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<div style="font-family:Arial;">Thanks, Samuel,<br></div>
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<div style="font-family:Arial;">Great point - using the N/2 sample makes a lot of sense. I'll start with that example and see how I go. Any thoughts on my followup question about the super-resolution resampling the N images together would be most appreciated.<br></div>
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<div style="font-family:Arial;">Regards -<br></div>
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<div style="font-family:Arial;"> :: Gavin<br></div>
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<div>On Tue, 6 Jun 2017, at 11:33 PM, Samuel Gerber wrote:<br></div>
<blockquote type="cite"><div dir="ltr"><div style="font-family:Arial;">Hi Gavin,<br></div>
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<div><div style="font-family:Arial;">One small addition, I would probably take the N/2 image to register everybody else to, in order to minimize the maximal transformation (might not matter in your case since it is only small transformations but it could minimize errors due to resampling).<br></div>
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<div style="font-family:Arial;">This example has all the required classes you should need:<br></div>
<div style="font-family:Arial;"><a href="https://itk.org/Wiki/ITK/Examples/Registration/ImageRegistrationMethod">https://itk.org/Wiki/ITK/Examples/Registration/ImageRegistrationMethod</a><br></div>
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<div>You will most likely want to use a different optimizer and you can see in the code how to set the size etc of the output image in the resampler.<br></div>
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<div defang_data-gmailquote="yes"><div style="font-family:Arial;">On Tue, Jun 6, 2017 at 9:16 AM, Dženan Zukić <span dir="ltr"><<a href="mailto:dzenanz@gmail.com">dzenanz@gmail.com</a>></span> wrote:<br></div>
<blockquote defang_data-gmailquote="yes" 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 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>
<div style="font-family:verdana, sans-serif;font-size:small;"><br></div>
<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>
<div style="font-family:verdana, sans-serif;font-size:small;"><div dir="ltr"><div style="font-family:verdana, sans-serif;font-size:small;"><br></div>
<div style="font-family:verdana, sans-serif;font-size:small;">Regards,<br></div>
<div><span class="font" 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 defang_data-gmailquote="yes"><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">gavinb+itk@antonym.org</a>></span> wrote:<br></div>
<blockquote defang_data-gmailquote="yes" 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>
<div style="font-family:Arial;"> <br></div>
<div style="font-family:Arial;"> Thanks -<br></div>
<div style="font-family:Arial;"> <br></div>
<div style="font-family:Arial;"> :: Gavin<br></div>
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<div style="font-family:Arial;">-- <br></div>
<div><div dir="ltr"><div style="font-family:Arial;"><span class="colour" style="color:rgb(136, 136, 136)"><span class="size" style="font-size:12.8px">Samuel Gerber</span></span><br></div>
<div style="font-family:Arial;"><span class="colour" style="color:rgb(136, 136, 136)"><span class="size" style="font-size:12.8px">R&D Engineer</span></span><br></div>
<div style="font-family:Arial;"><span class="colour" style="color:rgb(136, 136, 136)"><span class="size" style="font-size:12.8px">Kitware, Inc.</span></span><br></div>
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