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Abstract #0181

Improving Phase-based Conductivity Reconstructions by Means of Deep Learning-based Denoising of B1+ Phase Data

Kyu-Jin Jung1, Stefano Mandija2,3, Jun-Hyeong Kim1, Kanghyun Ryu1, Soozy Jung1, Mina Park4, Mohammed A. Al-masni1, Cornelis A.T. van den Berg2, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, Netherlands, 3Computational Imaging Group for MR diagnostics and therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, 4Department of Radiology, Gangnam Severance Hospital, Seoul, Korea, Republic of

Electrical Properties Tomography reconstruction technique is highly sensitive to noise, as it requires Laplacian calculations of phase data. To alleviate the noise amplification, large derivative kernels combined with image filters are used. However, this leads to severe errors at tissue boundaries. In this study, we employ a deep learning-based denoising network allowing for noise robust conductivity reconstructions obtained using smaller derivative kernels sizes. This comes with the intrinsic advantage of reduced boundary errors. The feasibility study was performed using cylindrical numerical simulations. Then, the proposed technique was tested using spin echo in-vivo data, and clinical patient data.

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