https://www.vistrails.org//index.php?action=history&feed=atom&title=SciVisFall2008%2FAssignment_3%2FFAQ 2026-04-24T06:01:38Z Revision history for this page on the wiki MediaWiki 1.36.2 https://www.vistrails.org//index.php?title=SciVisFall2008/Assignment_3/FAQ&diff=1497&oldid=prev 2008-11-13T22:37:41Z

<p>New page: You can post your questions to assignment 3 here: == Exercise 1 == === Can you explain what is meant by &quot;effect of the different scanning modality&quot; in Problem 1e? === The question is aimed...</p> <p><b>New page</b></p><div>You can post your questions to assignment 3 here:<br /> == Exercise 1 ==<br /> === Can you explain what is meant by &quot;effect of the different scanning modality&quot; in Problem 1e? ===<br /> The question is aimed at exploring the relationship between the quantity <br /> that a scanner is measuring and the type of visualization that can be <br /> produced. As explained in the assignment description, a CT scanner <br /> measures tissue density while an MRI measures the effect of water in the <br /> volume. This has important consequences when visualizing the final data <br /> even if the volumes are essentially the same (in this case, both are <br /> human heads).<br /> <br /> == Exercise 2 ==<br /> == Exercise 3 ==<br /> === Could you explain how do I compute discrete differences em 3D? I looked at the &quot;Gradient Generation&quot; python source from Assignment 2 but I did not really understand it and I'm not sure how to apply these discrete differences to a 3D space. ===<br /> <br /> The gradient is defined as:<br /> <br /> grad{f(x_0, ... , x_n)} = (df/dx_0, ... df/dx_n)<br /> <br /> In assignment 2, only two dimensions were used. You have to include the z component.<br /> The magnitude of this would be the standard vector length calculation.<br /> <br /> A discrete differencing approach to find the gradient is explained in:<br /> <br /> http://en.wikipedia.org/wiki/Finite_difference<br /> <br /> == Exercise 4 ==</div>

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