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.
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Posted 22 Mar, 2021
On 17 Apr, 2021
Received 31 Mar, 2021
On 11 Mar, 2021
On 08 Mar, 2021
Invitations sent on 08 Mar, 2021
On 08 Mar, 2021
On 08 Mar, 2021
Posted 22 Mar, 2021
On 17 Apr, 2021
Received 31 Mar, 2021
On 11 Mar, 2021
On 08 Mar, 2021
Invitations sent on 08 Mar, 2021
On 08 Mar, 2021
On 08 Mar, 2021
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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