Image Processing Reference
In-Depth Information
In the last few years, many approaches have been proposed to analyze images
and extract parameters of cardiac shape and function from a variety of cardiac-
imaging modalities. In particular, techniques based on spatiotemporal geometric
models have received considerable attention. This chapter surveys the literature
of two decades of research on cardiac modeling. The purpose of the chapter is
threefold: (1) to serve as a tutorial on the subject for both clinicians and technol-
ogists, (2) to provide an extensive account of modeling techniques in a compre-
hensive and systematic manner, and (3) to critically review these approaches in
terms of their performance and value in clinical evaluation with respect to the
final goal of cardiac functional analysis. From this review it is concluded that
whereas 3-D model-based approaches have the capability to improve the diag-
nostic value of cardiac images, issues such as robustness, 3-D interaction, com-
putational complexity, and clinical validation still require significant attention.
9.1
INTRODUCTION
Cardiovascular disease (CVD) has been the leading cause of death in the U.S.
since 1900 in every year but one (1918). Nearly 2600 Americans die each day
of CVD, an average of one death every 34 sec [1]. CVD claims more lives each
year than the next five leading causes of death combined, which are cancer,
chronic lower respiratory diseases, accidents, diabetes mellitus, and influenza and
pneumonia. According to the most recent computations of the Centers for Disease
Control and Prevention of the National Center for Health Statistics (CDC/NCHS),
if all forms of major CVD were eliminated, life expectancy would rise by almost
7 yr. If all forms of cancer were eliminated, the gain would be only 3 yr. According
to the same study, the probability at birth of eventually dying from a major CVD
is 47%, whereas the chance of dying from cancer is 22% [1].
*
Nowadays, there is a multitude of techniques available for cardiac imaging
that provide qualitative and quantitative information about the morphology and
function of the heart and great vessels (
Figure 9.1
). Use of these technologies
can help in guiding clinical diagnosis, treatment, and follow-up of cardiac dis-
eases. Spatiotemporal imaging is a valuable research tool to understand cardiac
motion and perfusion, and their relationship with different stages of disease.
Technological advances in cardiac-imaging techniques continue to provide
3-D information with increasing spatial and temporal resolution. Therefore, a
single cardiac examination can result in a large amount of data (particularly in
multiphase 3-D studies). These advances have led to an increasing need for
efficient algorithms to plan 3-D acquisitions, automate the extraction of clinically
relevant parameters, and to provide the tools for their visualization.
Segmentation of cardiac chambers is an invariable prerequisite for quantitative
functional analysis. Although many clinical studies still rely on manual delineation
of chamber boundaries, this procedure is time consuming and prone to intra- and
The most recent European survey of CVDs is the one published by the European Society of
Cardiology [2].
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