Meeting Banner
Abstract #3385

Artificial Neural Network for Suppression of Metal Artifacts with Slice Encoding for Metal Artifact Correction (SEMAC) MRI

Sunghun Seo1, Ki Hwan Kim1, Seung Hong Choi2, and Sung-Hong Park1

1Magnetic Resonance Imaging Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea, 2Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea

We present a new method of artificial neural network (ANN) to suppress metal artifacts in MR Imaging with Slice Encoding for Metal Artifact Correction (SEMAC). Seven titanium‑embedded phantoms were imaged using different SEMAC factors. The acquired data with low and high SEMAC factors were separated into input and label images, respectively, for training. The trained model was tested on separate phantoms. Metal artifacts in low SEMAC factors could be further suppressed visually and quantitatively using the implemented ANN, with the performance being comparable to that of label images. The proposed method reduces scan time necessary for high‑quality SEMAC imaging.

This abstract and the presentation materials are available to members only; a login is required.

Join Here