XmoNet: a Fully Convolutional Network for Cross-Modality MR Image Inference

Sophia Bano (Lead / Corresponding author), Muhammad Asad, Ahmed Fetit, Islem Rekik

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Abstract

Magnetic resonance imaging (MRI) can generate multimodal scans with complementary contrast information, capturing various anatomical or functional properties of organs of interest. But whilst the acquisition of multiple modalities is favourable in clinical and research settings, it is hindered by a range of practical factors that include cost and imaging artefacts.We propose XmoNet, a deep-learning architecture based on fully convolutional networks (FCNs) that enables cross-modality MR image inference. This multiple branch architecture operates on various levels of image spatial resolutions, encoding rich feature hierarchies suited for this image generation task. We illustrate the utility of XmoNet in learning the mapping between heterogeneous T1- and T2-weighted MRI scans for accurate and realistic image synthesis in a preliminary analysis. Our ndings support scaling the work to include larger samples and additional modalities.
Original languageEnglish
Title of host publicationPRedictive Intelligence in MEdicine
Subtitle of host publicationFirst International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings
EditorsIslem Rekik, Gozde Unal, Ehsan Adeli, Sang Hyun Park
PublisherSpringer International Publishing
Number of pages9
ISBN (Electronic)9783030003203
ISBN (Print)9783030003197
Publication statusPublished - 2018
EventFirst International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018 - Granada, Spain
Duration: 16 Sep 201820 Sep 2018
https://www.miccai2018.org/en/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743

Conference

ConferenceFirst International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018
CountrySpain
CityGranada
Period16/09/1820/09/18
Internet address

Keywords

  • fully convolutional networks
  • MRI
  • multimodal
  • image generation

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    Bano, S., Asad, M., Fetit, A., & Rekik, I. (2018). XmoNet: a Fully Convolutional Network for Cross-Modality MR Image Inference. In I. Rekik, G. Unal, E. Adeli, & S. H. Park (Eds.), PRedictive Intelligence in MEdicine: First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings (Lecture Notes in Computer Science). Springer International Publishing. https://www.springerprofessional.de/en/xmonet-a-fully-convolutional-network-for-cross-modality-mr-image/16118204