Given the task definition and input, reply with output. In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

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 type and pattern. As such, they have limited clinical adoption. In this paper, we introduce a "Zero-Shot" medical image Artifact Reduction (ZSAR) framework, which leverages the power of deep learning but without using general pre-trained networks or any clean image reference. Specifically, we utilize the low internal visual entropy of an image and train a light-weight image-specific artifact reduction network to reduce artifacts in an image at test-time. We use Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) as vehicles to show that ZSAR can reduce artifacts better than state-of-the-art both qualitatively and quantitatively, while using shorter execution time. To the best of our knowledge, this is the first deep learning framework that reduces artifacts in medical images without using a priori training set.
We introduce a "Zero-Shot" medical image Artifact Reduction framework, which leverages the power of deep learning but without using general pre-trained networks or any clean image reference.