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How can I remove correlated noise spikes from 2 signals?

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I have some MRI data collected across time. When the patient moves, this results in a spike in the signal (so I guess it's not really "noise"). I would like to identify and remove these. So far I have been removing based on the local median and it works pretty well for the big spikes, but I'd like to do better.

I noticed that if I measure the signal in 2 places in the image, some of the noise will be correlated between the 2 signals (e.g., at some time point, both signals show an upward spike of about the same size), while some of it does not appear correlated. It would make sense that spikes due to motion would be correlated, because the motion would affect the whole image. Is there a way I can exploit the apparent correlation to remove these points from the signals? I don't know where to start. Thanks for any tips.

An example of the signals:

matlab fourier-transform signal-processing correlation noise

share|cite|improve this question

edited Feb 1 at 6:45

BadAtGeometry

188215

asked Jun 4 '15 at 18:09

DavidDavid

1

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  A better site for this question is Signal Processing
  $endgroup$
  – user147263
  Jun 5 '15 at 0:58
  

  
  
  
  

add a comment | 

0

1

$begingroup$

I have some MRI data collected across time. When the patient moves, this results in a spike in the signal (so I guess it's not really "noise"). I would like to identify and remove these. So far I have been removing based on the local median and it works pretty well for the big spikes, but I'd like to do better.

I noticed that if I measure the signal in 2 places in the image, some of the noise will be correlated between the 2 signals (e.g., at some time point, both signals show an upward spike of about the same size), while some of it does not appear correlated. It would make sense that spikes due to motion would be correlated, because the motion would affect the whole image. Is there a way I can exploit the apparent correlation to remove these points from the signals? I don't know where to start. Thanks for any tips.

An example of the signals:

matlab fourier-transform signal-processing correlation noise

share|cite|improve this question

edited Feb 1 at 6:45

BadAtGeometry

188215

asked Jun 4 '15 at 18:09

DavidDavid

1

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  A better site for this question is Signal Processing
  $endgroup$
  – user147263
  Jun 5 '15 at 0:58
  

  
  
  
  

add a comment | 

$begingroup$

I have some MRI data collected across time. When the patient moves, this results in a spike in the signal (so I guess it's not really "noise"). I would like to identify and remove these. So far I have been removing based on the local median and it works pretty well for the big spikes, but I'd like to do better.

I noticed that if I measure the signal in 2 places in the image, some of the noise will be correlated between the 2 signals (e.g., at some time point, both signals show an upward spike of about the same size), while some of it does not appear correlated. It would make sense that spikes due to motion would be correlated, because the motion would affect the whole image. Is there a way I can exploit the apparent correlation to remove these points from the signals? I don't know where to start. Thanks for any tips.

An example of the signals:

matlab fourier-transform signal-processing correlation noise

share|cite|improve this question

edited Feb 1 at 6:45

BadAtGeometry

188215

asked Jun 4 '15 at 18:09

DavidDavid

1

$endgroup$

share|cite|improve this question

edited Feb 1 at 6:45

BadAtGeometry

188215

asked Jun 4 '15 at 18:09

DavidDavid

1

share|cite|improve this question

edited Feb 1 at 6:45

BadAtGeometry

188215

asked Jun 4 '15 at 18:09

DavidDavid

1

edited Feb 1 at 6:45

BadAtGeometry

188215

edited Feb 1 at 6:45

BadAtGeometry

188215

asked Jun 4 '15 at 18:09

DavidDavid

1

asked Jun 4 '15 at 18:09

DavidDavid

1

1 Answer
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0

$begingroup$

Yes the topic that you are talking about is in the area of robust statistics. Have a look at my previous answer here. You may want to check robust statistics for correlated data. But it is about the correlation of the data over time. In indeed there is correlation between two signals a short time correlation should tell this.

share|cite|improve this answer

edited Apr 13 '17 at 12:19

Community♦

1

answered Jun 4 '15 at 18:23

Seyhmus GüngörenSeyhmus Güngören

5,57131736

$endgroup$

add a comment | 

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0

$begingroup$

Yes the topic that you are talking about is in the area of robust statistics. Have a look at my previous answer here. You may want to check robust statistics for correlated data. But it is about the correlation of the data over time. In indeed there is correlation between two signals a short time correlation should tell this.

share|cite|improve this answer

edited Apr 13 '17 at 12:19

Community♦

1

answered Jun 4 '15 at 18:23

Seyhmus GüngörenSeyhmus Güngören

5,57131736

$endgroup$

add a comment | 

0

$begingroup$

Yes the topic that you are talking about is in the area of robust statistics. Have a look at my previous answer here. You may want to check robust statistics for correlated data. But it is about the correlation of the data over time. In indeed there is correlation between two signals a short time correlation should tell this.

share|cite|improve this answer

edited Apr 13 '17 at 12:19

Community♦

1

answered Jun 4 '15 at 18:23

Seyhmus GüngörenSeyhmus Güngören

5,57131736

$endgroup$

add a comment | 

$begingroup$

Yes the topic that you are talking about is in the area of robust statistics. Have a look at my previous answer here. You may want to check robust statistics for correlated data. But it is about the correlation of the data over time. In indeed there is correlation between two signals a short time correlation should tell this.

share|cite|improve this answer

edited Apr 13 '17 at 12:19

Community♦

1

answered Jun 4 '15 at 18:23

Seyhmus GüngörenSeyhmus Güngören

5,57131736

$endgroup$

share|cite|improve this answer

edited Apr 13 '17 at 12:19

Community♦

1

answered Jun 4 '15 at 18:23

Seyhmus GüngörenSeyhmus Güngören

5,57131736

edited Apr 13 '17 at 12:19

Community♦

1

edited Apr 13 '17 at 12:19

Community♦

1

answered Jun 4 '15 at 18:23

Seyhmus GüngörenSeyhmus Güngören

5,57131736

answered Jun 4 '15 at 18:23

Seyhmus GüngörenSeyhmus Güngören

5,57131736