{"doi":"10.1109/tmi.2026.3686724","title":"Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction","abstract":"Deep learning (DL) methods can reconstruct highly accelerated magnetic resonance imaging (MRI) scans, but they rely on application-specific large training datasets and often generalize poorly to out-of-distribution data. Self-supervised deep learning algorithms perform scan-specific reconstructions, but still require complicated hyperparameter tuning based on the acquisition and often offer limited acceleration. This work develops a bilevel-optimized implicit neural representation (INR) approach for scan-specific MRI reconstruction. The method explicitly formulates the undersampled MRI reconstruction problem as a bilevel optimization problem and automatically optimizes the multidimensional hyperparameters of the reconstruction method for a given acquisition protocol, enabling a tailored reconstruction without training data. The proposed algorithm uses Gaussian process regression to optimize INR hyperparameters, accommodating various acquisitions. The INR includes a trainable positional encoder for high-dimensional feature embedding and a small multilayer perceptron for decoding. The bilevel optimization is computationally efficient, requiring only a few minutes per typical 2D Cartesian scan. On the scanner hardware, the subsequent scan-specific reconstruction-using offline-optimized hyperparameters-is completed within seconds, while achieving comparable or improved image quality compared to previous model-based and self-supervised learning methods.","journal":"IEEE Transactions on Medical Imaging","year":2025,"id":542305,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.948,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":431768,"name":"Jeffrey A. Fessler","orcid":"0000-0001-9998-3315","position":1,"is_corresponding":false},{"id":1431401,"name":"Yun Jiang","orcid":"0000-0001-7768-5237","position":2,"is_corresponding":false},{"id":1431400,"name":"H.H. Yu","orcid":"0009-0003-1508-063X","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:52:55.809501Z","pmid":"42019070","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":[]}