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Abstract #4644

An Unsupervised Deep Learning Approach for Reconstructing Arterial Spin Labeling Images from Noisy Data

Kuang Gong1, Paul Kyu Han1, Debra E. Horng1, Georges El Fakhri1, Chao Ma1, and Quanzheng Li1

1Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States

Recently convolutional neural networks (CNNs) have been successfully applied to computer vision tasks and attracted growing interests in medical imaging. One barrier for the application of deep neural networks is the need of large amounts of training pairs, which are not always available in clinical practice. Inspired by the deep image prior method, this work presents a new image reconstruction framework based on CNN representation where no training pairs and pre-training are needed. We demonstrate the effectiveness of the proposed method by performing denoising and image reconstruction using noisy arterial spin labeling (ASL) data with and without undersampling.

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