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000153781 037__ $$aDKFZ-2020-00447
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000153781 1001_ $$aPolak, Daniel$$b0
000153781 245__ $$aNonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM).
000153781 260__ $$aNew York, NY$$bWiley$$c2020
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000153781 520__ $$aHigh-quality Quantitative Susceptibility Mapping (QSM) with Nonlinear Dipole Inversion (NDI) is developed with pre-determined regularization while matching the image quality of state-of-the-art reconstruction techniques and avoiding over-smoothing that these techniques often suffer from. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations and outperforms COSMOS even when using as few as 1-direction data. This is made possible by a nonlinear forward-model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule. We synergistically combine this physics-model with a Variational Network (VN) to leverage the power of deep learning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7 T using highly accelerated Wave-CAIPI acquisitions at 0.5 mm isotropic resolution and demonstrate high-quality QSM from as few as 2-direction data.
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000153781 7001_ $$aChatnuntawech, Itthi$$b1
000153781 7001_ $$aYoon, Jaeyeon$$b2
000153781 7001_ $$aIyer, Siddharth Srinivasan$$b3
000153781 7001_ $$aMilovic, Carlos$$b4
000153781 7001_ $$00000-0002-9485-5434$$aLee, Jongho$$b5
000153781 7001_ $$0P:(DE-He78)29b2f01310f7022916255ddba2750f9b$$aBachert, Peter$$b6$$udkfz
000153781 7001_ $$aAdalsteinsson, Elfar$$b7
000153781 7001_ $$aSetsompop, Kawin$$b8
000153781 7001_ $$aBilgic, Berkin$$b9
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