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Abstract #1038

The influence of different MR contrasts in multi-channel convolutional neural networks on pseudo-CT generation for orthopedic purposes

Mateusz C Florkow1, Frank Zijlstra1, Koen Willemsen1, René M Castelein1, Harrie Weinans1, Bart CH van der Wal1, Max A Viergever1, Marijn van Stralen1, and Peter R Seevinck1

1UMC Utrecht, Utrecht, Netherlands

Conventional MR images and pseudo-CT’s (pCT’s) generated using state-of-the-art machine learning techniques poorly characterize bone anatomies, preventing applicability for orthopedic applications. We hypothesize that smart use of several specific MR contrasts will expose the information needed for diagnostic quality bone visualization. We designed a patch-based convolutional neural network taking groups of different MR contrasts -which were obtained from a single multi-gradient sequence- as inputs . It generated competitive pCT scans, capturing local anatomical variances present in the dataset. We show that Dixon reconstructed inputs appear to generate better soft-tissue visualization, while complex-valued data show promising results in bone reconstruction.

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