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Exploring linearity of deep neural network trained QSM: QSMnet+ by Jung, Woojin; Yoon, Jaeyeon; Choi, Joon Yul; Kim, Jae Myung; Nam, Yoonho; Kim, Eung Yeop; Lee, Jongho is a scholarly article available to read on EtoBox.

What is Exploring linearity of deep neural network trained QSM: QSMnet+ about?

Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill conditioned dipole inversion in QSM and generated high-quality 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 linearity 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 computer-simulated lesions with an extended susceptibility range (-1.4 ppm to +1.4 ppm) and also in twelve hemorrhagic patients. The simulation results demonstrate improved linearity of QSMnet+ over QSMnet (root mean square error of QSMnet+: 0.04 ppm vs. QSMnet: 0.36 ppm). When applied to patient data, QSMnet+ maps show less noticeable artifacts to those of conventional QSM maps. Moreover,

Author

Jung, Woojin; Yoon, Jaeyeon; Choi, Joon Yul; Kim, Jae Myung; Nam, Yoonho; Kim, Eung Yeop; Lee, Jongho

Published

2019

Language

EN