\setheader{{\it CHAPTER \thechapter}}{}{}{\ctsimheadtitle}{}{{\it CHAPTER \thechapter}}
\ctsimfooter
-\section{Overview}\index{Conceptual overview}
+\section{Conceptual Overview}\index{Conceptual overview}
The operation of \ctsim\ begins with the phantom object. A
phantom object consists of geometric elements. A scanner is
specified and the collection of x-ray data, or projections, is
-simulated. That projection data can be reconstructed using various
+simulated. This projection data can be reconstructed using various
user-controlled algorithms producing an image of the phantom
object. These reconstructions can be visually and statistically
compared to the original phantom object.
\helprefn{scanner}{conceptscanner}.
\section{Phantoms}\label{conceptphantom}
-\subsection{Overview}\label{phantomoverview}\index{Phantom!Overview}%
\ctsim\ uses geometrical objects to describe the object being
scanned. A phantom is composed of one or more phantom elements.
\subsubsection{ellipse}
Ellipses use \texttt{dx} and \texttt{dy} to define the semi-major and
-semi-minor axis lengths, with the center of the ellipse at \texttt{(cx,cy)}.
+semi-minor axis lengths with the center of the ellipse at \texttt{(cx,cy)}.
Of note, the commonly used phantom described by
Shepp and Logan\cite{SHEPP74} uses only ellipses.
\subsubsection{rectangle}
-Rectangles use \texttt{cx} and \texttt{cy} to define the position of
+Rectangles use \texttt{(cx,cy)} to define the position of
the center of the rectangle with respect to the origin. \texttt{dx}
and \texttt{dy} are the half-width and half-height of the rectangle.
\subsubsection{triangle}
-Triangles are drawn with the center of the base at \texttt{(cx,cy)},
-with a base half-width of \texttt{dx} and a height of \texttt{dy}.
+Triangles are drawn with the center of the base at \texttt{(cx,cy)}
+and a base half-width of \texttt{dx} and a height of \texttt{dy}.
Rotations are then applied about the center of the base.
\subsubsection{segment}
0.101 in each direction.
\section{Scanner}\label{conceptscanner}\index{Scanner!Concepts}%
-\subsection{Dimensions}
Understanding the scanning geometry is the most complicated aspect of
using \ctsim. For real-world CT simulators, this is actually quite
simple. The geometry is fixed by the manufacturer during the
being a very flexible simulator, gives tremendous options in
setting up the geometry for a scan.
-In general, the geometry for a scan all starts with the size of
+\subsection{Dimensions}
+The geometry for a scan starts with the size of
the phantom being scanned. This is because \ctsim\ allows for
statistical comparisons between the original phantom image and
it's reconstructions. Since CT scanners scan a circular area, the
\subsection{Divergent Geometries}\label{geometrydivergent}\index{Equilinear geometry}\index{Equiangular geometry}
\index{Scanner!Equilinear}\index{Scanner!Equiangular}
-\subsubsection{Overview}
For both equilinear (second generation) and equiangular
(third, fourth, and fifth generation) geometries,
the x-ray beams diverge from a single source to a detector array.
\latexonly{\begin{equation} d_l = 4\,f \tan (\alpha / 2)\end{equation}}
\latexignore{\\\centerline{\emph{4 x F x tan(\alpha/2)}}}
\latexonly{This geometry is shown in figure~\ref{equilinearfig}.}
-\begin{figure}\label{equilinearfig}
+\begin{figure}
\centerline{\image{10cm;0cm}{equilinear.eps}}
\latexonly{\caption{\label{equilinearfig} Equilinear geometry}}
\end{figure}
to interpolation occurring in the frequency domain rather than the
spatial domain.
-\subsection{Filtered Backprojection}\index{Filtered backprojection}
+\subsection{Filtered Backprojection}\index{Filtered backprojection}\index{Symmetric multiprocessing}\index{SMP}
The technique is comprised of two sequential steps:
filtering projections followed by backprojecting the filtered projections. Though
these two steps are sequential, each view position can be processed independently.
\subsubsection{Parallel Computer Processing}\index{Parallel processing}
Since each view can be processed independently, filtered backprojection is amendable to
parallel processing. Indeed, this has been used in commercial scanners to speed reconstruction.
-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 cluster of 16 computers with excellent
+This parallelism is exploited both in the \ctsim\ graphical shell and
+in the \helpref{LAM}{ctsimtextlam} version of \ctsimtext. \ctsim\ can distribute it's workload
+amongst multiple processors working in parallel.
+
+The graphical shell will automatically take advantage of multiple CPU's when
+running on a \emph{Symmetric Multiprocessing}
+computer. Dual-CPU computers are commonly available which provide a near doubling
+in reconstruction speeds. \ctsim, though, has no limits on the number of CPU's
+that can be used in parallel. The \emph{LAM} version
+of \ctsimtext\ is designed to work in a cluster of computers.
+This has been testing with a cluster of 16 computers in a
+\urlref{Beowulf-class}{http://www.beowulf.org} cluster with excellent
results.
\subsubsection{Filter projections}
by \ctsim. They are taken from the standard measurements used by
Herman\cite{HERMAN80}. They are:
-\begin{twocollist}
-\twocolitem{\textbf{$d$}}{The normalized root mean squared distance measure.}
-\twocolitem{\textbf{$r$}}{The normalized mean absolute distance measure.}
-\twocolitem{\textbf{$e$}}{The worst case distance measure over a \latexonly{$2\times2$}\latexignore{\emph{2 x 2}} pixel area.}
-\end{twocollist}
+\begin{itemize}\itemsep=0pt
+\item[]\textbf{$d$}\quad The normalized root mean squared distance measure.
+\item[]\textbf{$r$}\quad The normalized mean absolute distance measure.
+\item[]\textbf{$e$}\quad The worst case distance measure over a \latexonly{$2\times2$}\latexignore{\emph{2 x 2}} pixel area.
+\end{itemize}
These measurements are defined in equations \ref{dequation} through \ref{bigrequation}.
In these equations, $p$ denotes the phantom image, $r$ denotes the reconstruction