{"doi":"10.1101/2020.10.05.327395","title":"In vivo human whole-brain Connectom diffusion MRI dataset at 760 μm isotropic resolution","abstract":"Abstract We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy subject. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T 1 -weighted and T 2 -weighted images at submillimeter scale along with field maps are also made available.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":121756,"datarank":0.3730467634192446,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.08116024106094756,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.08116024106094756,"corpus_percentile":51.86818287305639,"corpus_rank":6223,"citation_count":6,"citer_count":3,"citers_with_citation_signal":3,"citers_with_endowment":3,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.935,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":561870,"name":"Zijing Dong","orcid":"0000-0001-9334-0968","position":1,"is_corresponding":false},{"id":251970,"name":"Qiyuan Tian","orcid":"0000-0002-8350-5295","position":2,"is_corresponding":false},{"id":263265,"name":"Congyu Liao","orcid":"0000-0003-2270-276X","position":3,"is_corresponding":false},{"id":263264,"name":"Qiuyun Fan","orcid":"0000-0001-9053-6279","position":4,"is_corresponding":false},{"id":388280,"name":"W. Scott Hoge","orcid":"0000-0003-0944-2350","position":5,"is_corresponding":false},{"id":492626,"name":"Boris Keil","orcid":"0000-0003-0805-8330","position":6,"is_corresponding":false},{"id":30837,"name":"Jon̈athan R. Polimeni","orcid":"0000-0002-1348-1179","position":7,"is_corresponding":false},{"id":241418,"name":"Lawrence L. Wald","orcid":"0000-0001-8278-6307","position":8,"is_corresponding":false},{"id":263270,"name":"Susie Y. Huang","orcid":"0000-0003-2950-7254","position":9,"is_corresponding":false},{"id":263269,"name":"Kawin Setsompop","orcid":"0000-0003-0455-7634","position":10,"is_corresponding":false},{"id":561869,"name":"Fuyixue Wang","orcid":"0000-0001-8975-2775","position":0,"is_corresponding":true}],"reference_count":72,"raw_metadata":null,"created_at":"2026-07-18T23:14:46.979435Z","pmid":null,"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":[]}