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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