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Abstract #1272

Uncertainty-Aware Conductivity Reconstruction via a 2.5D Patch-to-Patch Data-driven Model

Chan-Hee Park1, Thierry G Meerbothe2,3, Kyu-Jin Jung1, Chuanjiang Cui1, Cornelis A.T. van den Berg2,3, Stefano Mandija2,3, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Department of Radiotherapy, Division of Imaging and Oncology, UMC Utrecht, Utrecht, Netherlands, 3Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, UMC Utrecht, Utrecht, Netherlands

Synopsis

Keywords: Electromagnetic Tissue Properties, Electromagnetic Tissue Properties, Conductivity, EPT

Motivation: To address the lack of confidence in end-to-end data-driven conductivity reconstructions.

Goal(s): To propose a method for calculating voxel-wise variability of reconstructed conductivity maps and complex B1+ field data discrepancy using a feedback loop, facilitating a comprehensive assessment of uncertainty in deep-learning electrical-property-reconstructions.

Approach: We utilize an end-to-end model to derive conductivity and voxel-wise variability maps, from which complex B1+ maps are predicted using a finite difference approach as data discrepancy index.

Results: The proposed method show high-quality conductivity maps. The voxel-wise variability and data discrepancy index provide confidence on the reconstructed conductivity maps.

Impact: A 2.5D uncertainty-aware data-driven framework is developed for conductivity reconstructions. This approach enhances estimation accuracy and quantifies variability, providing insights into model reliability and improving performance compared to conventional physics-based and end-to-end deep-learning methods.

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Keywords