Improved Padding in CNNs for Quantitative Susceptibility Mapping

TL;DR

This paper introduces an improved padding method for CNNs used in quantitative susceptibility mapping, significantly reducing artifacts and increasing accuracy in boundary regions during various QSM processing tasks.

Contribution

The paper presents a novel padding technique that leverages neighboring valid voxels to enhance CNN performance in QSM, addressing boundary artifacts common in traditional padding methods.

Findings

Enhanced boundary artifact reduction in QSM tasks

Improved accuracy in simulated and in-vivo data

Effective in background removal and inversion tasks

Abstract

Recently, deep learning methods have been proposed for quantitative susceptibility mapping (QSM) data processing: background field removal, field-to-source inversion, and single-step QSM reconstruction. However, the conventional padding mechanism used in convolutional neural networks (CNNs) can introduce spatial artifacts, especially in QSM background field removal and single-step QSM which requires inference from total fields with extreme large values at the edge boundaries of volume of interest. To address this issue, we propose an improved padding technique which utilizes the neighboring valid voxels to estimate the invalid voxels of feature maps at volume boundaries in the neural networks. Studies using simulated and in-vivo data show that the proposed padding greatly improves estimation accuracy and reduces artifacts in the results in the tasks of background field removal, field-to-source inversion, and single-step QSM reconstruction.