{"doi":"10.1109/msp.2022.3215288","title":"Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging","abstract":"Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these. We then focus on physics-driven deep learning approaches, covering physics-driven loss functions, plug-and-play methods, generative models, and unrolled networks. We highlight domain-specific challenges such as real- and complex-valued building blocks of neural networks, and translational applications in MRI with linear and non-linear forward models. Finally, we discuss common issues and open challenges, and draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline.","journal":"IEEE Signal Processing Magazine","year":2023,"id":316210,"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":111,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9581,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":59493,"name":"Thomas Küstner","orcid":"0000-0002-0353-4898","position":1,"is_corresponding":false},{"id":813571,"name":"Burhaneddin Yaman","orcid":"0000-0003-0791-5900","position":2,"is_corresponding":false},{"id":619834,"name":"Zhengnan Huang","orcid":null,"position":3,"is_corresponding":false},{"id":50812,"name":"Daniel Rueckert","orcid":"0000-0002-5683-5889","position":4,"is_corresponding":false},{"id":230009,"name":"Florian Knöll","orcid":"0000-0001-5357-8656","position":5,"is_corresponding":false},{"id":256600,"name":"Mehmet Akçakaya","orcid":"0000-0001-6400-7736","position":6,"is_corresponding":false},{"id":371095,"name":"Kerstin Hammernik","orcid":"0000-0002-2734-1409","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T01:06:38.213358Z","pmid":"37304755","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":[]}