A Low-Field MRI Dataset For Spatiotemporal Analysis of Developing Brain

被引:0
|
作者
Sun, Zhexian [1 ,2 ]
Huang, Jian [1 ,2 ]
Ma, Xiaohui [1 ]
Liang, Jiawei [1 ]
Sun, Chensheng [1 ,2 ]
Hu, Lanyin [3 ]
He, Hongjian [3 ,4 ]
Yu, Gang [1 ,2 ]
机构
[1] Zhejiang Univ, Childrens Hosp,Sch Med, Natl Clin Res Ctr Child Hlth, Natl Childrens Reg Med Ctr, Hangzhou 310052, Peoples R China
[2] Sino Finland Joint AI Lab Child Hlth Zhejiang Prov, Hangzhou 310052, Peoples R China
[3] Zhejiang Univ, Sch Phys, Hangzhou 310058, Peoples R China
[4] Zhejiang Univ, State Key Lab Brain Machine Intelligence, Hangzhou 310058, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
SIGNAL-TO-NOISE; OUTCOMES; IMPACT;
D O I
10.1038/s41597-025-04450-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Recently, imaging investigation of brain development has increasingly captured the attention of researchers and clinicians in an attempt to understand the link between the brain and behavioral changes. Although high-field MR imaging of infants is feasible, the necessary customizations have limited its accessibility, affordability, and reproducibility. Low-field MR, as an emerging solution for scrutinizing developing brain, has exhibited its unique advantages in safety, portability, and cost-effectiveness. The presented low-field infant structural MR data aims to manifest the feasibility of using low-field MR image to exam brain structural changes during early life in infants. The dataset comprises 100 T2 weighed MR images from infants with in-plane resolution of similar to 0.85 mm and similar to 6 mm slice thickness. To demonstrate the potential utility, we conducted atlas-based whole brain segmentations and volumetric quantifications to analyze brain development features in first 10 week in postnatal life. This dataset addresses the scarcity of a large, extended-span infant brain dataset that restricts the further tracking of infant brain development trajectories and the development of routine low-field MR imaging pipelines.
引用
收藏
页数:8
相关论文
共 50 条
  • [41] Low-field MRI can be more sensitive than high-field MRI
    Coffey, Aaron M.
    Truong, Milton L.
    Chekmenev, Eduard Y.
    JOURNAL OF MAGNETIC RESONANCE, 2013, 237 : 169 - 174
  • [42] The deep route to low-field MRI with high potential
    Patricia M. Johnson
    Yvonne W. Lui
    Nature, 2023, 623 : 700 - 701
  • [43] MRI in rheumatoid arthritis of the hands: Comparison of high-field and low-field MRI
    Taouli, BA
    Zaim, S
    Lynch, JA
    Peterfy, CG
    Stork, A
    Fan, B
    RADIOLOGY, 2001, 221 : 523 - 523
  • [44] Low-field MRI: A report on the 2022 ISMRM workshop
    Campbell-Washburn, Adrienne E.
    Keenan, Kathryn E.
    Hu, Peng
    Mugler, John P., III
    Nayak, Krishna S.
    Webb, Andrew G.
    Obungoloch, Johnes
    Sheth, Kevin N.
    Hennig, Juergen
    Rosen, Matthew S.
    Salameh, Najat
    Sodickson, Daniel K.
    Stein, Joel M.
    Marques, Jose P.
    Simonetti, Orlando P.
    MAGNETIC RESONANCE IN MEDICINE, 2023, 90 (04) : 1682 - 1694
  • [45] Deep learning for fast low-field MRI acquisitions
    Reina Ayde
    Tobias Senft
    Najat Salameh
    Mathieu Sarracanie
    Scientific Reports, 12
  • [46] MRI of the equine digit with a dedicated low-field magnet
    Choquet, P
    Sick, H
    Constantinesco, A
    VETERINARY RECORD, 2000, 146 (21) : 616 - 617
  • [47] Nexus: A versatile console for advanced low-field MRI
    Schote, David
    Silemek, Berk
    O'Reilly, Thomas
    Seifert, Frank
    Assmy, Jan-Lukas
    Kolbitsch, Christoph
    Webb, Andrew G.
    Winter, Lukas
    MAGNETIC RESONANCE IN MEDICINE, 2025, 93 (05) : 2224 - 2238
  • [48] Deep learning for fast low-field MRI acquisitions
    Ayde, Reina
    Senft, Tobias
    Salameh, Najat
    Sarracanie, Mathieu
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [49] Repeatability of image quality in very low-field MRI
    Poojar, Pavan
    Oiye, Ivan Etoku
    Aggarwal, Kunal
    Jimeno, Marina Manso
    Vaughan, John Thomas
    Geethanath, Sairam
    NMR IN BIOMEDICINE, 2024, 37 (10)
  • [50] Low-Field Designs for Interior MRI and CT Coupling
    Gjesteby, Lars
    Getzin, Matthew
    Wang, Ge
    2015 41ST ANNUAL NORTHEAST BIOMEDICAL ENGINEERING CONFERENCE (NEBEC), 2015,