{"doi":"10.1109/msp.2023.3236483","title":"Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging: Performance improvements through integration with deep neural networks","abstract":null,"journal":"IEEE Signal Processing Magazine","year":2023,"id":647288,"datarank":0.42498200160843247,"base_score":2.833213344056216,"endowment":2.833213344056216,"self_citation_contribution":0.42498200160843247,"citation_network_contribution":0.0,"self_endowment_contribution":0.42498200160843247,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":16,"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":254060,"name":"Jing Cheng","orcid":"0000-0002-5379-2203","position":1,"is_corresponding":false},{"id":1686315,"name":"Zhuo-Xu Cui","orcid":null,"position":2,"is_corresponding":false},{"id":1686316,"name":"Qingyong Zhu","orcid":null,"position":3,"is_corresponding":false},{"id":364873,"name":"Leslie Ying","orcid":"0000-0001-9801-1362","position":4,"is_corresponding":false},{"id":1354204,"name":"Dong Liang","orcid":"0000-0002-3166-4235","position":5,"is_corresponding":false},{"id":482669,"name":"Yanjie Zhu","orcid":"0000-0002-9131-689X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging: Performance improvements through integration with deep neural networks","abstract":"Quantitative magnetic resonance imaging (qMRI) aims to obtain quantitative biophysical parameters based on physical models derived from MR spin magnetization evolution. This requires the acquisition of multiple MR images, resulting in very long scan times. Recently, deep learning (DL) has shown significant potential for reconstructing undersampled MR data. When applying DL to fast qMRI, physical priors can be integrated into the DL framework to improve the performance of qMRI further. This article introduces the physical models of qMRI and four ways to integrate these models into a deep neural network (NN), namely, training sample generation, contrast image prediction, loss function design, and network architecture design. The core concepts and methods for each category are presented. The associated signal processing issues regarding the generalization and reliability of physics-driven DL-based fast qMRI methods are also discussed.","is_dataset_classified":null,"base_score":2.833213344056216,"endowment":2.833213344056216,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4322490886","authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2020YFA0712200","title":null},{"funder_name":"National Key Research and Development Program of China","grant_id":"2021YFF0501503","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"12226008","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"81971611","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62106252","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62125111","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"12026603","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"62206273","title":null},{"funder_name":"National Natural Science Foundation of China","grant_id":"U21A6005","title":null},{"funder_name":"Shenzhen Science and Technology Program","grant_id":"RCYX20210609104444089","title":null}],"total_grants":10,"fwci":2.5036,"citation_percentile":0.89457218,"influential_citations":0,"citation_trend":[{"year":2023,"count":5},{"year":2024,"count":5},{"year":2025,"count":5},{"year":2026,"count":1}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/79/10054188/10054204.pdf?arnumber=10054204","host_type":"publisher"},{"url":"https://doi.org/10.1109/msp.2023.3236483","host_type":"journal"}],"fields_of_study":["Advanced MRI Techniques and Applications","Medical Imaging Techniques and Applications","Advanced Neuroimaging Techniques and Applications"],"mesh_terms":[],"keywords":["Computer science","Artificial neural network","Generalization","Deep learning","Artificial intelligence","Reliability (semiconductor)","Physics","Mathematics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Industry, innovation and infrastructure"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T17:45:35.097155Z","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":[]}