醫學圖像常附帶許多不同種類的偽影，取決於許多因素，其中包括掃描設置，機器狀況，患者大小和年齡，周圍環境等。另一方面，現有基於深度學習的醫學圖像降噪方法受限於特定的訓練數據，幾乎不可能包含所有偽影種類，導致臨床的應用上有限。在本文中，我們提出了“有限樣本”的醫學圖像偽影去除方法，在不需要預先訓練的情況下使用深度學習的能力。明確來說，我們在測試階段利用輸入影像合成出訓練數據，並用來訓練一個輕量化的降噪網路。無需事前準備的訓練數據，我們的方法幾乎可以處理任何含有各種或未知偽影的醫學圖像。實驗結果中，我們使用斷層掃描和核磁共振影像，並展現出我們方法的降噪效果，無論是主觀或客觀的評斷下，都比目前最具代表性的方法好。據作者所知，這是第一個在沒有事先訓練的情況下減少醫學圖像偽影的深度學習架構。

Medical images exhibit various types of artifacts with different patterns and their mixtures, which depend on many factors including scan setting, machine condition, patient size and age, surrounding environment, etc.
On the other hand, existing deep learning based medical image artifact reduction methods are restricted by the specific training data that contains predetermined artifact types and patterns, which can hardly capture all possibilities exclusively. Accordingly, they can only work well under the scenarios defined by the training data, resulting in limited clinical adoption.
In this thesis, we introduce ``One-Shot'' medical image Artifact Reduction (OSAR), which exploits the power of deep learning but without using pre-trained networks. Specifically, at test time, we train a light-weight image-specific artifact reduction network using data synthesized from the input image. Without requiring any prior training data, OSAR can work with almost any medical images that contain varying or unknown artifacts. We use Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) as vehicles and show that OSAR can reduce artifacts better than state-of-the-art both qualitatively and quantitatively in comparable time. To the best of the authors' knowledge, this is the first deep learning framework that reduces medical image artifacts without a priori training.

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