{"doi":"10.58530/2022/4692","title":"Fast and Automatic Rank Determination (ARD) via Deep Learning for Low-rank Calibrationless Reconstruction","abstract":"<jats:p>Low-rank matrix completion has emerged as a potent reconstruction approach for calibrationless parallel imaging. However, in all existing low-rank reconstruction methods, the rank threshold must be carefully chosen slice by slice in a manual and trial-and-error manner, severely hindering the adoption of low-rank reconstruction in routine clinical applications. To tackle the problem, we proposed a fast and automatic rank determination via deep learning. It directly determines optimal rank from undersampled k-space data by exploiting coil sensitivity and finite image support.  Our proposed method enables fast, automatic and robust rank determination for all existing calibrationless reconstruction using low-rank matrix completion.</jats:p>","journal":"ISMRM Annual Meeting","year":null,"id":687172,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"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":1795208,"name":"Zheyuan Yi","orcid":null,"position":1,"is_corresponding":false},{"id":504995,"name":"Yujiao Zhao","orcid":"0000-0001-9142-4332","position":2,"is_corresponding":false},{"id":1795209,"name":"Christopher Man","orcid":null,"position":3,"is_corresponding":false},{"id":1740555,"name":"Linfang Xiao","orcid":null,"position":4,"is_corresponding":false},{"id":1795210,"name":"Vick Lau","orcid":null,"position":5,"is_corresponding":false},{"id":233694,"name":"Shi Su","orcid":"0000-0001-6848-2817","position":6,"is_corresponding":false},{"id":1795211,"name":"Ziming Huang","orcid":null,"position":7,"is_corresponding":false},{"id":1369488,"name":"Junhao Zhang","orcid":"0000-0002-5511-9231","position":8,"is_corresponding":false},{"id":1795212,"name":"Alex Leong","orcid":null,"position":9,"is_corresponding":false},{"id":400219,"name":"Fei Chen","orcid":"0000-0002-1576-4543","position":10,"is_corresponding":false},{"id":1740558,"name":"Ed Wu","orcid":null,"position":11,"is_corresponding":false},{"id":485205,"name":"Jiahao Hu","orcid":"0000-0001-5835-1012","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Fast and Automatic Rank Determination (ARD) via Deep Learning for Low-rank Calibrationless Reconstruction","abstract":"Low-rank matrix completion has emerged as a potent reconstruction approach for calibrationless parallel imaging. However, in all existing low-rank reconstruction methods, the rank threshold must be carefully chosen slice by slice in a manual and trial-and-error manner, severely hindering the adoption of low-rank reconstruction in routine clinical applications. To tackle the problem, we proposed a fast and automatic rank determination via deep learning. It directly determines optimal rank from undersampled k-space data by exploiting coil sensitivity and finite image support. Our proposed method enables fast, automatic and robust rank determination for all existing calibrationless reconstruction using low-rank matrix completion.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26207759","pmcid":null,"openalex_id":"https://openalex.org/W4385556248","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.29946299,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://dx.doi.org/10.58530/2022/4692","host_type":"journal"}],"fields_of_study":["Advanced MRI Techniques and Applications","Advanced X-ray Imaging Techniques","Photoacoustic and Ultrasonic Imaging"],"mesh_terms":[],"keywords":["Rank (graph theory)","Low-rank approximation","Computer science","Artificial intelligence","Matrix (chemical analysis)","Iterative reconstruction","Learning to rank","Matrix completion","Sensitivity (control systems)","Algorithm","Pattern recognition (psychology)","Computer vision","Mathematics","Engineering"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-18T21:27:52.691327Z","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":[]}