A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery Article Swipe
Yue Hu
,
Xiaohan Liu
,
Mathews Jacob
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2018.2886290
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2018.2886290
Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized structured low-rank algorithm to recover images from their undersampled Fourier coefficients using infimal convolution regularizations. The image is modeled as the superposition of a piecewise constant component and a piecewise linear component. The Fourier coefficients of each component satisfy an annihilation relation, which results in a structured Toeplitz matrix. We exploit the low-rank property of the matrices to formulate a combined regularized optimization problem. In order to solve the problem efficiently and to avoid the high-memory demand resulting from the large-scale Toeplitz matrices, we introduce a fast and a memory-efficient algorithm based on the half-circulant approximation of the Toeplitz matrix. We demonstrate our algorithm in the context of single and multi-channel MR images recovery. Numerical experiments indicate that the proposed algorithm provides improved recovery performance over the state-of-the-art approaches.
Related Topics
Concepts
Toeplitz matrix
Circulant matrix
Algorithm
Convolution (computer science)
Mathematics
Rank (graph theory)
Matrix (chemical analysis)
Low-rank approximation
Piecewise
Mathematical optimization
Hankel matrix
Computer science
Artificial intelligence
Mathematical analysis
Artificial neural network
Pure mathematics
Combinatorics
Composite material
Materials science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tmi.2018.2886290
- OA Status
- green
- Cited By
- 30
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2902695296
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2902695296Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tmi.2018.2886290Digital Object Identifier
- Title
-
A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image RecoveryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-12-11Full publication date if available
- Authors
-
Yue Hu, Xiaohan Liu, Mathews JacobList of authors in order
- Landing page
-
https://doi.org/10.1109/tmi.2018.2886290Publisher 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/6559879Direct OA link when available
- Concepts
-
Toeplitz matrix, Circulant matrix, Algorithm, Convolution (computer science), Mathematics, Rank (graph theory), Matrix (chemical analysis), Low-rank approximation, Piecewise, Mathematical optimization, Hankel matrix, Computer science, Artificial intelligence, Mathematical analysis, Artificial neural network, Pure mathematics, Combinatorics, Composite material, Materials scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 3, 2022: 4, 2021: 7Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2902695296 |
|---|---|
| doi | https://doi.org/10.1109/tmi.2018.2886290 |
| ids.doi | https://doi.org/10.1109/tmi.2018.2886290 |
| ids.mag | 2902695296 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/30561342 |
| ids.openalex | https://openalex.org/W2902695296 |
| fwci | 4.11479177 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000465 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Algorithms |
| mesh[1].qualifier_ui | Q000000981 |
| mesh[1].descriptor_ui | D000842 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | diagnostic imaging |
| mesh[1].descriptor_name | Ankle |
| mesh[2].qualifier_ui | Q000000981 |
| mesh[2].descriptor_ui | D001921 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | diagnostic imaging |
| mesh[2].descriptor_name | Brain |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D006801 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Humans |
| mesh[4].qualifier_ui | Q000379 |
| mesh[4].descriptor_ui | D007091 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | methods |
| mesh[4].descriptor_name | Image Processing, Computer-Assisted |
| mesh[5].qualifier_ui | Q000379 |
| mesh[5].descriptor_ui | D008279 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | methods |
| mesh[5].descriptor_name | Magnetic Resonance Imaging |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D019047 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Phantoms, Imaging |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D000465 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Algorithms |
| mesh[8].qualifier_ui | Q000000981 |
| mesh[8].descriptor_ui | D000842 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | diagnostic imaging |
| mesh[8].descriptor_name | Ankle |
| mesh[9].qualifier_ui | Q000000981 |
| mesh[9].descriptor_ui | D001921 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | diagnostic imaging |
| mesh[9].descriptor_name | Brain |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D006801 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Humans |
| mesh[11].qualifier_ui | Q000379 |
| mesh[11].descriptor_ui | D007091 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | methods |
| mesh[11].descriptor_name | Image Processing, Computer-Assisted |
| mesh[12].qualifier_ui | Q000379 |
| mesh[12].descriptor_ui | D008279 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | methods |
| mesh[12].descriptor_name | Magnetic Resonance Imaging |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D019047 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Phantoms, Imaging |
| type | article |
| title | A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery |
| awards[0].id | https://openalex.org/G6232534283 |
| awards[0].funder_id | https://openalex.org/F4320337385 |
| awards[0].display_name | |
| awards[0].funder_award_id | F2016018 |
| awards[0].funder_display_name | NIH Blueprint for Neuroscience Research |
| awards[1].id | https://openalex.org/G266734223 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 1R01EB019961-01A1 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 8 |
| biblio.volume | 38 |
| biblio.last_page | 1851 |
| biblio.first_page | 1841 |
| topics[0].id | https://openalex.org/T10500 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2206 |
| topics[0].subfield.display_name | Computational Mechanics |
| topics[0].display_name | Sparse and Compressive Sensing Techniques |
| 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 | 0.9986000061035156 |
| 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/T10688 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.998199999332428 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Image and Signal Denoising Methods |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320337385 |
| funders[1].ror | https://ror.org/012wp4251 |
| funders[1].display_name | NIH Blueprint for Neuroscience Research |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C147710293 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8899467587471008 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q849428 |
| concepts[0].display_name | Toeplitz matrix |
| concepts[1].id | https://openalex.org/C115973184 |
| concepts[1].level | 2 |
| concepts[1].score | 0.635464608669281 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q245457 |
| concepts[1].display_name | Circulant matrix |
| concepts[2].id | https://openalex.org/C11413529 |
| concepts[2].level | 1 |
| concepts[2].score | 0.614302933216095 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[2].display_name | Algorithm |
| concepts[3].id | https://openalex.org/C45347329 |
| concepts[3].level | 3 |
| concepts[3].score | 0.598578691482544 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q5166604 |
| concepts[3].display_name | Convolution (computer science) |
| concepts[4].id | https://openalex.org/C33923547 |
| concepts[4].level | 0 |
| concepts[4].score | 0.5434404611587524 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[4].display_name | Mathematics |
| concepts[5].id | https://openalex.org/C164226766 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5370790958404541 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7293202 |
| concepts[5].display_name | Rank (graph theory) |
| concepts[6].id | https://openalex.org/C106487976 |
| concepts[6].level | 2 |
| concepts[6].score | 0.523570716381073 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q685816 |
| concepts[6].display_name | Matrix (chemical analysis) |
| concepts[7].id | https://openalex.org/C90199385 |
| concepts[7].level | 3 |
| concepts[7].score | 0.5226502418518066 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q6692777 |
| concepts[7].display_name | Low-rank approximation |
| concepts[8].id | https://openalex.org/C164660894 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4306890666484833 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2037833 |
| concepts[8].display_name | Piecewise |
| concepts[9].id | https://openalex.org/C126255220 |
| concepts[9].level | 1 |
| concepts[9].score | 0.38657233119010925 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[9].display_name | Mathematical optimization |
| concepts[10].id | https://openalex.org/C25023664 |
| concepts[10].level | 2 |
| concepts[10].score | 0.37292641401290894 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1575637 |
| concepts[10].display_name | Hankel matrix |
| concepts[11].id | https://openalex.org/C41008148 |
| concepts[11].level | 0 |
| concepts[11].score | 0.3426007628440857 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[11].display_name | Computer science |
| concepts[12].id | https://openalex.org/C154945302 |
| concepts[12].level | 1 |
| concepts[12].score | 0.1644965410232544 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[12].display_name | Artificial intelligence |
| concepts[13].id | https://openalex.org/C134306372 |
| concepts[13].level | 1 |
| concepts[13].score | 0.11442786455154419 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[13].display_name | Mathematical analysis |
| concepts[14].id | https://openalex.org/C50644808 |
| concepts[14].level | 2 |
| concepts[14].score | 0.07677978277206421 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[14].display_name | Artificial neural network |
| concepts[15].id | https://openalex.org/C202444582 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[15].display_name | Pure mathematics |
| concepts[16].id | https://openalex.org/C114614502 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q76592 |
| concepts[16].display_name | Combinatorics |
| concepts[17].id | https://openalex.org/C159985019 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q181790 |
| concepts[17].display_name | Composite material |
| concepts[18].id | https://openalex.org/C192562407 |
| concepts[18].level | 0 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[18].display_name | Materials science |
| keywords[0].id | https://openalex.org/keywords/toeplitz-matrix |
| keywords[0].score | 0.8899467587471008 |
| keywords[0].display_name | Toeplitz matrix |
| keywords[1].id | https://openalex.org/keywords/circulant-matrix |
| keywords[1].score | 0.635464608669281 |
| keywords[1].display_name | Circulant matrix |
| keywords[2].id | https://openalex.org/keywords/algorithm |
| keywords[2].score | 0.614302933216095 |
| keywords[2].display_name | Algorithm |
| keywords[3].id | https://openalex.org/keywords/convolution |
| keywords[3].score | 0.598578691482544 |
| keywords[3].display_name | Convolution (computer science) |
| keywords[4].id | https://openalex.org/keywords/mathematics |
| keywords[4].score | 0.5434404611587524 |
| keywords[4].display_name | Mathematics |
| keywords[5].id | https://openalex.org/keywords/rank |
| keywords[5].score | 0.5370790958404541 |
| keywords[5].display_name | Rank (graph theory) |
| keywords[6].id | https://openalex.org/keywords/matrix |
| keywords[6].score | 0.523570716381073 |
| keywords[6].display_name | Matrix (chemical analysis) |
| keywords[7].id | https://openalex.org/keywords/low-rank-approximation |
| keywords[7].score | 0.5226502418518066 |
| keywords[7].display_name | Low-rank approximation |
| keywords[8].id | https://openalex.org/keywords/piecewise |
| keywords[8].score | 0.4306890666484833 |
| keywords[8].display_name | Piecewise |
| keywords[9].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[9].score | 0.38657233119010925 |
| keywords[9].display_name | Mathematical optimization |
| keywords[10].id | https://openalex.org/keywords/hankel-matrix |
| keywords[10].score | 0.37292641401290894 |
| keywords[10].display_name | Hankel matrix |
| keywords[11].id | https://openalex.org/keywords/computer-science |
| keywords[11].score | 0.3426007628440857 |
| keywords[11].display_name | Computer science |
| keywords[12].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[12].score | 0.1644965410232544 |
| keywords[12].display_name | Artificial intelligence |
| keywords[13].id | https://openalex.org/keywords/mathematical-analysis |
| keywords[13].score | 0.11442786455154419 |
| keywords[13].display_name | Mathematical analysis |
| keywords[14].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[14].score | 0.07677978277206421 |
| keywords[14].display_name | Artificial neural network |
| language | en |
| locations[0].id | doi:10.1109/tmi.2018.2886290 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S58069681 |
| locations[0].source.issn | 0278-0062, 1558-254X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0278-0062 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IEEE Transactions on Medical Imaging |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| 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 | IEEE Transactions on Medical Imaging |
| locations[0].landing_page_url | https://doi.org/10.1109/tmi.2018.2886290 |
| locations[1].id | pmid:30561342 |
| 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 | IEEE transactions on medical imaging |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/30561342 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:6559879 |
| 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 | IEEE Trans Med Imaging |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/6559879 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5087167168 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-4648-611X |
| authorships[0].author.display_name | Yue Hu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I204983213 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China |
| authorships[0].institutions[0].id | https://openalex.org/I204983213 |
| authorships[0].institutions[0].ror | https://ror.org/01yqg2h08 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I204983213 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Harbin Institute of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yue Hu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China |
| authorships[1].author.id | https://openalex.org/A5100758656 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1154-8122 |
| authorships[1].author.display_name | Xiaohan Liu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I204983213 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China |
| authorships[1].institutions[0].id | https://openalex.org/I204983213 |
| authorships[1].institutions[0].ror | https://ror.org/01yqg2h08 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I204983213 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Harbin Institute of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xiaohan Liu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China |
| authorships[2].author.id | https://openalex.org/A5072299998 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6196-3933 |
| authorships[2].author.display_name | Mathews Jacob |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I126307644 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA |
| authorships[2].institutions[0].id | https://openalex.org/I126307644 |
| authorships[2].institutions[0].ror | https://ror.org/036jqmy94 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I126307644 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of Iowa |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Mathews Jacob |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA |
| 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/6559879 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10500 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2206 |
| primary_topic.subfield.display_name | Computational Mechanics |
| primary_topic.display_name | Sparse and Compressive Sensing Techniques |
| related_works | https://openalex.org/W3177828492, https://openalex.org/W2034415022, https://openalex.org/W2149698301, https://openalex.org/W4388372565, https://openalex.org/W63525287, https://openalex.org/W2286689764, https://openalex.org/W2026406497, https://openalex.org/W4388372654, https://openalex.org/W2105266774, https://openalex.org/W1444802581 |
| cited_by_count | 30 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 3 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 4 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 7 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 7 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 3 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:6559879 |
| 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 | IEEE Trans Med Imaging |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/6559879 |
| primary_location.id | doi:10.1109/tmi.2018.2886290 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S58069681 |
| primary_location.source.issn | 0278-0062, 1558-254X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0278-0062 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IEEE Transactions on Medical Imaging |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| 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 | IEEE Transactions on Medical Imaging |
| primary_location.landing_page_url | https://doi.org/10.1109/tmi.2018.2886290 |
| publication_date | 2018-12-11 |
| publication_year | 2018 |
| referenced_works | https://openalex.org/W2963275005, https://openalex.org/W2440567159, https://openalex.org/W1959070854, https://openalex.org/W2165142794, https://openalex.org/W2196426102, https://openalex.org/W1971056102, https://openalex.org/W1679811722, https://openalex.org/W2158537680, https://openalex.org/W2098662489, https://openalex.org/W2593158722, https://openalex.org/W2601784259, https://openalex.org/W1914211332, https://openalex.org/W1497904071, https://openalex.org/W2168903001, https://openalex.org/W2113030459, https://openalex.org/W2964321202, https://openalex.org/W1972150100, https://openalex.org/W2055834582, https://openalex.org/W2145962650, https://openalex.org/W2116581540, https://openalex.org/W2076833396, https://openalex.org/W2047163846, https://openalex.org/W2101675075, https://openalex.org/W2962903101, https://openalex.org/W2020111712, https://openalex.org/W1758598986, https://openalex.org/W2039939700, https://openalex.org/W2330813655, https://openalex.org/W2082675951, https://openalex.org/W2326045555, https://openalex.org/W2609937952, https://openalex.org/W2479702039, https://openalex.org/W3098518526, https://openalex.org/W2801822645 |
| referenced_works_count | 34 |
| abstract_inverted_index.a | 7, 27, 52, 57, 74, 88, 114, 117 |
| abstract_inverted_index.In | 22, 93 |
| abstract_inverted_index.MR | 140 |
| abstract_inverted_index.We | 78, 129 |
| abstract_inverted_index.an | 4, 16, 68 |
| abstract_inverted_index.as | 48 |
| abstract_inverted_index.in | 73, 133 |
| abstract_inverted_index.is | 46 |
| abstract_inverted_index.of | 2, 51, 64, 83, 125, 136 |
| abstract_inverted_index.on | 121 |
| abstract_inverted_index.or | 11 |
| abstract_inverted_index.to | 19, 32, 86, 95, 101 |
| abstract_inverted_index.we | 25, 112 |
| abstract_inverted_index.The | 44, 61 |
| abstract_inverted_index.and | 56, 100, 116, 138 |
| abstract_inverted_index.has | 14 |
| abstract_inverted_index.our | 131 |
| abstract_inverted_index.the | 49, 80, 84, 97, 103, 108, 122, 126, 134, 147, 155 |
| abstract_inverted_index.each | 65 |
| abstract_inverted_index.fast | 115 |
| abstract_inverted_index.from | 35, 107 |
| abstract_inverted_index.into | 6 |
| abstract_inverted_index.over | 154 |
| abstract_inverted_index.that | 146 |
| abstract_inverted_index.this | 23 |
| abstract_inverted_index.avoid | 102 |
| abstract_inverted_index.based | 120 |
| abstract_inverted_index.image | 5, 45 |
| abstract_inverted_index.order | 94 |
| abstract_inverted_index.solve | 96 |
| abstract_inverted_index.their | 36 |
| abstract_inverted_index.using | 40 |
| abstract_inverted_index.which | 71 |
| abstract_inverted_index.Hankel | 12 |
| abstract_inverted_index.Recent | 0 |
| abstract_inverted_index.become | 15 |
| abstract_inverted_index.demand | 105 |
| abstract_inverted_index.images | 34, 141 |
| abstract_inverted_index.linear | 59 |
| abstract_inverted_index.matrix | 13 |
| abstract_inverted_index.method | 18 |
| abstract_inverted_index.paper, | 24 |
| abstract_inverted_index.single | 137 |
| abstract_inverted_index.theory | 1 |
| abstract_inverted_index.Fourier | 38, 62 |
| abstract_inverted_index.context | 135 |
| abstract_inverted_index.exploit | 79 |
| abstract_inverted_index.images. | 21 |
| abstract_inverted_index.infimal | 41 |
| abstract_inverted_index.mapping | 3 |
| abstract_inverted_index.matrix. | 77, 128 |
| abstract_inverted_index.modeled | 47 |
| abstract_inverted_index.problem | 98 |
| abstract_inverted_index.recover | 33 |
| abstract_inverted_index.restore | 20 |
| abstract_inverted_index.results | 72 |
| abstract_inverted_index.satisfy | 67 |
| abstract_inverted_index.Toeplitz | 10, 76, 110, 127 |
| abstract_inverted_index.combined | 89 |
| abstract_inverted_index.constant | 54 |
| abstract_inverted_index.improved | 151 |
| abstract_inverted_index.indicate | 145 |
| abstract_inverted_index.low-rank | 9, 30, 81 |
| abstract_inverted_index.matrices | 85 |
| abstract_inverted_index.problem. | 92 |
| abstract_inverted_index.property | 82 |
| abstract_inverted_index.proposed | 148 |
| abstract_inverted_index.provides | 150 |
| abstract_inverted_index.recovery | 152 |
| abstract_inverted_index.Numerical | 143 |
| abstract_inverted_index.algorithm | 31, 119, 132, 149 |
| abstract_inverted_index.component | 55, 66 |
| abstract_inverted_index.effective | 17 |
| abstract_inverted_index.formulate | 87 |
| abstract_inverted_index.introduce | 26, 113 |
| abstract_inverted_index.matrices, | 111 |
| abstract_inverted_index.piecewise | 53, 58 |
| abstract_inverted_index.recovery. | 142 |
| abstract_inverted_index.relation, | 70 |
| abstract_inverted_index.resulting | 106 |
| abstract_inverted_index.component. | 60 |
| abstract_inverted_index.structured | 8, 29, 75 |
| abstract_inverted_index.approaches. | 157 |
| abstract_inverted_index.convolution | 42 |
| abstract_inverted_index.demonstrate | 130 |
| abstract_inverted_index.efficiently | 99 |
| abstract_inverted_index.experiments | 144 |
| abstract_inverted_index.generalized | 28 |
| abstract_inverted_index.high-memory | 104 |
| abstract_inverted_index.large-scale | 109 |
| abstract_inverted_index.performance | 153 |
| abstract_inverted_index.regularized | 90 |
| abstract_inverted_index.annihilation | 69 |
| abstract_inverted_index.coefficients | 39, 63 |
| abstract_inverted_index.optimization | 91 |
| abstract_inverted_index.undersampled | 37 |
| abstract_inverted_index.approximation | 124 |
| abstract_inverted_index.multi-channel | 139 |
| abstract_inverted_index.superposition | 50 |
| abstract_inverted_index.half-circulant | 123 |
| abstract_inverted_index.memory-efficient | 118 |
| abstract_inverted_index.regularizations. | 43 |
| abstract_inverted_index.state-of-the-art | 156 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.93561672 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |