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Exploring sequence transformation in magnetic resonance imaging via deep learning using data from a single asymptomatic patient

Exploring sequence transformation in magnetic resonance imaging via deep learning using data from a single asymptomatic patient
Exploring sequence transformation in magnetic resonance imaging via deep learning using data from a single asymptomatic patient

We investigate the potential for deep learning to create a transfer function from T1 to T2 magnetic resonance imaging sequences using data collected from an asymptomatic patient. Neural networks were trained on images of a human left hand, and then applied to convert T1 images to T2 images for the associated right hand. Analysis showed that the most accurate neural network considered the features in the surrounding ∼1 cm when converting to T2, hence indicating that the neural network was able to identify structural correlations between the sequences. However, some small features measuring <2 mm differed, and grid patterning was evident from the images. While using deep learning for sequence transformations could enable faster processing and diagnosis and in turn reduce patient waiting times, additional work, such as synergising physics-based modelling with neural networks, will likely be required to demonstrate that deep learning can be used to accurately create T2 characteristics from T1 images. In addition, since the present work was conducted using data collected from a single patient, further example datasets collected from patients with a range of different pathologies will be required in order to validate the proposed method.

Deep learning, MRI, Medical imaging, Optics
Grant-Jacob, James
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Everitt, Chris
bd3e1a25-c4d9-4aba-a2f3-15a31ade2f63
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
King, Leonard
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Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Everitt, Chris
bd3e1a25-c4d9-4aba-a2f3-15a31ade2f63
Eason, R.W.
e38684c3-d18c-41b9-a4aa-def67283b020
King, Leonard
48c2d067-d98b-42a6-9014-b7284815aef8
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

Grant-Jacob, James, Everitt, Chris, Eason, R.W., King, Leonard and Mills, Benjamin (2021) Exploring sequence transformation in magnetic resonance imaging via deep learning using data from a single asymptomatic patient. Journal of Physics Communications, 5 (9), [095015]. (doi:10.1088/2399-6528/AC24D8).

Record type: Article

Abstract

We investigate the potential for deep learning to create a transfer function from T1 to T2 magnetic resonance imaging sequences using data collected from an asymptomatic patient. Neural networks were trained on images of a human left hand, and then applied to convert T1 images to T2 images for the associated right hand. Analysis showed that the most accurate neural network considered the features in the surrounding ∼1 cm when converting to T2, hence indicating that the neural network was able to identify structural correlations between the sequences. However, some small features measuring <2 mm differed, and grid patterning was evident from the images. While using deep learning for sequence transformations could enable faster processing and diagnosis and in turn reduce patient waiting times, additional work, such as synergising physics-based modelling with neural networks, will likely be required to demonstrate that deep learning can be used to accurately create T2 characteristics from T1 images. In addition, since the present work was conducted using data collected from a single patient, further example datasets collected from patients with a range of different pathologies will be required in order to validate the proposed method.

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Grant-Jacob_2021_J._Phys._Commun._5_095015 - Version of Record
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More information

Accepted/In Press date: 8 September 2021
Published date: 27 September 2021
Additional Information: Funding Information: BM was supported by an EPSRC Early Career Fellowship (EP/N03368X/1) and EPSRC grant (EP/T026197/1).
Keywords: Deep learning, MRI, Medical imaging, Optics

Identifiers

Local EPrints ID: 451600
URI: http://eprints.soton.ac.uk/id/eprint/451600
PURE UUID: e01cbc40-f69c-4982-a86f-b83ede057b4f
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for R.W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 13 Oct 2021 16:30
Last modified: 28 Apr 2022 02:04

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Contributors

Author: Chris Everitt
Author: R.W. Eason ORCID iD
Author: Leonard King
Author: Benjamin Mills ORCID iD

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