{"doi":"10.1109/tbme.2024.3358223","title":"High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models","abstract":"OBJECTIVE: To develop a new method that integrates subspace and generative image models for high-dimensional MR image reconstruction. METHODS: We proposed a formulation that synergizes a low-dimensional subspace model of high-dimensional images, an adaptive generative image prior serving as spatial constraints on the sequence of \"contrast-weighted\" images or spatial coefficients of the subspace model, and a conventional sparsity regularization. A special pretraining plus subject-specific network adaptation strategy was proposed to construct an accurate generative-network-based representation for images with varying contrasts. An iterative algorithm was introduced to jointly update the subspace coefficients and the multi-resolution latent space of the generative image model that leveraged an recently proposed intermediate layer optimization technique for network inversion. RESULTS: We evaluated the utility of the proposed method for two high-dimensional imaging applications: accelerated MR parameter mapping and high-resolution MR spectroscopic imaging. Improved performance over state-of-the-art subspace-based methods was demonstrated in both cases. CONCLUSION: The proposed method provided a new way to address high-dimensional MR image reconstruction problems by incorporating an adaptive generative model as a data-driven spatial prior for constraining subspace reconstruction. SIGNIFICANCE: Our work demonstrated the potential of integrating data-driven and adaptive generative priors with canonical low-dimensional modeling for high-dimensional imaging problems.","journal":"IEEE Transactions on Biomedical Engineering","year":2024,"id":463519,"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":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9564,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":729546,"name":"Xi Peng","orcid":"0000-0003-1898-9964","position":1,"is_corresponding":false},{"id":713453,"name":"Varun A. Kelkar","orcid":null,"position":2,"is_corresponding":false},{"id":298431,"name":"Mark A. Anastasio","orcid":"0000-0002-3192-4172","position":3,"is_corresponding":false},{"id":379449,"name":"Fan Lam","orcid":"0000-0002-4124-0663","position":4,"is_corresponding":false},{"id":1187886,"name":"Ruiyang Zhao","orcid":"0009-0007-4048-6227","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-19T02:04:33.257852Z","pmid":"38265912","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":[]}