Lesion probability mapping in MS patients using a regression network on MR fingerprinting
Ingo Hermann (TU Delft - ImPhys/Computational Imaging, University Heidelberg)
Alena K. Golla (University Heidelberg)
Eloy Martínez-Heras (Universitat Politecnica de Catalunya)
Ralf Schmidt (University Heidelberg)
Elisabeth Solana (Universitat Politecnica de Catalunya)
Sara Llufriu (University Heidelberg)
Achim Gass (University Heidelberg)
Lothar R. Schad (University Heidelberg)
Frank G. Zöllner (University Heidelberg)
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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 T1, T2∗, 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 T1 and T2∗ 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 T1 and T2∗ 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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