<div dir="ltr"><div class="gmail_default" style="font-family:verdana,sans-serif;font-size:small">Hi Gavin,</div><div class="gmail_default" style="font-family:verdana,sans-serif;font-size:small"><br></div><div class="gmail_default" style="font-family:verdana,sans-serif;font-size:small">if you want to avoid keeping N resampled images, you could have a sum of resampled images which you divide by N at the end to get the average.</div><div class="gmail_default" style="font-family:verdana,sans-serif;font-size:small"><div dir="ltr"><div class="gmail_default" style="font-family:verdana,sans-serif;font-size:small"><br></div><div class="gmail_default" style="font-family:verdana,sans-serif;font-size:small">Regards,</div><div class="gmail_default"><font face="verdana, sans-serif">Dženan Zukić, PhD, Senior R&D Engineer, Kitware (Carrboro, N.C.)</font></div></div></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, Jun 6, 2017 at 8:39 PM, Gavin Baker <span dir="ltr"><<a href="mailto:gavinb+itk@antonym.org" target="_blank">gavinb+itk@antonym.org</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><u></u>




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<div style="font-family:Arial">Thanks, <span class="m_7748408977052133996font" style="font-family:verdana," sans-serif"">Dženan -</span><br></div>
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<div style="font-family:Arial"><span class="m_7748408977052133996font" style="font-family:verdana," sans-serif"">I'll start with the N x 1:1 registration then.</span><br></div>
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<div style="font-family:Arial"><span class="m_7748408977052133996font" style="font-family:verdana," sans-serif"">I can see how to resample the moving image, with the transform applied, as per the examples. However it is not clear how to _combine_ the N images together for the super-resolution resampling. Or would it be a two-step process, where each moving image is first resampled, and after that they are averaged together?</span><br></div>
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<div style="font-family:Arial"><span class="m_7748408977052133996font" style="font-family:verdana," sans-serif"">Thanks -</span><br></div>
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<div style="font-family:Arial"><span class="m_7748408977052133996font" style="font-family:verdana," sans-serif"">  :: Gavin</span><br></div><div><div class="h5">
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<div>On Tue, 6 Jun 2017, at 11:16 PM, Dženan Zukić wrote:<br></div>
<blockquote type="cite"><div dir="ltr"><div style="font-family:verdana,sans-serif;font-size:small">Hi Gavin,<br></div>
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<div style="font-family:verdana,sans-serif;font-size:small">your plan sounds good! There is no 1:N registration, so you should proceed with N 1:1 registrations. Pick one as a reference (#0 is good), register all the other time points to it. You can initialize the k+1-st iteration by the resulting transform of k-th registration to speed things up.<br></div>
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<div style="font-family:verdana,sans-serif;font-size:small">And yes, you can do super-resolution by resampling all these images onto a higher resolution grid, e.g. same origin and direction, 2x higher size and 2x smaller spacing.<br></div>
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<div style="font-family:verdana,sans-serif;font-size:small">ITK has all the required classes for this process. Will you let us know how satisfactory the result was? Ideally with some images :)<br></div>
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<div style="font-family:verdana,sans-serif;font-size:small">Regards,<br></div>
<div><span class="m_7748408977052133996font" style="font-family:verdana," sans-serif"">Dženan Zukić, PhD, Senior R&D Engineer, Kitware (Carrboro, N.C.)</span><br></div>
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<div><div style="font-family:Arial">On Tue, Jun 6, 2017 at 4:00 AM, Gavin Baker <span dir="ltr"><<a href="mailto:gavinb+itk@antonym.org" target="_blank">gavinb+itk@antonym.org</a>></span> wrote:<br></div>
<blockquote style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0.8ex;border-left-width:1px;border-left-style:solid;border-left-color:rgb(204,204,204);padding-left:1ex"><div style="font-family:Arial">Hello!<br></div>
<div style="font-family:Arial"> <br></div>
<div style="font-family:Arial"> I have a time series of 3D data (relatively low resolution), captured in<br></div>
<div style="font-family:Arial"> sequence, with small positional changes (eg. translation). I would like<br></div>
<div style="font-family:Arial"> to perform a super-resolution resampling by first co-registering each<br></div>
<div style="font-family:Arial"> volumetric dataset (using rigid registration) in order to reduce noise<br></div>
<div style="font-family:Arial"> and improve detail.<br></div>
<div style="font-family:Arial"> <br></div>
<div style="font-family:Arial"> Is there a registration process that is 1:N (fixed:moving)?<br></div>
<div style="font-family:Arial"> <br></div>
<div style="font-family:Arial"> Or is the recommended method to pick a fixed image (ie. #0) and register<br></div>
<div style="font-family:Arial"> each 1..N individually to it?<br></div>
<div style="font-family:Arial"> <br></div>
<div style="font-family:Arial"> Given a set of transforms that map each of the 1..N moving images back<br></div>
<div style="font-family:Arial"> to the fixed image for registration, is it possible to then resample the<br></div>
<div style="font-family:Arial"> volume at a higher spatial resolution, combining all image data? IOW<br></div>
<div style="font-family:Arial"> super-resolution resampling?<br></div>
<div style="font-family:Arial"> <br></div>
<div style="font-family:Arial"> I tried searching for the above and didn't have much luck finding<br></div>
<div style="font-family:Arial"> relevant info.<br></div>
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<div style="font-family:Arial"> Thanks -<br></div>
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<div style="font-family:Arial">   :: Gavin<br></div>
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