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Abstract #4219

Accelerated Imaging of Metallic Implants Using a 3D Convolutional Neural Network

Xinwei Shi1,2, Kathryn Stevens2, and Brian Hargreaves1,2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Multi-Spectral Imaging (MSI) methods, such as SEMAC and MAVRIC-SL, resolve metal-induced field perturbations by applying additional encoding in the spectral dimension, at the cost of increased scan time. In this work, we introduce a 3D-CNN-based reconstruction to accelerate MSI utilizing spatial-spectral features of aliasing artifacts. We demonstrate in in vivo experiments that the proposed method can accelerate MAVRIC-SL acquisitions by a factor of 3 when used alone, and 17-25 when combined with parallel imaging and half-Fourier acquisition. The 3D-CNN showed significant improvement in image quality compared with parallel image and compressed sensing (PI&CS), with negligible additional computation time.

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