Exploring linearity of deep neural network trained QSM: QSMnet. Abstract   Recently, deep neural networkpowered quantitative susceptibility mapping (QSM), QSMnet, successfully performed illconditioned dipole inversion in QSM and generated highquality susceptibility maps. In this paper, the network, which was trained by healthy volunteer data, is evaluated for hemorrhagic lesions that have substantially higher susceptibility than healthy tissues in order to test xe2x80x9clinearityxe2x80x9d of QSMnet for susceptibility. The results show that QSMnet underestimates susceptibility in hemorrhagic lesions, revealing degraded linearity of the network for the untrained susceptibility range. To overcome this limitation, a data augmentation method is proposed to generalize the network for a wider range of susceptibility. The newly trained network, which is referred to as QSMnet, is assessed in computersimulated lesions with an extended susceptibility range (xe2x88x921.4xc2xa0ppm to 1.4xc2xa0ppm) and also in twelve hemorrhagic patients. The simulation results demonstrate improved linearity of QSMnet over QSMnet (root mean square error of QSMnet: 0.04xe2x80xafppm vs. QSMnet: 0.36xe2x80xafppm). When applied to patient data, QSMnet maps show less noticeable artifacts to those of conventional QSM maps. Moreover, the susceptibility values of QSMnet in hemorrhagic lesions are better matched to those of the conventional QSM method than those of QSMnet when analyzed using linear regression (QSMnet: slopexe2x80xafxe2x80xaf1.05, interceptxe2x80xafxe2x80xafxe2x88x920.03, R2xe2x80xafxe2x80xaf0.93; QSMnet: slopexe2x80xafxe2x80xaf0.68, interceptxe2x80xafxe2x80xaf0.06, R2xe2x80xafxe2x80xaf0.86), consolidating improved linearity in QSMnet. This study demonstrates the importance of the trained data range in deep neural networkpowered parametric mapping and suggests the data augmentation approach for generalization of network. The new network can be applicable for a wide range of susceptibility quantification.

Fidelity imposed network edit (FINE) for solving illposed image reconstruction Abstract   Deep learning (DL) is increasingly used to solve illposed inverse problems in medical imaging, such as reconstruction from noisy andor incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DLbased methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DLbased image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pretrained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and undersampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.