Metal Artifact Correction MRI Using Multi-contrast Deep Neural Networks for Diagnosis of Degenerative Spinal Diseases

Our research aims to accelerate Slice Encoding for Metal Artifact Correction (SEMAC) MRI using multi-contrast deep neural networks for patients with degenerative spine diseases. To reduce the scan time of SEMAC, we propose multi-contrast deep neural networks which can produce high SEMAC factor data from low SEMAC factor data. We investigated acceleration in k-space along the SEMAC encoding direction as well as phase encoding direction to reduce the scan time further. To leverage the complementary information of multi-contrast images, we downsampled the data at different k-space positions. The output of multi-contrast SEMAC reconstruction provided great performance for correcting metal artifacts. The developed networks potentially enable clinical use of SEMAC in a reduced scan time with reasonable quality.

Automated summary

The research aims to accelerate MRI for metal artifact correction in patients with degenerative spine diseases using multi-contrast deep neural networks. It proposes a method to reduce the scan time by generating high SEMAC factor data from low SEMAC factor data. The developed networks provide great performance for correcting metal artifacts with potentially reduced scan time and reasonable quality.