X-Git-Url: http://git.kpe.io/?p=ctsim.git;a=blobdiff_plain;f=doc%2Fctsim-concepts.tex;h=48386e6cb74b4ba015f53056c99f07dc613ef504;hp=797e1a953587d2e3eb043e8c0002106b84fde4e8;hb=cc68f60c280df39d8cd14dfde3c1f5b736ede026;hpb=8c620a980e4fdcc47dbcaba6064e82a70e06e157

diff --git a/doc/ctsim-concepts.tex b/doc/ctsim-concepts.tex
index 797e1a9..48386e6 100644
--- a/doc/ctsim-concepts.tex
+++ b/doc/ctsim-concepts.tex
@@ -3,25 +3,25 @@
 \setfooter{\thepage}{}{}{}{}{\thepage}%
 
 \section{Overview}\label{conceptoverview}\index{Concepts,Overview}%
-In CTSim, a phantom object, or a geometrical description of the object
+In \ctsim, a phantom object, or a geometrical description of the object
 of a CT study is constructed and an image can be created.  Then a
 scanner geometry can be specified, and the projection data simulated.
 Finally that projection data can be reconstructed using various user
 controlled algorithms producing an image of the phantom or study object.
 
-In order to use CTSim effectively, some knowledge of how CTSim works
-and the approach taken is required. \ctsim deals with a variety of
+In order to use \ctsim\ effectively, some knowledge of how \ctsim\ works
+and the approach taken is required. \ctsim\ deals with a variety of
 object, but the two we need to be concerned with are the 'phantom' and
 the 'scanner'.
 
 \section{Phantoms}\label{conceptphantom}\index{Concepts,Phantoms}%
 \subsection{Overview}\label{phantomoverview}\index{Concepts,Phantoms,Overview}%
 
-CTSim uses geometrical objects to
+\ctsim\ uses geometrical objects to
 describe the object being scanned: rectangles, triangles, ellipses,
 sectors and segments.  With these the standard phantoms used in the CT
 literature (the Herman and the Shepp-Logan) can be constructed.  In fact
-CTSim provides a shortcut to construct those phantoms for you.  It also 
+\ctsim\ provides a shortcut to construct those phantoms for you.  It also 
 allows you to write a file in which the composition of your own phantom is
 described.
 
@@ -94,7 +94,7 @@ course you can't stuff an object into a scanner if the object is larger
 than the bore!  In this model, the scanner size or field of view would 
 be used as the standard length scale.
 
-However, CTSim takes another approach.  I believe this approach arose
+However, \ctsim\ takes another approach.  I believe this approach arose
 because the "image" of the phantom produced from the phantom description
 was being matched to the reconstruction image of the phantom.  That is, 
 the dimensions of the 'before' and 'after' images  were being matched.
@@ -226,7 +226,7 @@ $\alpha$ is smaller still.  The dotted square is the bounding square of
 the phantom rotated by 45 degrees, corresponding to the geometry of a
 projection taken at that angle.  Note that the fan beam now clips the top
 and bottom corners of the bounding square.  This illustrates that one may
-still be clipping the phantom, despite CTSim's best efforts.  You have
+still be clipping the phantom, despite \ctsim\'s best efforts.  You have
 been warned.  
 \begin{figure}
 \includegraphics[width=\textwidth]{ctsimfig5.eps}
@@ -245,13 +245,13 @@ backprojection mostly due to interpolation occuring in the frequency domain rath
 than the spatial domain. The technique is comprised of two sequential steps:
 filtering projections and then backprojecting the filtered projections. Though
 these two steps are sequential, each view position can be processed individually.
-This parallelism is exploited in the MPI versions of \ctsim where the data from
+This parallelism is exploited in the MPI versions of \ctsim\ where the data from
 all the views are spread about amongst all of the processors. This has been testing
 in a 16-CPU cluster with good results.
 
 \subsubsection{Filter projections}
 The projections for a single view have their frequency data multipled by
-a filter of absolute(w). \ctsim permits four different ways to accomplish this
+a filter of absolute(w). \ctsim\ permits four different ways to accomplish this
 filtering. Two of the methods use convolution of the projection data with the
 inverse fourier transform of absolute(x). The other two methods perform an fourier
 transform of the projection data and multiply that by the absolute(x) filter and