{"doi":"10.1016/j.mri.2015.10.009","title":"Fast reconstruction of highly undersampled MR images using one and two dimensional principal component analysis","abstract":null,"journal":"Magnetic Resonance Imaging","year":2016,"id":605444,"datarank":0.32958368660043297,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.0,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1553832,"name":"Marcel Nogueira d’Eurydice","orcid":null,"position":1,"is_corresponding":false},{"id":1553834,"name":"Petrik Galvosas","orcid":"0000-0001-8660-4005","position":2,"is_corresponding":false},{"id":1553830,"name":"Fangrong Zong","orcid":"0000-0002-2299-2069","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Fast reconstruction of highly undersampled MR images using one and two dimensional principal component analysis","abstract":"Recent compressed sensing techniques allow signal acquisition with less sampling than required by the Nyquist-Shannon theorem which reduces the data acquisition time in magnetic resonance imaging (MRI). However, prior knowledge becomes essential to reconstruct detailed features when the sampling rate is exceedingly low. In this work, one compressed sensing scheme developed in wireless sensing networks was adapted for the purpose of reconstructing magnetic resonance images by using one-dimensional principal component analysis (1D-PCA). Moreover, another related reconstruction method was proposed based on two-dimensional principal component analysis (2D-PCA). When comparing with one wavelet compressed sensing method, we demonstrate that these techniques are feasible and efficient at high undersampling rates.","is_dataset_classified":null,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26514390","pmcid":null,"openalex_id":"https://openalex.org/W2218273114","authors":[],"funders":[{"funder_name":"NIA NIH HHS","grant_id":"P01 AG03991","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"R01 MH56584","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"P50 MH071616","title":null},{"funder_name":"NIA NIH HHS","grant_id":"P50 AG05681","title":null},{"funder_name":"NIA NIH HHS","grant_id":"R01 AG021910","title":null},{"funder_name":"NCRR NIH HHS","grant_id":"U24 RR021382","title":null}],"total_grants":6,"fwci":0.9788,"citation_percentile":0.76626152,"influential_citations":0,"citation_trend":[{"year":2016,"count":2},{"year":2017,"count":1},{"year":2018,"count":1},{"year":2021,"count":2},{"year":2023,"count":1},{"year":2024,"count":1}],"oa_status":"closed","license":"https://www.elsevier.com/tdm/userlicense/1.0/","oa_locations":[{"url":"https://api.elsevier.com/content/article/PII:S0730725X15002465?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0730725X15002465?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.mri.2015.10.009","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/26514390","host_type":"repository"}],"fields_of_study":["Advanced MRI Techniques and Applications","Sparse and Compressive Sensing Techniques","Photoacoustic and Ultrasonic Imaging","Algorithms","Artifacts","Computer Simulation","Data Compression","Image Enhancement","Image Interpretation, Computer-Assisted","Magnetic Resonance Imaging","Models, Statistical","Phantoms, Imaging","Principal Component Analysis","Reproducibility of Results","Sample Size","Sensitivity and Specificity","Signal Processing, Computer-Assisted"],"mesh_terms":["Algorithms","Computer Simulation","Image Enhancement","Image Interpretation, Computer-Assisted","Magnetic Resonance Imaging","Sensitivity and Specificity","Signal Processing, Computer-Assisted","Reproducibility of Results","Models, Statistical","Artifacts","Sample Size","Phantoms, Imaging","Principal Component Analysis","Data Compression"],"keywords":["Undersampling","Compressed sensing","Principal component analysis","Computer science","Nyquist–Shannon sampling theorem","Artificial intelligence","Sampling (signal processing)","Wavelet","Nyquist rate","Pattern recognition (psychology)","Real-time MRI","Iterative reconstruction","Signal reconstruction","Computer vision","Magnetic resonance imaging","Signal processing","Telecommunications","Recognition","MRI","Fast Imaging"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-30T02:27:01.104812Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}