ArticleArXiv2024
Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.
Amir Heydari, Abbas Ahmadi, Tae Hyung Kim, Berkin Bilgic
Abstract readPreprint
In one paragraphArticle in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 · The registryThe trial behind it
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3 · Its place in the literatureWho cites it
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4 · The recordCorrections and comments
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5 · Who and what moneyAuthors and funding
4 authors.
Amir HeydariDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.
Abbas AhmadiDepartment of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran.
Tae Hyung KimDepartment of Computer Engineering, Hongik University, Seoul, Korea.
Berkin BilgicAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.
Funding
CONNECTOME 2.0: DEVELOPING THE NEXT GENERATION HUMAN MRI SCANNER FOR BRIDGING STUDIES OF THE MICRO-, MESO- AND MACRO-CONNECTOMEU01EB026996 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI BASSER, PETER J., HUANG, SUSIE YI · 2018 to 2022
$14.2MTraining and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Susie Yi Huang · 2020 to 2026
$10.9MMRI technology for restoring brain image quality for pediatric patients with metallic orthodontic hardwareR01EB028797 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Berkin Bilgic, Jason P Stockmann · 2020 to 2026
$3.0MHarmonizing data acquisition, reconstruction, and analysis for reproducible, cross-vendor, open source MRIR01EB032378 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI BILGIC, BERKIN, RATHI, YOGESH · 2022 to 2025
$2.7MNext-generation Upgrade for 7T Human MRI SystemS10OD036263 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI BILGIC, BERKIN · 2024 to 2024
$2.0MLow-dose contrast enhanced fast pediatric brain MRIR01EB034757 · NIBIB · BOSTON CHILDREN'S HOSPITAL · PI Berkin Bilgic, Borjan Gagoski · 2024 to 2026
$1.8MHybrid TMS/MRI system for regionally tailored causal mapping of human cortical circuits and connectivityUG3EB034875 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI BILGIC, BERKIN, HUANG, SUSIE YI · 2023 to 2024
$837kSubmillimeter resolution diffusion MRI of the medial temporal lobe in the earliest stages of Alzheimer's diseaseR21AG082377 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI BILGIC, BERKIN, HUANG, SUSIE YI · 2024 to 2025
$459kRapid Fetal HASTE MR ImagingR03EB031175 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI BILGIC, BERKIN · 2021 to 2022
$188kNIA NIH HHS R21 AG082377NIBIB NIH HHS P41 EB030006NIBIB NIH HHS R01 EB028797NIBIB NIH HHS R01 EB032378NIBIB NIH HHS R01 EB034757NIBIB NIH HHS R03 EB031175NIBIB NIH HHS U01 EB026996NIBIB NIH HHS UG3 EB034875NIH HHS S10 OD036263
6 · The paper itselfAbstract
Quantification of tissue parameters using MRI is emerging as a powerful tool in clinical diagnosis and research studies. The need for multiple long scans with different acquisition parameters prohibits quantitative MRI from reaching widespread adoption in routine clinical and research exams. Accelerated parameter mapping techniques leverage parallel imaging, signal modelling and deep learning to offer more practical quantitative MRI acquisitions. However, the achievable acceleration and the quality of maps are often limited. Joint MAPLE is a recent state-of-the-art multi-parametric and scan-specific parameter mapping technique with promising performance at high acceleration rates. It synergistically combines parallel imaging, model-based and machine learning approaches for joint mapping of
Indexed as
parameter mappingquantitative MRIscan-specific deep learningself-supervised networks
Identifiers
PMID39148933
PMCPMC11326419
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