Abstract #3185

In vivo pelvis conductivity mapping with a 3D patch-based convolutional neural network trained on in silico MR data

Soraya Gavazzi¹, Cornelis AT van den Berg^1,2, Mark HF Savenije^1,2, H Petra Kok³, Lukas JA Stalpers³, Jan JW Lagendijk¹, Hans Crezee³, and Astrid LHMW van Lier¹

¹Radiotherapy Department, University Medical Center Utrecht, Utrecht, Netherlands, ²Computational Imaging Group for MR diagnostic & therapy, University Medical Center Utrecht, Utrecht, Netherlands, ³Radiation Oncology Department, Amsterdam University Medical Center, Amsterdam, Netherlands

Pelvis conductivity is typically reconstructed with Helmholtz-based EPT. To overcome typical limitations of Helmholtz-based EPT in this challenging body site we explored reconstructing pelvis conductivity with deep learning. A 3D patch-based convolutional neural network was trained on in silica MR data (either a full complex B₁⁺ field or transceive phase only) with realistic noise levels. These data were related to realistic pelvic anatomies and electrical properties. Preliminary results indicate that the network retrieved anatomically-detailed conductivity maps, without a priori anatomical knowledge given in input. Quantitatively, conductivity estimates on in vivo volunteer MR data were in line with literature.

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