Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks

Enes Albay; Ugur Demir; Gozde Unal

doi:10.1007/978-3-030-00320-3_19

Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks

Enes Albay^*, Ugur Demir, Gozde Unal

^*Corresponding author for this work

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Spatial resolution is one of the main constraints in diffusion Magnetic Resonance Imaging (dMRI). Increasing resolution leads to a decrease in SNR of the diffusion images. Acquiring high resolution images without reducing SNRs requires larger magnetic fields and long scan times which are typically not applicable in the clinical settings. Currently feasible voxel size is around 1 mm³ for a diffusion image. In this paper, we present a deep neural network based post-processing method to increase the spatial resolution in diffusion MRI. We utilize Generative Adversarial Networks (GANs) to obtain a higher resolution diffusion MR image in the spatial dimension from lower resolution diffusion images. The obtained real data results demonstrate a first time proof of concept that GANs can be useful in super-resolution problem of diffusion MRI for upscaling in the spatial dimension.

Original language	English
Title of host publication	PRedictive Intelligence in MEdicine - First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Proceedings
Editors	Islem Rekik, Gozde Unal, Ehsan Adeli, Sang Hyun Park
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	155-163
Number of pages	9
ISBN (Print)	9783030003197
DOIs	https://doi.org/10.1007/978-3-030-00320-3_19
Publication status	Published - 2018
Event	1st International Workshop on PRedictive Intelligence in Medicine, PRIME 2018 Held in Conjunction with MICCAI 2018 - Granada, Spain Duration: 16 Sept 2018 → 16 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11121 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on PRedictive Intelligence in Medicine, PRIME 2018 Held in Conjunction with MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 16/09/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

Diffusion MRI (dMRI)
Generative adversarial networks (GANs)
Magnetic resonance imaging (MRI)
Super resolution

Access to Document

10.1007/978-3-030-00320-3_19

Cite this

Albay, E., Demir, U., & Unal, G. (2018). Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks. 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, Proceedings (pp. 155-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11121 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-00320-3_19

Albay, Enes ; Demir, Ugur ; Unal, Gozde. / Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks. PRedictive Intelligence in MEdicine - First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Islem Rekik ; Gozde Unal ; Ehsan Adeli ; Sang Hyun Park. Springer Science and Business Media Deutschland GmbH, 2018. pp. 155-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Spatial resolution is one of the main constraints in diffusion Magnetic Resonance Imaging (dMRI). Increasing resolution leads to a decrease in SNR of the diffusion images. Acquiring high resolution images without reducing SNRs requires larger magnetic fields and long scan times which are typically not applicable in the clinical settings. Currently feasible voxel size is around 1 mm3 for a diffusion image. In this paper, we present a deep neural network based post-processing method to increase the spatial resolution in diffusion MRI. We utilize Generative Adversarial Networks (GANs) to obtain a higher resolution diffusion MR image in the spatial dimension from lower resolution diffusion images. The obtained real data results demonstrate a first time proof of concept that GANs can be useful in super-resolution problem of diffusion MRI for upscaling in the spatial dimension.",

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author = "Enes Albay and Ugur Demir and Gozde Unal",

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Albay, E, Demir, U & Unal, G 2018, Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks. in I Rekik, G Unal, E Adeli & SH Park (eds), PRedictive Intelligence in MEdicine - First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11121 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 155-163, 1st International Workshop on PRedictive Intelligence in Medicine, PRIME 2018 Held in Conjunction with MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00320-3_19

Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks. / Albay, Enes; Demir, Ugur; Unal, Gozde.
PRedictive Intelligence in MEdicine - First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Islem Rekik; Gozde Unal; Ehsan Adeli; Sang Hyun Park. Springer Science and Business Media Deutschland GmbH, 2018. p. 155-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11121 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks

AU - Albay, Enes

AU - Demir, Ugur

AU - Unal, Gozde

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Spatial resolution is one of the main constraints in diffusion Magnetic Resonance Imaging (dMRI). Increasing resolution leads to a decrease in SNR of the diffusion images. Acquiring high resolution images without reducing SNRs requires larger magnetic fields and long scan times which are typically not applicable in the clinical settings. Currently feasible voxel size is around 1 mm3 for a diffusion image. In this paper, we present a deep neural network based post-processing method to increase the spatial resolution in diffusion MRI. We utilize Generative Adversarial Networks (GANs) to obtain a higher resolution diffusion MR image in the spatial dimension from lower resolution diffusion images. The obtained real data results demonstrate a first time proof of concept that GANs can be useful in super-resolution problem of diffusion MRI for upscaling in the spatial dimension.

AB - Spatial resolution is one of the main constraints in diffusion Magnetic Resonance Imaging (dMRI). Increasing resolution leads to a decrease in SNR of the diffusion images. Acquiring high resolution images without reducing SNRs requires larger magnetic fields and long scan times which are typically not applicable in the clinical settings. Currently feasible voxel size is around 1 mm3 for a diffusion image. In this paper, we present a deep neural network based post-processing method to increase the spatial resolution in diffusion MRI. We utilize Generative Adversarial Networks (GANs) to obtain a higher resolution diffusion MR image in the spatial dimension from lower resolution diffusion images. The obtained real data results demonstrate a first time proof of concept that GANs can be useful in super-resolution problem of diffusion MRI for upscaling in the spatial dimension.

KW - Diffusion MRI (dMRI)

KW - Generative adversarial networks (GANs)

KW - Magnetic resonance imaging (MRI)

KW - Super resolution

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M3 - Conference contribution

AN - SCOPUS:85066140785

SN - 9783030003197

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 155

EP - 163

BT - PRedictive Intelligence in MEdicine - First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Proceedings

A2 - Rekik, Islem

A2 - Unal, Gozde

A2 - Adeli, Ehsan

A2 - Park, Sang Hyun

PB - Springer Science and Business Media Deutschland GmbH

T2 - 1st International Workshop on PRedictive Intelligence in Medicine, PRIME 2018 Held in Conjunction with MICCAI 2018

Y2 - 16 September 2018 through 16 September 2018

ER -

Albay E, Demir U, Unal G. Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks. In Rekik I, Unal G, Adeli E, Park SH, editors, PRedictive Intelligence in MEdicine - First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer Science and Business Media Deutschland GmbH. 2018. p. 155-163. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00320-3_19

Diffusion MRI Spatial Super-Resolution Using Generative Adversarial Networks

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