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Abstract #2532

Introducing prior knowledge through the non-local means filter in model-based reconstructions improves ASL perfusion imaging

Samuel Fielden 1 , Li Zhao 1 , Max Wintermark 2 , and Craig Meyer 1,3

1 Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States, 2 Radiology, Stanford University, Palo Alto, California, United States, 3 Radiology, University of Virginia, Charlottesville, Virginia, United States

The major disadvantage for ASL is low SNR and low spatial resolution of the resulting images. The hypothesis of this work is that the SNR and spatial resolution of perfusion images acquired with ASL can be improved by incorporating side information from high-SNR anatomical images into iterative reconstructions of the data. Here, we use the non-local means filter, trained on high-SNR anatomical images, to denoise and sharpen the ASL reconstruction results. We have tested this method in a simulated numerical phantom and with in-vivo data and found that it improves SNR and reduces error.

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