Creating 4D images from a spattering of 3D images could
require anywhere from 500 Mbytes to 5 Gbytes of data per
patient. This is sure to grow as resolution and the number of
image slices increase. Factor in the number of patients
seen on a daily basis, and a thin-client network that stores
all patient data on a fast central server and uses local PCs to
display the images starts to look attractive.
But when portability is mandatory, a system based on the
MicroTCA architecture may be the best bet. MicroTCA provides a rugged small form factor with lots of compute power,
bandwidth, and built-in network connectivity. Meanwhile,
the display of 4D images requires several gigabytes of temporary storage. Designers have to consider the amount,
type, and speed of graphics memories like Graphics Double
Data Rate (GDDR) (see "High-Speed Memory Drives Visualization").
If you segment out your architecture properly, with an
overall goal of designing only what isn't readily available,
chances are you're in good shape. The major building
blocks include the analog front end, the digital back end,
the graphics display renderer, and a system controller with
optional networking ().
Data acquisition and image pre-processing make up the
analog front end. They rely heavily on the imaging modality,
which may require one or more DSPs, FPGAs, or ASIC ICs.
The digital back end includes the image reconstruction and
post-processing blocks. Depending on the modality's complexity, this block could be a simple processor (GPU) or one
or more advanced processors (CPU and/or GPU) containing
multithreading capabilities with multiple cores. For
demanding tasks like image processing and reconstruction,
when top performance is needed, processors like the Cell
Broadband Engine may be more appropriate ().
If your future involves multiple cores, seriously consider
software-based decisions, such as the operating system,
message passing interface, parallel programming language, and so on. Even otherwise trivial decisions like which
type of file system to use become substantially more important and should be made carefully (see "Parallel Programming And Multicore Environments" and "Multicore My Way").
TechniScan's UltraSound CT Imaging System produces
fully digital breast images based on transmission ultrasound. This type of ultrasound can be used to produce two
images of the breast based on both the speed and attenuation of sound ().
"When a vendor says that they can replace a major component in my system that doubles the performance of the
original component and requires the same power and cooling as the original component, I get really interested," says
Frank Setinsek, system architect for TechniScan (see
"Advances Trigger An Ultrasonic Boom,").
IMAGING MODALITIES
Except for X-rays, which are
recorded directly on film, all medical imaging modalities use similar basic principles and rely on a similar
data flow (). The process starts with
the imaging machine building an analog
"image." It does so by applying one stimulus or more to the patient (subject) and then
recording the response to the stimulus.
Then the raw data is usually pre-processed
and "scrubbed" to both suppress noise and
enhance signal quality.
Next, the pre-processed image is typically
reconstructed by converting (e.g., using a
Fourier transform) thousands of transmission measurements into a pixel map that
makes up a physically meaningful image or
volume. The image or volume then is postprocessed to improve its appearance and
usefulness. The image display may be
standalone or a composite built using overlaying images captured with different technologies, like MRI and PET. If slicing techniques were used, the slices may be viewed
one at a time or combined for a 3D view.
Finally, computer-aided diagnosis (CAD) may be
employed to aid in analysis and interpretation of images.
CAD works by using the post-processed data and applying
segmentation, followed by feature selection for the regions
of interest and feature classification using pattern-recognition algorithms. The physician or radiologist then enters the
equation as the final interpreter. After analyzing the images
and optionally using historical data as a base for comparison, the physician delivers the diagnosis or update to the
patient (see "Video Processing Brings New Meaning To
Motion,").
ADDITIONAL WEB RESOURCES
One Web site, www.rtstudents.com, was designed with radiology students
in mind. This portal to other useful sites also contains a
plethora of great links for research, discussion, and
resources to aid learning.
GE's Medcyclopaedia includes a medical-imaging encyclopedia, a glossary, and an outstanding interactive e-learning section with a complete anatomy breakdown (www.medcyclopaedia.com). With this site, you'll never get the
cerebellum confused with the temporal lobe again; the elearning module also includes a virtual index-card-by-picture or -by-name learning system for medical-imaging terms.
If you're looking for information on high-performance
computing (HPC) using clusters, some helpful Web sites
include IEEE's Computer Society Task Force on Cluster Computing (www.ieeetfcc.org), the Linux HPC site (www.linuxhpc.org), the Windows HPC site (www.winhpc.org), the Sun
HPC site (sun.com/hpc), and IBM's deep computing site (www.ibm.com/servers/deepcomputing/).
Also, be sure to read GPU Cluster for High Performance
Computing by Zhe Fan, et al. Other written resources
include white papers such as Intel's Optimizing Software
for Multi-core Processors and How Much Performance Do
You Need for 3D Medical Imaging?, Toshiba's The Next
Revolution: 256-Slice CT by Richard Mather, PhD, and Altera's Medical Imaging Implementation
Using FPGAs.
DIAGNOSTIC FOOD FOR THOUGHT
Before designing your next medical imaging system, there's one last thing to consider. With the correct image analysis and diagnostic programming, is it possible for a computer to "out-diagnose" a physician or radiologist? It certainly seems feasible for
some ailments even now, and this possibility grows stronger with each generation of processor power and
knowledge.