From Individual to Population: Challenges in Medical Visualization - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 257

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Fig. 23.3 The American Heart Association standardized 2D bull's eye plot (BEP) of the left ventri-
cle of the heart (Courtesy of Konrad Mühler, inspired by Ref. [ 14 ]). Each numbered segment in 2D
corresponds to a particular anatomical segment of the very much three dimensional heart muscle

to predict the outcome of a disease process or therapeutic procedure, or that enrich
measured data with expected physiological phenomena. Examples besides the blood
flow simulations of Taylor et al. include interactive skeletal range of motion [ 43 ] and
biomechanical stress [ 18 ] simulation models for implant planning in orthopedics and
nasal airflow simulation for reconstructive rhinosurgery [ 77 ].
The integration of these predictive models, although potentially valuable, brings
with it new challenges. The addition of potentially complex and dynamic simulation
output data to existing visualizations requires new visual representation techniques.
Furthermore, for the simulation results to be maximally useful, the models should be
tightly coupled to and steered by the user's interaction with the medical visualization.
Finally, most simulations yield data with a certain degree of inherent uncertainty.
The role of this uncertainty should be fully explored and it should be carefully but
explicitly represented as an integral part of the visualization.

23.3.6 Mappings and Reformations

In 2002, the American Heart Association proposed a standardised segmentation and
accompanying 2D bull's eye plot (see Fig. 23.3 ) of the myocardium, or heart mus-
cle, of the left heart ventricle [ 14 ]. This 2D plot is a simple but great example of
reducing complex 3D data to a standardized 2D representation that greatly facili-
tates the interpretation of that data. Another well-known example is that of curved
planar reformation, or CPR, where volume data is sampled along a curved plane
following the trajectory of a blood vessel or other tubular structure, thus enabling
the study of the vessel and its surroundings with the minimum of interaction [ 36 ].
Other good examples of reformation can also be found in brain flattening [ 22 ] and
colon unfolding [ 72 ].
Recently, the idea of intelligently reformatting or mapping 3D data was further
explored by Neugebauer et al. [ 53 ] with aneurysm maps for the visualization of
complex blood flowsimulation data on the inside surfaces of aneurysms and byRieder
et al. [ 59 ] with their tumor maps for the post-operative assessment of radiofrequency