Convolutional neural network for reconstruction of 7T-like images from 3T MRI using appearance and anatomical features

Khosro Bahrami, Feng Shi, Islem Rekik, Dinggang Shen

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

50 Citations (Scopus)

Abstract

The advanced 7 Tesla (7T) Magnetic Resonance Imaging (MRI) scanners provide images with higher resolution anatomy than 3T MRI scanners, thus facilitating early diagnosis of brain diseases. However, 7T MRI scanners are less accessible, compared to the 3T MRI scanners. This motivates us to reconstruct 7T-like images from 3T MRI. We propose a deep architecture for Convolutional Neural Network (CNN), which uses the appearance (intensity) and anatomical (labels of brain tissues) features as input to non-linearly map 3T MRI to 7T MRI. In the training step, we train the CNN by feeding it with both appearance and anatomical features of the 3T patch. This outputs the intensity of center voxel in the corresponding 7T patch. In the testing step, we apply the trained CNN to map each input 3T patch to the 7T-like image patch. Our performance is evaluated on 15 subjects, each with both 3T and 7T MR images. Both visual and numerical results show that our method outperforms the comparison methods.

Original languageEnglish
Title of host publicationDeep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings
PublisherSpringer Verlag
Pages39-47
Number of pages9
Volume10008 LNCS
ISBN (Print)9783319469751
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 212016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10008 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/2116/10/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Bahrami, K., Shi, F., Rekik, I., & Shen, D. (2016). Convolutional neural network for reconstruction of 7T-like images from 3T MRI using appearance and anatomical features. In Deep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings (Vol. 10008 LNCS, pp. 39-47). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10008 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46976-8_5