Lesion probability mapping in MS patients using a regression network on MR fingerprinting

Abstract

Abstract Background 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 1 , $${T_2}^*$$ 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 $$T_1$$ T 1 and $${T_2}^*$$ T 2 ∗ maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps. Results WM lesions were predicted with a dice coefficient of $$0.61\pm 0.09$$ 0.61 ± 0.09 and a lesion detection rate of $$0.85\pm 0.25$$ 0.85 ± 0.25 for a threshold of 33%. The network jointly enabled accurate $$T_1$$ T 1 and $${T_2}^*$$ T 2 ∗ times with relative deviations of 5.2% and 5.1% and average dice coefficients of $$0.92\pm 0.04$$ 0.92 ± 0.04 and $$0.91\pm 0.03$$ 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
• $$T_1$$ T 1 Mapping
• $${T_2}^*$$ T 2 ∗ Mapping