We propose Nonlinear Dipole Inversion (NDI) for high - quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state - of - the - art reconstruction techniques. In addition to avoiding over - smoothing that these techniques often suffer from, we also ob viate the need for parameter selection. 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 d eep l earning 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 7T using highly accelerated Wave - CAIPI acquisition s at 0.5 mm isotropic resolutio n and demonstrate high - quality QSM from as f e w as 2 - direction data .

Purpose: To introduce a combined machine learning (ML) and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI), and demonstrate its application in high-resolution structural imaging. Methods: Singleshot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging due to severe distortion artifacts and blurring. While msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot physiological variations which disrupt the combination of the multiple-shot data into a single image. We employ Deep Learning to obtain an interim magnitude-valued image with minimal artifacts, which permits estimation of image phase variations due to shot-to-shot physiological changes. These variations are then included in a Joint Virtual Coil Sensitivity Encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. Results: Our combined ML + physics approach enabled R=8-fold acceleration from 2 EPI-shots while providing 1.8-fold error reduction compared to the MUSSELS, a state-of-the-art reconstruction technique, which is also used as an input to our ML network. Using 3 shots allowed us to push the acceleration to R=10-fold, where we obtained a 1.7-fold error reduction over MUSSELS. Conclusion: Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.