  Medical images may contain various types of artifacts with different patterns
and mixtures, which depend on many factors such as scan setting, machine
condition, patients' characteristics, surrounding environment, etc. However,
existing deep-learning-based artifact reduction methods are restricted by their
training set with specific predetermined artifact types and patterns. As such,
they have limited clinical adoption. In this paper, we introduce One-Shot
medical image Artifact Reduction (OSAR), which exploits the power of deep
learning but without using pre-trained general networks. Specifically, we train
a light-weight image-specific artifact reduction network using data synthesized
from the input image at test-time. Without requiring any prior large training
data set, OSAR can work with almost any medical images that contain varying
additive artifacts which are not in any existing data sets. In addition,
Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are used as
vehicles and show that the proposed method can reduce artifacts better than
state-of-the-art both qualitatively and quantitatively using shorter test time.
