Radiation Dose and Imaging |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.7548037767410278 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C2778045648 |
| concepts[1].level | 4 |
| concepts[1].score | 0.6703979969024658 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q176827 |
| concepts[1].display_name | Markov random field |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6554505825042725 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| concepts[3].id | https://openalex.org/C81363708 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6149577498435974 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[3].display_name | Convolutional neural network |
| concepts[4].id | https://openalex.org/C2781195486 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5564056634902954 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q289436 |
| concepts[4].display_name | Texture (cosmology) |
| concepts[5].id | https://openalex.org/C2776401178 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5482363104820251 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[5].display_name | Feature (linguistics) |
| concepts[6].id | https://openalex.org/C103278499 |
| concepts[6].level | 3 |
| concepts[6].score | 0.524385392665863 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q254465 |
| concepts[6].display_name | Similarity (geometry) |
| concepts[7].id | https://openalex.org/C107673813 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5096383094787598 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q812534 |
| concepts[7].display_name | Bayesian probability |
| concepts[8].id | https://openalex.org/C141379421 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4630994498729706 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q6094427 |
| concepts[8].display_name | Iterative reconstruction |
| concepts[9].id | https://openalex.org/C9810830 |
| concepts[9].level | 3 |
| concepts[9].score | 0.4552158713340759 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q635384 |
| concepts[9].display_name | Maximum a posteriori estimation |
| concepts[10].id | https://openalex.org/C177769412 |
| concepts[10].level | 3 |
| concepts[10].score | 0.42151832580566406 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q278090 |
| concepts[10].display_name | Prior probability |
| concepts[11].id | https://openalex.org/C41008148 |
| concepts[11].level | 0 |
| concepts[11].score | 0.41038978099823 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[11].display_name | Computer science |
| concepts[12].id | https://openalex.org/C71924100 |
| concepts[12].level | 0 |
| concepts[12].score | 0.3744524419307709 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[12].display_name | Medicine |
| concepts[13].id | https://openalex.org/C115961682 |
| concepts[13].level | 2 |
| concepts[13].score | 0.297011137008667 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[13].display_name | Image (mathematics) |
| concepts[14].id | https://openalex.org/C33923547 |
| concepts[14].level | 0 |
| concepts[14].score | 0.2944827675819397 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[14].display_name | Mathematics |
| concepts[15].id | https://openalex.org/C124504099 |
| concepts[15].level | 3 |
| concepts[15].score | 0.14964494109153748 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q56933 |
| concepts[15].display_name | Image segmentation |
| concepts[16].id | https://openalex.org/C105795698 |
| concepts[16].level | 1 |
| concepts[16].score | 0.14651140570640564 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[16].display_name | Statistics |
| concepts[17].id | https://openalex.org/C138885662 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[17].display_name | Philosophy |
| concepts[18].id | https://openalex.org/C41895202 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[18].display_name | Linguistics |
| concepts[19].id | https://openalex.org/C49781872 |
| concepts[19].level | 2 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q1045555 |
| concepts[19].display_name | Maximum likelihood |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.7548037767410278 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/markov-random-field |
| keywords[1].score | 0.6703979969024658 |
| keywords[1].display_name | Markov random field |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.6554505825042725 |
| keywords[2].display_name | Pattern recognition (psychology) |
| keywords[3].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[3].score | 0.6149577498435974 |
| keywords[3].display_name | Convolutional neural network |
| keywords[4].id | https://openalex.org/keywords/texture |
| keywords[4].score | 0.5564056634902954 |
| keywords[4].display_name | Texture (cosmology) |
| keywords[5].id | https://openalex.org/keywords/feature |
| keywords[5].score | 0.5482363104820251 |
| keywords[5].display_name | Feature (linguistics) |
| keywords[6].id | https://openalex.org/keywords/similarity |
| keywords[6].score | 0.524385392665863 |
| keywords[6].display_name | Similarity (geometry) |
| keywords[7].id | https://openalex.org/keywords/bayesian-probability |
| keywords[7].score | 0.5096383094787598 |
| keywords[7].display_name | Bayesian probability |
| keywords[8].id | https://openalex.org/keywords/iterative-reconstruction |
| keywords[8].score | 0.4630994498729706 |
| keywords[8].display_name | Iterative reconstruction |
| keywords[9].id | https://openalex.org/keywords/maximum-a-posteriori-estimation |
| keywords[9].score | 0.4552158713340759 |
| keywords[9].display_name | Maximum a posteriori estimation |
| keywords[10].id | https://openalex.org/keywords/prior-probability |
| keywords[10].score | 0.42151832580566406 |
| keywords[10].display_name | Prior probability |
| keywords[11].id | https://openalex.org/keywords/computer-science |
| keywords[11].score | 0.41038978099823 |
| keywords[11].display_name | Computer science |
| keywords[12].id | https://openalex.org/keywords/medicine |
| keywords[12].score | 0.3744524419307709 |
| keywords[12].display_name | Medicine |
| keywords[13].id | https://openalex.org/keywords/image |
| keywords[13].score | 0.297011137008667 |
| keywords[13].display_name | Image (mathematics) |
| keywords[14].id | https://openalex.org/keywords/mathematics |
| keywords[14].score | 0.2944827675819397 |
| keywords[14].display_name | Mathematics |
| keywords[15].id | https://openalex.org/keywords/image-segmentation |
| keywords[15].score | 0.14964494109153748 |
| keywords[15].display_name | Image segmentation |
| keywords[16].id | https://openalex.org/keywords/statistics |
| keywords[16].score | 0.14651140570640564 |
| keywords[16].display_name | Statistics |
| language | en |
| locations[0].id | doi:10.1117/1.jmi.7.3.032502 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S4210193455 |
| locations[0].source.issn | 2329-4302, 2329-4310 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2329-4302 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Medical Imaging |
| locations[0].source.host_organization | https://openalex.org/P4310315543 |
| locations[0].source.host_organization_name | SPIE |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310315543 |
| locations[0].source.host_organization_lineage_names | SPIE |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Journal of Medical Imaging |
| locations[0].landing_page_url | https://doi.org/10.1117/1.jmi.7.3.032502 |
| locations[1].id | pmid:32118093 |
| 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 | Journal of medical imaging (Bellingham, Wash.) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/32118093 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:7040436 |
| 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 | J Med Imaging (Bellingham) |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7040436 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5028884087 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6169-3478 |
| authorships[0].author.display_name | Yongfeng Gao |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I1327163397 |
| authorships[0].affiliations[0].raw_affiliation_string | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[0].institutions[0].id | https://openalex.org/I1327163397 |
| authorships[0].institutions[0].ror | https://ror.org/01q1z8k08 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I1327163397 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | State University of New York |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yongfeng Gao |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[1].author.id | https://openalex.org/A5101972788 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9908-3691 |
| authorships[1].author.display_name | Jiaxing Tan |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1327163397 |
| authorships[1].affiliations[0].raw_affiliation_string | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[1].institutions[0].id | https://openalex.org/I1327163397 |
| authorships[1].institutions[0].ror | https://ror.org/01q1z8k08 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I1327163397 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | State University of New York |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jiaxing Tan |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[2].author.id | https://openalex.org/A5041502507 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3751-9450 |
| authorships[2].author.display_name | Yongyi Shi |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1327163397 |
| authorships[2].affiliations[0].raw_affiliation_string | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[2].institutions[0].id | https://openalex.org/I1327163397 |
| authorships[2].institutions[0].ror | https://ror.org/01q1z8k08 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I1327163397 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | State University of New York |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yongyi Shi |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[3].author.id | https://openalex.org/A5110746204 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Siming Lu |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I59553526 |
| authorships[3].affiliations[0].raw_affiliation_string | Stony Brook Univ. (United States) |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I1327163397 |
| authorships[3].affiliations[1].raw_affiliation_string | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[3].institutions[0].id | https://openalex.org/I1327163397 |
| authorships[3].institutions[0].ror | https://ror.org/01q1z8k08 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I1327163397 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | State University of New York |
| authorships[3].institutions[1].id | https://openalex.org/I59553526 |
| authorships[3].institutions[1].ror | https://ror.org/05qghxh33 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I59553526 |
| authorships[3].institutions[1].country_code | US |
| authorships[3].institutions[1].display_name | Stony Brook University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Siming Lu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | State University of New York, Department of Radiology, Stony Brook, New York, Stony Brook Univ. (United States) |
| authorships[4].author.id | https://openalex.org/A5068338358 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-4002-0669 |
| authorships[4].author.display_name | Amit Gupta |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1327163397 |
| authorships[4].affiliations[0].raw_affiliation_string | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[4].institutions[0].id | https://openalex.org/I1327163397 |
| authorships[4].institutions[0].ror | https://ror.org/01q1z8k08 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I1327163397 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | State University of New York |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Amit Gupta |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[5].author.id | https://openalex.org/A5101528670 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-7632-8497 |
| authorships[5].author.display_name | Haifang Li |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I1327163397 |
| authorships[5].affiliations[0].raw_affiliation_string | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[5].institutions[0].id | https://openalex.org/I1327163397 |
| authorships[5].institutions[0].ror | https://ror.org/01q1z8k08 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I1327163397 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | State University of New York |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Haifang Li |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[6].author.id | https://openalex.org/A5110322946 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Zhengrong Liang |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I59553526 |
| authorships[6].affiliations[0].raw_affiliation_string | Stony Brook Univ. (United States) |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I1327163397 |
| authorships[6].affiliations[1].raw_affiliation_string | State University of New York, Department of Radiology, Stony Brook, New York |
| authorships[6].institutions[0].id | https://openalex.org/I1327163397 |
| authorships[6].institutions[0].ror | https://ror.org/01q1z8k08 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I1327163397 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | State University of New York |
| authorships[6].institutions[1].id | https://openalex.org/I59553526 |
| authorships[6].institutions[1].ror | https://ror.org/05qghxh33 |
| authorships[6].institutions[1].type | education |
| authorships[6].institutions[1].lineage | https://openalex.org/I59553526 |
| authorships[6].institutions[1].country_code | US |
| authorships[6].institutions[1].display_name | Stony Brook University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Zhengrong Liang |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | State University of New York, Department of Radiology, Stony Brook, New York, Stony Brook Univ. (United States) |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7040436 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT images |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| 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 | 0.9998999834060669 |
| 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 | Medical Imaging Techniques and Applications |
| related_works | https://openalex.org/W1839961359, https://openalex.org/W2075146114, https://openalex.org/W2097090565, https://openalex.org/W2008444830, https://openalex.org/W2100805585, https://openalex.org/W2804375118, https://openalex.org/W2112506753, https://openalex.org/W2029809897, https://openalex.org/W2132110568, https://openalex.org/W2160831725 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2022 |
| counts_by_year[1].cited_by_count | 3 |
| counts_by_year[2].year | 2021 |
| counts_by_year[2].cited_by_count | 2 |
| counts_by_year[3].year | 2020 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:7040436 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| 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 | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| 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 | J Med Imaging (Bellingham) |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7040436 |
| primary_location.id | doi:10.1117/1.jmi.7.3.032502 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S4210193455 |
| primary_location.source.issn | 2329-4302, 2329-4310 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2329-4302 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Medical Imaging |
| primary_location.source.host_organization | https://openalex.org/P4310315543 |
| primary_location.source.host_organization_name | SPIE |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310315543 |
| primary_location.source.host_organization_lineage_names | SPIE |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Medical Imaging |
| primary_location.landing_page_url | https://doi.org/10.1117/1.jmi.7.3.032502 |
| publication_date | 2020-02-25 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2131720521, https://openalex.org/W2124735791, https://openalex.org/W2147821156, https://openalex.org/W2080477227, https://openalex.org/W2311563794, https://openalex.org/W2288483235, https://openalex.org/W2971496199, https://openalex.org/W4200409436, https://openalex.org/W2584483805, https://openalex.org/W2574952845, https://openalex.org/W4231200252, https://openalex.org/W2963470893, https://openalex.org/W2617128058, https://openalex.org/W2793419304, https://openalex.org/W2743780012, https://openalex.org/W2552453838, https://openalex.org/W2755573624, https://openalex.org/W2165399609, https://openalex.org/W2154280873, https://openalex.org/W2098092211, https://openalex.org/W2117893569, https://openalex.org/W2063305716, https://openalex.org/W4296142362, https://openalex.org/W2081917971, https://openalex.org/W2104455572, https://openalex.org/W2167890826, https://openalex.org/W2054218460, https://openalex.org/W2947795511, https://openalex.org/W2573726823, https://openalex.org/W2613155248, https://openalex.org/W2911459672, https://openalex.org/W2971995640, https://openalex.org/W1981025032, https://openalex.org/W2109255472, https://openalex.org/W2141689871, https://openalex.org/W2750897499, https://openalex.org/W2141983208, https://openalex.org/W2044465660, https://openalex.org/W2345136208, https://openalex.org/W1987869189, https://openalex.org/W2761360575, https://openalex.org/W2946890405, https://openalex.org/W3104324122, https://openalex.org/W2542870090, https://openalex.org/W2143163922, https://openalex.org/W2907215791, https://openalex.org/W3007750044 |
| referenced_works_count | 47 |
| abstract_inverted_index.a | 4, 42, 55 |
| abstract_inverted_index.In | 123 |
| abstract_inverted_index.ML | 175 |
| abstract_inverted_index.We | 36 |
| abstract_inverted_index.an | 103 |
| abstract_inverted_index.be | 34 |
| abstract_inverted_index.by | 102 |
| abstract_inverted_index.in | 157 |
| abstract_inverted_index.is | 32 |
| abstract_inverted_index.of | 40, 94, 159, 172, 188, 197, 202 |
| abstract_inverted_index.to | 33, 53, 125, 147, 206 |
| abstract_inverted_index.CNN | 190 |
| abstract_inverted_index.MRF | 211 |
| abstract_inverted_index.Our | 65 |
| abstract_inverted_index.The | 97 |
| abstract_inverted_index.and | 23, 77, 79, 107, 132, 141, 154, 162, 169, 181, 194 |
| abstract_inverted_index.for | 7, 17, 28, 91 |
| abstract_inverted_index.our | 110, 207 |
| abstract_inverted_index.the | 19, 29, 38, 48, 81, 84, 149, 160, 167, 170, 173, 186, 195, 199, 203 |
| abstract_inverted_index.two | 15 |
| abstract_inverted_index.was | 100 |
| abstract_inverted_index.(ML) | 45 |
| abstract_inverted_index.(SP) | 88 |
| abstract_inverted_index.<i>a | 25 |
| abstract_inverted_index.Both | 178 |
| abstract_inverted_index.data | 20, 89 |
| abstract_inverted_index.fat, | 76 |
| abstract_inverted_index.four | 68 |
| abstract_inverted_index.from | 59 |
| abstract_inverted_index.mean | 129 |
| abstract_inverted_index.peak | 133 |
| abstract_inverted_index.that | 31 |
| abstract_inverted_index.used | 146 |
| abstract_inverted_index.were | 145 |
| abstract_inverted_index.with | 14, 73, 83, 109 |
| abstract_inverted_index.ULdCT | 95 |
| abstract_inverted_index.bone, | 75 |
| abstract_inverted_index.error | 131 |
| abstract_inverted_index.field | 114 |
| abstract_inverted_index.image | 12, 30, 182 |
| abstract_inverted_index.lung, | 74 |
| abstract_inverted_index.model | 193 |
| abstract_inverted_index.prior | 58, 82, 118, 192, 213 |
| abstract_inverted_index.shift | 86 |
| abstract_inverted_index.sound | 5 |
| abstract_inverted_index.study | 66 |
| abstract_inverted_index.terms | 16, 158 |
| abstract_inverted_index.using | 41 |
| abstract_inverted_index.(CNN), | 52 |
| abstract_inverted_index.Markov | 112 |
| abstract_inverted_index.called | 105, 120 |
| abstract_inverted_index.index, | 138 |
| abstract_inverted_index.model. | 214 |
| abstract_inverted_index.neural | 50 |
| abstract_inverted_index.nodule | 204 |
| abstract_inverted_index.prelog | 85 |
| abstract_inverted_index.random | 113 |
| abstract_inverted_index.ratio, | 135 |
| abstract_inverted_index.showed | 185 |
| abstract_inverted_index.theory | 2 |
| abstract_inverted_index.tissue | 163 |
| abstract_inverted_index.(ULdCT) | 11 |
| abstract_inverted_index.Poisson | 87 |
| abstract_inverted_index.between | 152 |
| abstract_inverted_index.feature | 139 |
| abstract_inverted_index.images. | 96 |
| abstract_inverted_index.machine | 43 |
| abstract_inverted_index.measure | 148 |
| abstract_inverted_index.muscle, | 78 |
| abstract_inverted_index.network | 51 |
| abstract_inverted_index.priors, | 71 |
| abstract_inverted_index.results | 184 |
| abstract_inverted_index.squared | 130 |
| abstract_inverted_index.texture | 57, 70, 117, 142, 164, 191, 212 |
| abstract_inverted_index.Bayesian | 1, 92, 98 |
| abstract_inverted_index.Haralick | 143 |
| abstract_inverted_index.SP-CNN-T | 106, 153 |
| abstract_inverted_index.SP-MRF-T | 155 |
| abstract_inverted_index.addition | 124 |
| abstract_inverted_index.compared | 108 |
| abstract_inverted_index.computed | 9, 62 |
| abstract_inverted_index.features | 144 |
| abstract_inverted_index.learning | 44 |
| abstract_inverted_index.modeling | 18 |
| abstract_inverted_index.previous | 60, 111, 208 |
| abstract_inverted_index.property | 22, 90 |
| abstract_inverted_index.provides | 3 |
| abstract_inverted_index.regional | 209 |
| abstract_inverted_index.training | 179 |
| abstract_inverted_index.SP-MRF-T. | 121 |
| abstract_inverted_index.algorithm | 104, 119 |
| abstract_inverted_index.approach. | 176 |
| abstract_inverted_index.comparing | 205 |
| abstract_inverted_index.construct | 54 |
| abstract_inverted_index.framework | 6 |
| abstract_inverted_index.full-dose | 61 |
| abstract_inverted_index.improving | 198 |
| abstract_inverted_index.knowledge | 27 |
| abstract_inverted_index.measures, | 128 |
| abstract_inverted_index.potential | 171, 196 |
| abstract_inverted_index.strategy, | 46 |
| abstract_inverted_index.structure | 136, 161, 200 |
| abstract_inverted_index.algorithms | 156 |
| abstract_inverted_index.constructs | 67 |
| abstract_inverted_index.difference | 151 |
| abstract_inverted_index.integrates | 80 |
| abstract_inverted_index.priori</i> | 26 |
| abstract_inverted_index.similarity | 137 |
| abstract_inverted_index.tomography | 10 |
| abstract_inverted_index.(MRF)-based | 115 |
| abstract_inverted_index.feasibility | 39, 168, 187 |
| abstract_inverted_index.implemented | 101 |
| abstract_inverted_index.investigate | 37 |
| abstract_inverted_index.performance | 150, 180 |
| abstract_inverted_index.similarity, | 140 |
| abstract_inverted_index.statistical | 21 |
| abstract_inverted_index.tomography. | 63 |
| abstract_inverted_index.constructing | 189 |
| abstract_inverted_index.conventional | 126 |
| abstract_inverted_index.investigated | 174 |
| abstract_inverted_index.particularly | 47 |
| abstract_inverted_index.preservation | 201 |
| abstract_inverted_index.quantitative | 127 |
| abstract_inverted_index.convolutional | 49 |
| abstract_inverted_index.corresponding | 72 |
| abstract_inverted_index.demonstrating | 166 |
| abstract_inverted_index.incorporating | 24 |
| abstract_inverted_index.preservation, | 165 |
| abstract_inverted_index.ultralow-dose | 8 |
| abstract_inverted_index.reconstructed. | 35 |
| abstract_inverted_index.reconstruction | 13, 93, 99, 183 |
| abstract_inverted_index.<b>Purpose</b>: | 0 |
| abstract_inverted_index.<b>Results</b>: | 122 |
| abstract_inverted_index.signal-to-noise | 134 |
| abstract_inverted_index.tissue-specific | 56, 69, 116, 210 |
| abstract_inverted_index.<b>Approach:</b> | 64 |
| abstract_inverted_index.<b>Conclusions</b>: | 177 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.72476581 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |