Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography,NMR in Biomedicine

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Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography
NMR in Biomedicine ( IF 2.7 ) Pub Date : 2019-12-16 , DOI: 10.1002/nbm.4211
Reijer Leijsen ₁ , Cornelis van den Berg _{2,

3} , Andrew Webb ₁ , Rob Remis ₄ , Stefano Mandija _{2,

3}

Affiliation

Magnetic resonance electrical properties tomography (MR-EPT) is a technique used to estimate the conductivity and permittivity of tissues from MR measurements of the transmit magnetic field. Different reconstruction methods are available; however, all these methods present several limitations, which hamper the clinical applicability. Standard Helmholtz-based MR-EPT methods are severely affected by noise. Iterative reconstruction methods such as contrast source inversion electrical properties tomography (CSI-EPT) are typically time-consuming and are dependent on their initialization. Deep learning (DL) based methods require a large amount of training data before sufficient generalization can be achieved. Here, we investigate the benefits achievable using a hybrid approach, that is, using MR-EPT or DL-EPT as initialization guesses for standard 3D CSI-EPT. Using realistic electromagnetic simulations at 3 and 7 T, the accuracy and precision of hybrid CSI reconstructions are compared with those of standard 3D CSI-EPT reconstructions. Our results indicate that a hybrid method consisting of an initial DL-EPT reconstruction followed by a 3D CSI-EPT reconstruction would be beneficial. DL-EPT combined with standard 3D CSI-EPT exploits the power of data-driven DL-based EPT reconstructions, while the subsequent CSI-EPT facilitates a better generalization by providing data consistency.

中文翻译：

在基于 MR 的电特性断层扫描中结合深度学习和 3D 对比源反演

磁共振电特性断层扫描 (MR-EPT) 是一种用于根据发射磁场的 MR 测量来估计组织的电导率和介电常数的技术。有不同的重建方法可供选择；然而，所有这些方法都存在一些局限性，阻碍了临床适用性。标准的基于亥姆霍兹的 MR-EPT 方法受到噪声的严重影响。诸如对比源反转电特性断层扫描 (CSI-EPT) 等迭代重建方法通常非常耗时并且依赖于它们的初始化。基于深度学习 (DL) 的方法需要大量的训练数据才能实现足够的泛化。在这里，我们研究了使用混合方法可以获得的好处，即，使用 MR-EPT 或 DL-EPT 作为标准 3D CSI-EPT 的初始化猜测。使用 3 和 7 T 的真实电磁模拟，将混合 CSI 重建的准确度和精度与标准 3D CSI-EPT 重建进行比较。我们的结果表明，由初始 DL-EPT 重建和 3D CSI-EPT 重建组成的混合方法将是有益的。DL-EPT 与标准 3D CSI-EPT 结合利用了数据驱动的基于 DL 的 EPT 重建的强大功能，而随后的 CSI-EPT 通过提供数据一致性来促进更好的泛化。我们的结果表明，由初始 DL-EPT 重建和 3D CSI-EPT 重建组成的混合方法将是有益的。DL-EPT 与标准 3D CSI-EPT 结合利用了数据驱动的基于 DL 的 EPT 重建的强大功能，而随后的 CSI-EPT 通过提供数据一致性来促进更好的泛化。我们的结果表明，由初始 DL-EPT 重建和 3D CSI-EPT 重建组成的混合方法将是有益的。DL-EPT 与标准 3D CSI-EPT 结合利用了数据驱动的基于 DL 的 EPT 重建的强大功能，而随后的 CSI-EPT 通过提供数据一致性来促进更好的泛化。

更新日期：2019-12-16

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