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.
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