{"doi":"10.1002/mrm.30142","title":"A pharmacokinetic model for hyperpolarized <scp><sup>13</sup>C</scp>‐pyruvate <scp>MRI</scp> when using metabolite‐specific <scp>bSSFP</scp> sequences","abstract":"Abstract Purpose Metabolite‐specific balanced SSFP (MS‐bSSFP) sequences are increasingly used in hyperpolarized [1‐ 13 C]Pyruvate (HP 13 C) MRI studies as they improve SNR by refocusing the magnetization each TR. Currently, pharmacokinetic models used to fit conversion rate constants, k PL and k PB , and rate constant maps do not account for differences in the signal evolution of MS‐bSSFP acquisitions. Methods In this work, a flexible MS‐bSSFP model was built that can be used to fit conversion rate constants for these experiments. The model was validated in vivo using paired animal (healthy rat kidneys n = 8, transgenic adenocarcinoma of the mouse prostate n = 3) and human renal cell carcinoma ( n = 3) datasets. Gradient echo (GRE) acquisitions were used with a previous GRE model to compare to the results of the proposed GRE‐bSSFP model. Results Within simulations, the proposed GRE‐bSSFP model fits the simulated data well, whereas a GRE model shows bias because of model mismatch. For the in vivo datasets, the estimated conversion rate constants using the proposed GRE‐bSSFP model are consistent with a previous GRE model. Jointly fitting the lactate T 2 with k PL resulted in less precise k PL estimates. Conclusion The proposed GRE‐bSSFP model provides a method to estimate conversion rate constants, k PL and k PB , for MS‐bSSFP HP 13 C experiments. This model may also be modified and used for other applications, for example, estimating rate constants with other hyperpolarized reagents or multi‐echo bSSFP.","journal":"Magnetic Resonance in Medicine","year":2024,"id":475770,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9499,"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":1312727,"name":"Marie Frederikke Garnæs","orcid":"0009-0008-2750-2517","position":1,"is_corresponding":false},{"id":1313099,"name":"Anna Bennett","orcid":null,"position":2,"is_corresponding":false},{"id":387685,"name":"Nicholas Dwork","orcid":"0000-0002-9838-4772","position":3,"is_corresponding":false},{"id":422376,"name":"Shuyu Tang","orcid":"0000-0002-9911-482X","position":4,"is_corresponding":false},{"id":884982,"name":"Xiaoxi Liu","orcid":"0000-0003-0657-8922","position":5,"is_corresponding":false},{"id":718920,"name":"Manushka Vaidya","orcid":"0000-0002-1473-2590","position":6,"is_corresponding":false},{"id":264063,"name":"Zhen J. Wang","orcid":"0000-0002-2065-5296","position":7,"is_corresponding":false},{"id":313995,"name":"Peder E. Z. Larson","orcid":"0000-0003-4183-3634","position":8,"is_corresponding":false},{"id":1097936,"name":"Sule Sahin","orcid":"0000-0002-7006-4793","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T02:06:21.071690Z","pmid":"38775035","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":[]}