\documentclass[12pt,twocolumn,fullpage]{article}
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\title{Volume Visualization}

% highlight things that we need to revisit
\newcommand{\todo}[1]{\textcolor{red}{\textit{#1}}}
% how we are naming the DBPs
\newcommand{\WinslowDBP}[0]{\textit{Winslow}~}
\newcommand{\EMDBP}[0]{\textit{EM}~}

\begin{document}

\section{Large-Scale Data Visualization}

\begin{figure*}[tb]
  \centering
  \includegraphics[width=0.97\linewidth]{images/histeq-lowres}
  %\includegraphics[width=\linewidth]{images/heq-very-lowres}

  \caption{Preliminary image processing effects on heart data from JHU.}

  \label{fig:jhu-correction}
\end{figure*}

Data being acquired and refined by a group at Johns Hopkins University
(JHU) creates a new analysis and visualization challenge that many in
the biomedical domain are experiencing.  They have developed a complex,
novel process based on 2-photon microscopy which can image organs at
unprecedented levels of detail, and are using it to investigate cardiac
conditions and treatments, such as cardiac ablation.
Figure~\ref{fig:jhu-correction} shows a couple examples of the
resolutions obtained from their imaging process, along with some
post-processing we have done in an effort to draw out some of the
features.  However, due to the intricacy of the operation, it takes
almost 3 weeks to go from a specimen to a complete, imaged heart from
which they can gain insight.  A full week of this process is dedicated
to processing and filtering the data to reconstruct a single coherent
volume from a multitude of independent samples.  Worse, the data are
so large that most existing visualization software is incapable of
visualizing these data the process completes.

The group's standing solution was---after all the aforementioned effort
to acquire these high-resolution scans---to downsample the data into a
size which is amenable to current visualization software.  They turned
to us when they heard about our ImageVis3D tool, whose out-of-core
processing capabilities decouple data size from available memory,
allowing terabytes of data to be visualized on a single
workstation~\cite{Fogal:2010:Tuvok}.  Unfortunately our initial forays
into volume visualization with these data exhibit artifacts which
inhibit one's ability to pull out
features of interest, as demonstrated by Figure~\ref{fig:jhu-diagonal}.

\begin{figure}
  \centering
  \includegraphics[width=\linewidth]{images/heart_2}

  \caption{Heart data from the \WinslowDBP collaboration with a
  diagonal cut on one end.  Collaborators wish to visualize the
  striations visible in the transverse plane across the coronal or
  sagittal planes, however the noise overcomes the signal as the
  cutting plane's angle of incidence increases.}

  \label{fig:jhu-diagonal}
\end{figure}

The current volume reconstruction process is based on the algorithm of
Gopinath et al.~\cite{Gopinath:2008:Reconstruction}, using local window
information from the scans so that the solution remains computationally
tractable.  Our collaborators note
\begin{quote}
  Our application to a range of 70 $\mu$m and the inherently large
  light scattering effect of cardiac tissue being imaged in our work
  created a more significant attenuation than originally considered in
  the design of [Gopinath et al.\cite{Gopinath:2008:Reconstruction}].
\end{quote}
To give context to this quote, portions of the heart are shaved off
and the remaining heart volume is imaged.  As a thick volume, energy
is absorbed when it hits the tissue, but it also penetrates the volume
and is absorbed and scattered at varying rates, based on the type of
cells which it intersects.  As such, the effect is one of a gradual
as opposed to immediate attenuation, and features much larger than
the desired slice thickness will accumulate in a single slice.  The
reconstruction process aims to minimize this effect, however as noted
above, this process leaves much to be desired.

The group's data issues are representative of a growing trend in
large-scale biomedical data acquisition: large-scale acquisition
requires computationally intense reconstruction processes which can
take as much time as the initial scanning process.  For many, the
subsequent analysis processes take more time than the data acquisition
itself, and thus they become the bottleneck in developing and testing
biomedical hypotheses.

Increasingly, visualization and analysis software must be specifically
architected to handle the scale of the data acquired.

\section{Volume Visualization}

We propose the research and development of biomedical and data analysis
software which can help to alleviate the data-intensive science
problems that groups like the one at JHU are experiencing.  As leaders
in the field of ray-guided volume
visualization~\cite{Hadwiger:2012:Guided, Fogal:2013:Analysis}, we are
well-poised to tackle this challenge.

Ray-guided volume visualization has the potential to starkly reduce the
amount
of data which must be processed~\cite{Crassin:2009:Gigavoxels,
Hadwiger:2012:Guided, Fogal:2013:Analysis} to create a visualization.
In 2012, Hadwiger, Pfister, et al. demonstrated that such preprocessing
steps can be fruitfully
applied \emph{during} rendering, if appropriately guided by what the
user needs to see.  Figure~\ref{fig:vishuman-debug} illustrates this
concept: here, the 13 gigabyte Visible Human male has less than 500
megabytes of data visible at any one time.  By explicitly considering
the viewport we are outputting to, data can be reduced substantially,
as shown in
Figure~\ref{fig:rmi-resolution}.

\begin{figure}
  \centering
  \includegraphics[width=\linewidth]{images/vishuman-debug}
  %\includegraphics[width=\linewidth]{images/draft}

  \caption{Per-ray behavior while rendering a large biomedical data
  set.  Green represents space which is skipped.  In red regions, dense
  sampling was required. Blue areas indicate that a ray terminated very
  quickly.  Most of the rendering is blue or green, indicating that
  very little work needed to be done.}
  \label{fig:vishuman-debug}
\end{figure}

We intend to include visualization much earlier in the processing
pipeline.  By using the visual analysis step to guide the
reconstruction, we can produce tools which are able to provide a visual
representation of the data \emph{as} scanning proceeds, due to the data
reduction implied by these ray-guided techniques.

\begin{figure}
  \centering
  \includegraphics[width=\linewidth]{images/rmi-resolution-bricks}

  \caption{Amount of data needed, per-frame, to generate volume
  renderings of a Richtmyer-Meshkov instability at different output
  resolutions.  Output-sensitive adaptive sampling can significantly
  effect the footprint of data needed for a visualization.}

  \label{fig:rmi-resolution}

\end{figure}

Our proposed modified system architecture appears in
Figure~\ref{fig:architecture}.  The prominent feature is the limiting
of data processing to the sections of the data which are known to
be visible.  We note that the `Data store' may be any resource;
classical approaches utilize local or network-attached storage here.
However, we intend for this to be a network server, to further decouple
visualization from data access, storage, and the reconstruction
process.  This allows any reconstruction process to run completely
independent from the visualization software itself, enabling truly
distributed visualization.  While the reconstruction process runs, the
server should return any intermediate results it can---likely specific
to the application in question---with an indication that results are
preliminary.

\begin{figure*}
  \centering
  \includegraphics[width=\linewidth]{images/pipeline}

  \caption{Visualization-guided data access pipeline.  Visualization
  first populates a set of missing data, which is used to dictate
  subsets of the data to reconstruct.  These subregions are queued for
  the reconstruction process.}

  \label{fig:architecture}

\end{figure*}

\bibliographystyle{acm}
\bibliography{vr}
\end{document}
