{"doi":"10.1002/mrm.30214","title":"Efficient standardization of clinical T<sub>2</sub>‐weighted images: Phase‐conjugacy e‐CAMP with projected gradient descent","abstract":"Abstract Purpose To standardize T‐weighted images from clinical Turbo Spin Echo (TSE) scans by generating corresponding T maps with the goal of removing scanner‐ and/or protocol‐specific heterogeneity. Methods The T map is estimated by minimizing an objective function containing a data fidelity term in a Virtual Conjugate Coils (VCC) framework, where the signal evolution model is expressed as a linear constraint. The objective function is minimized by Projected Gradient Descent (PGD). Results The algorithm achieves accuracy comparable to methods with customized sampling schemes for accelerated T mapping. The results are insensitive to the tunable parameters, and the relaxed background phase prior produces better T maps compared to the strict real‐value enforcement. It is worth noting that the algorithm works well with challenging Tw‐TSE data using typical clinical parameters. The observed normalized root mean square error ranges from 6.8% to 12.3% over grey and white matter, a clinically common level of quantitative map error. Conclusion The novel methodological development creates an efficient algorithm that allows for T map generated from TSE data with typical clinical parameters, such as high resolution, long echo train length, and low echo spacing. Reconstruction of T maps from TSE data with typical clinical parameters has not been previously reported.","journal":"Magnetic Resonance in Medicine","year":2024,"id":500737,"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":0.9501,"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":297386,"name":"Nahla Elsaid","orcid":"0000-0002-8832-3087","position":1,"is_corresponding":false},{"id":937066,"name":"Heng Sun","orcid":"0000-0001-9091-4591","position":2,"is_corresponding":false},{"id":327210,"name":"Hemant D. Tagare","orcid":"0000-0001-9421-0612","position":3,"is_corresponding":false},{"id":975905,"name":"Gigi Galiana","orcid":"0000-0001-5974-8708","position":4,"is_corresponding":false},{"id":1305195,"name":"Zhehong Zhang","orcid":"0009-0001-1416-2380","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-07-19T02:10:08.435215Z","pmid":"38988054","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":[]}