Separation
of Signal and Noise in Dynamic MRI Data Using the Kolmogorov-Smirnov Test
David S. Smith1,
Stephanie Barnes1, Thomas E. Yankeelov1
1Vanderbilt
University, Nashville, TN, United States
We present preliminary efforts that indicate that the
Kolmogorov-Smirnov statistical test may be an extremely useful method for
automatically separating signal from noise in dynamic imaging data,
especially when aliased power should be captured but noise should be
ignored.We compare to Otsu's method
and demonstrate an improved automatic classification of signal and noise in
in vivo tumor-bearing mouse data.
