Dual-domain cascaded regression for synthesizing 7T from 3T MRI

Yongqin Zhang, Jie Zhi Cheng, Lei Xiang, Pew Thian Yap, Dinggang Shen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    19 Citations (Scopus)

    Abstract

    Due to the high cost and low accessibility of 7T magnetic resonance imaging (MRI) scanners, we propose a novel dual-domain cascaded regression framework to synthesize 7T images from the routine 3T images. Our framework is composed of two parallel and interactive multi-stage regression streams, where one stream regresses on spatial domain and the other regresses on frequency domain. These two streams complement each other and enable the learning of complex mappings between 3T and 7T images. We evaluated the proposed framework on a set of 3T and 7T images by leave-one-out cross-validation. Experimental results demonstrate that the proposed framework generates realistic 7T images and achieves better results than state-of-the-art methods.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
    EditorsJulia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi
    PublisherSpringer Verlag
    Pages410-417
    Number of pages8
    ISBN (Print)9783030009274
    DOIs
    Publication statusPublished - 2018
    Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
    Duration: 2018 Sept 162018 Sept 20

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11070 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
    Country/TerritorySpain
    CityGranada
    Period18/9/1618/9/20

    Bibliographical note

    Publisher Copyright:
    © Springer Nature Switzerland AG 2018.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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