FR-Net: Joint Reconstruction and Segmentation in Compressed Sensing Cardiac MRI

Qiaoying Huang, Dong Yang, Jingru Yi, Leon Axel, Dimitris Metaxas

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

5 Scopus citations

Abstract

We provide a novel solution to the inverse problem in medical imaging that takes as input the undersampled k-space data from Magnetic Resonance Imaging (MRI) scans and outputs both the reconstructed images and the segmented myocardium. Previously, the undersampled k-space data is first transformed into a reconstructed MRI image. From this image, the myocardium is contours are subsequently extracted using a segmentation method. However, this sequential approach is not optimal and requires manual intervention. In order to automate and improve the results of these approaches, we propose a new method to solve the reconstruction and segmentation problems simultaneously. Our method is based on a novel deep learning approach we term “Joint-FR-Net”, which consists of a reconstruction module derived from the fast iterative shrinkage-thresholding algorithm (FISTA) and a segmentation module. We test our approach on an undersampled short-axis (SAX) cardiac dataset and show the effectiveness of the Joint FR-Net in both image reconstruction and myocardium joint segmentation.

Original languageEnglish (US)
Title of host publicationFunctional Imaging and Modeling of the Heart - 10th International Conference, FIMH 2019, Proceedings
EditorsValéry Ozenne, Edward Vigmond, Yves Coudière, Nejib Zemzemi
PublisherSpringer Verlag
Pages352-360
Number of pages9
ISBN (Print)9783030219482
DOIs
StatePublished - 2019
Event10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019 - Bordeaux, France
Duration: Jun 6 2019Jun 8 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11504 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2019
CountryFrance
CityBordeaux
Period6/6/196/8/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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