• Login
    View Item 
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    A multi-scale residual network for accelerated radial MR parameter mapping

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    main_clean_small.pdf
    Size:
    1.301Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
    Download
    Author
    Fu, Zhiyang
    Mandava, Sagar
    Keerthivasan, Mahesh B
    Li, Zhitao
    Johnson, Kevin
    Martin, Diego R
    Altbach, Maria I
    Bilgin, Ali
    Affiliation
    Univ Arizona, Dept Elect & Comp Engn
    Univ Arizona, Dept Med Imaging
    Univ Arizona, Dept Biomed Engn
    Issue Date
    2020-09-01
    Keywords
    Convolutional neural networks
    Deep learning
    image reconstruction
    Multi-contrast imaging
    T(1) mapping
    T(2) mapping
    
    Metadata
    Show full item record
    Publisher
    ELSEVIER SCIENCE INC
    Citation
    Fu, Z., Mandava, S., Keerthivasan, M. B., Li, Z., Johnson, K., Martin, D. R., ... & Bilgin, A. (2020). A multi-scale residual network for accelerated radial MR parameter mapping. Magnetic Resonance Imaging, 73, 152-162.
    Journal
    MAGNETIC RESONANCE IMAGING
    Rights
    Copyright © 2020 Elsevier Inc. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T1 mapping results are obtained on brain and abdomen datasets and in vivo T2 mapping results are obtained on brain and knee datasets. Quantitative results for the T2 mapping of the knee show that MS-ResNet trained using either fully sampled or undersampled data outperforms conventional model-based compressed sensing methods. This is significant because obtaining fully sampled training data is not possible in many applications. in vivo brain and abdomen results for T1 mapping and in vivo brain results for T2 mapping demonstrate that MS-ResNet yields contrast-weighted images and parameter maps that are comparable to those achieved by model-based iterative methods while offering two orders of magnitude reduction in reconstruction times. The proposed approach enables recovery of high-quality contrast-weighted images and parameter maps from highly accelerated radial data acquisitions. The rapid image reconstructions enabled by the proposed approach makes it a good candidate for routine clinical use.
    Note
    12 month embargo; available online 1 September 2020
    ISSN
    0730-725X
    EISSN
    1873-5894
    PubMed ID
    32882339
    DOI
    10.1016/j.mri.2020.08.013
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.mri.2020.08.013
    Scopus Count
    Collections
    UA Faculty Publications

    entitlement

    Related articles

    • High-performance rapid MR parameter mapping using model-based deep adversarial learning.
    • Authors: Liu F, Kijowski R, Feng L, El Fakhri G
    • Issue date: 2020 Dec
    • Magnetic resonance parameter mapping using model-guided self-supervised deep learning.
    • Authors: Liu F, Kijowski R, El Fakhri G, Feng L
    • Issue date: 2021 Jun
    • MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR parameter mapping.
    • Authors: Liu F, Feng L, Kijowski R
    • Issue date: 2019 Jul
    • Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method.
    • Authors: Jun Y, Shin H, Eo T, Kim T, Hwang D
    • Issue date: 2021 May
    • Improving subspace constrained radial fast spin echo MRI using block matching driven non-local low rank regularization.
    • Authors: Mandava S, Keerthivasan MB, Martin DR, Altbach MI, Bilgin A
    • Issue date: 2021 Feb 11
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.