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Technical advance
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Purpose
To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to T 1 , T 2 * , NAWM, and GM- probability maps.
Methods
We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected T 1 and T 2 * maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps.
Results
WM lesions were predicted with a dice coefficient of 0.61±0.09 and a lesion detection rate of 0.85±0.25 for a threshold of 33%.
The network jointly enabled accurate T 1 and T 2 * times with relative deviations of 5.2% and 5.1% and average dice coefficients of 0.92±0.04 and 0.91±0.03 for NAWM and GM after binarizing with a threshold of 80%.
Conclusion
DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.
Keywords
deep learning reconstruction, magnetic resonance fingerprinting, lesion prediction, T1 Mapping, T2 ∗ Mapping
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CURRENT STATUS: Published
View the published article at BMC Medical Imaging.
Version 1
Posted 22 Mar, 2021
No community comments so far
Review #2 received
Received 29 Apr, 2021
Reviewer #2 agreed
On 17 Apr, 2021
Review #1 received
Received 31 Mar, 2021
Reviewer #1 agreed
On 11 Mar, 2021
Editor assigned
On 08 Mar, 2021
Reviewers invited
Invitations sent on 08 Mar, 2021
Submission checks complete
On 08 Mar, 2021
Editor invited
On 08 Mar, 2021
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Subject Areas
Nuclear Medicine & Medical Imaging
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See the published version of this preprint at BMC Medical Imaging.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Technical advance
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Imaging.
Version 1
Posted 22 Mar, 2021
No community comments so far
Review #2 received
Received 29 Apr, 2021
Reviewer #2 agreed
On 17 Apr, 2021
Review #1 received
Received 31 Mar, 2021
Reviewer #1 agreed
On 11 Mar, 2021
Editor assigned
On 08 Mar, 2021
Reviewers invited
Invitations sent on 08 Mar, 2021
Submission checks complete
On 08 Mar, 2021
Editor invited
On 08 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Nuclear Medicine & Medical Imaging
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Imaging.
Purpose
To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to T 1 , T 2 * , NAWM, and GM- probability maps.
Methods
We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected T 1 and T 2 * maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps.
Results
WM lesions were predicted with a dice coefficient of 0.61±0.09 and a lesion detection rate of 0.85±0.25 for a threshold of 33%.
The network jointly enabled accurate T 1 and T 2 * times with relative deviations of 5.2% and 5.1% and average dice coefficients of 0.92±0.04 and 0.91±0.03 for NAWM and GM after binarizing with a threshold of 80%.
Conclusion
DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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