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Abstract #3923

Improved Padding in CNNs for Quantitative Susceptibility Mapping

Juan Liu1
1Yale University, NEW HAVEN, CT, United States


Recently, deep learning approaches have been proposed for QSM processing - background field removal, field-to-source inversion, and single-step QSM reconstruction. In these tasks, the networks usually take local fields or total fields as inputs, which have valid voxels within volume of interests (VOIs) and invalid voxels outside of VOIs. CNNs fail to consider this spatial information when using spatial invariant filters and conventional padding mechanism, which could introduce spatial artifacts in the QSM results. Here, we propose an improved padding technique utilizing neighboring valid voxels of invalid voxels to estimate the invalid voxels in feature maps of CNNs.

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