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Abstract #3371

A Learning-based Metal Artifacts Correction Method using Dual-Polarity Readout Gradients

Kinam Kwon1, Jaejin Cho1, Seohee So1, Byungjai Kim1, Namho Jeong1, and HyunWook Park1

1KAIST, Daejeon, Republic of Korea

Metallic implants induce large field perturbations, which generate various types of artifacts according to the spatial encoding mechanisms in MRI. Especially, a frequency encoding dimension is influenced by bulk displacements with off-resonance frequencies and the pixel sizes are distorted in the frequency encoding dimension. In the abstract, a new learning-based method is proposed to map two metal-induced-artifacts images with positive and negative-polarity readout gradients into a metal-induced-artifacts-free image. Simulated data was utilized for training the network instead of real MR data that requires many resources to be collected.

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