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Abstract #1771

Removing structured noise from dynamic arterial spin labeling images

Yanchen Guo1, Zongpai Zhang1, Shichun Chen1, Lijun Yin1, David C. Alsop2, and Weiying Dai1
1Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, United States, 2Department of Radiology, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, MA, United States

Dynamic arterial spin labeling (dASL) images showed the existence of large-scale structured noise, which violates the Gaussian assumptions of baseline functional imaging studies. Here, we evaluated the performance of two deep neural network (DNN) methods on removing the structured noise of ASL images, using the simulated data and real image data. The DNN model, with the noise structure learned and incorporated, demonstrates consistently improved performance compared to the DNN model without the explicitly incorporated noise structure. These results indicate that the noise structure incorporated DNN model is promising in removing the structured noise from the ASL functional images.

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