{"doi":"10.1111/jon.70005","title":"High‐Field‐Blinded Assessment of Portable Ultra‐Low‐Field Brain MRI for Multiple Sclerosis","abstract":"BACKGROUND AND PURPOSE: MRI is crucial for multiple sclerosis (MS), but the relative value of portable ultra-low field MRI (pULF-MRI), a technology that holds promise for extending access to MRI, is unknown. We assessed white matter lesion (WML) detection on pULF-MRI compared to high-field MRI (HF-MRI), focusing on blinded assessments, assessor self-training, and multiplanar acquisitions. METHODS: Fifty-five adults with MS underwent pULF-MRI following their HF-MRI. Two neuroradiologists independently assessed pULF-MRI images in an evaluation process, including initial assessment blinded to HF-MRI, self-training with reference to HF-MRI and evaluation of 20 cases with additional T2-fluid-attenuated inversion recovery in an additional plane. A third rater conducted cross-referenced analysis with HF-MRI data to determine true-positive lesions, false-positive areas, and case-level sensitivity and positive predictive value. RESULTS: The mean age of participants was 50 years (standard deviation: 11; 74% women). Initially, Rater 2 marked more false-positive areas than Rater 1 (p = 0.003). After self-training, both raters embraced a conservative approach, with Rater 2 marking fewer false-positive areas (p = 0.01). Both raters maintained 100% case-level sensitivity and positive predictive value for detecting at least one WML, particularly in periventricular areas. Multiplanar acquisitions reduced both false-positive areas and true-positive lesions. True-positive lesions and false-positive areas had similar contrast-to-noise ratios in the juxtacortical region (p = 0.73) but not in periventricular, deep parenchymal regions (p = 0.004, p = 0.01). CONCLUSION: With adequate training, radiological interpretation of pULF-MRI has high sensitivity and positive predictive value for MS lesions but should be approached conservatively. These results suggest utility for patient triage, potentially reducing diagnostic delay, and screening high-risk individuals.","journal":"Journal of Neuroimaging","year":2025,"id":529871,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6883,"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":247664,"name":"Govind Nair","orcid":"0000-0003-3725-615X","position":1,"is_corresponding":false},{"id":861032,"name":"Karan D. Kawatra","orcid":null,"position":2,"is_corresponding":false},{"id":1385301,"name":"Ashley A. Thommana","orcid":null,"position":3,"is_corresponding":false},{"id":999069,"name":"Corinne Donnay","orcid":null,"position":4,"is_corresponding":false},{"id":998676,"name":"María I. Gaitán","orcid":"0000-0003-4455-5641","position":5,"is_corresponding":false},{"id":240420,"name":"Joel M. Stein","orcid":"0000-0002-0741-1780","position":6,"is_corresponding":false},{"id":230800,"name":"Daniel S. Reich","orcid":"0000-0002-2628-4334","position":7,"is_corresponding":false},{"id":845983,"name":"Serhat V. Okar","orcid":"0000-0003-3716-2196","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-19T02:51:01.235017Z","pmid":"39815369","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":[]}