PURPOSE: To create a robust means to remove noise pixels using complex data. MATERIALS AND METHODS: A receiver operating characteristic (ROC) curve was used to determine the appropriate choice of magnitude and phase thresholds as well as connectivity values to determine what pixels represent noise in the image. To fine-tune the results, a spike removal and hole replacement operator is applied to reduce Type I error and remove small islands of noise. RESULTS: The use of phase information improves the magnitude-only thresholding approach by further recognizing pixels that contain only noise. The performance of the method is enhanced using local connectivity of magnitude and phase data. An ROC analysis on simulated data shows that the Type I and Type II errors are less than 10(-4) and 10(-3), respectively, without connectivity and 0 and 10(-3), respectively, with connectivity for a signal-to-noise ratio (SNR) of 3:1 or higher. CONCLUSION: The joint use of both magnitude and phase images helps to improve the removal of noise points in magnetic resonance images. This can prove useful in automating the visualization of phase images without the highly distractive phase noise in noise regions. Also, it is useful in susceptibility weighted imaging when taking the minimum intensity projections of variably sized regions. Copyright (c) 2008 Wiley-Liss, Inc.
PURPOSE: To create a robust means to remove noise pixels using complex data. MATERIALS AND METHODS: A receiver operating characteristic (ROC) curve was used to determine the appropriate choice of magnitude and phase thresholds as well as connectivity values to determine what pixels represent noise in the image. To fine-tune the results, a spike removal and hole replacement operator is applied to reduce Type I error and remove small islands of noise. RESULTS: The use of phase information improves the magnitude-only thresholding approach by further recognizing pixels that contain only noise. The performance of the method is enhanced using local connectivity of magnitude and phase data. An ROC analysis on simulated data shows that the Type I and Type II errors are less than 10(-4) and 10(-3), respectively, without connectivity and 0 and 10(-3), respectively, with connectivity for a signal-to-noise ratio (SNR) of 3:1 or higher. CONCLUSION: The joint use of both magnitude and phase images helps to improve the removal of noise points in magnetic resonance images. This can prove useful in automating the visualization of phase images without the highly distractive phase noise in noise regions. Also, it is useful in susceptibility weighted imaging when taking the minimum intensity projections of variably sized regions. Copyright (c) 2008 Wiley-Liss, Inc.
Authors: Samuel R S Barnes; E Mark Haacke; Muhammad Ayaz; Alexander S Boikov; Wolff Kirsch; Dan Kido Journal: Magn Reson Imaging Date: 2011-05-14 Impact factor: 2.546
Authors: Yulin Ge; Vahe M Zohrabian; Etin-Osa Osa; Jian Xu; Hina Jaggi; Joseph Herbert; E Mark Haacke; Robert I Grossman Journal: J Magn Reson Imaging Date: 2009-05 Impact factor: 4.813