Architecting New Dimensions Of Medical Imaging

Several technologies—like 4D (3D over time) ultrasound imaging (Fig. 1)—have taken the medical-imaging market by storm. The medical field will continue to benefit from Moore's Law as speed and resolution continue to improve. Take for example the joint effort between engineers and scientists from IBM and the Mayo Clinic that seeks to exploit recent parallelism advances in processors such as the Cell.

The result is a dramatic acceleration in 3D medical-image processing, which significantly advances the image-fusion process. Also known as registration and overlay, this process creates 3D images by aligning two or more images captured by different devices (e.g., MRI and CT), or the same type of device on different dates. Using alignment algorithms, images are "fused" to provide more complete visual information for easier detection of tissue changes like tumor growth or shrinkage.

But University of Calgary students have taken a different approach in creating the most complete 4D model of a human yet (Fig. 2). Using a joystick, the object-oriented hologram, dubbed CAVEman, can provide a view of up to 3000 distinct body parts. This technology will help physicians plan for complex surgeries and allow patients to see a map of their body before surgery.

LET'S TALK ABOUT GOALS
Instead of using the hospital's ICU equipment, patients can be monitored at home. The patient's quality of life improves and medical costs are reduced, achieving two key goals of these new technologies. Another goal is improved accuracy—for example, imaging the heart in a single beat or the lungs in a single breath. Researchers also hope to improve diagnostic capabilities via the least invasive procedure in as close to real time as possible.

Let's not forget about reducing or eliminating false positives and false negatives. Traditional mammograms have a high percentage of false positives, resulting in the unnecessary removal of tissue in far too many patients. Added costs for false positives include evaluation costs, treatment costs (of the observed breast cancer), and the immeasurable emotional cost associated with a false-positive result. Of course, a false negative can be much worse, possibly leading to death. And for the physicians responsible for interpreting these images, the ongoing goal is to increase the potential to find anomalies in organs, tissue, and cells via the least invasive means.

ARCHITECTING MEDICAL IMAGING SYSTEMS
These goals imply one ongoing theme for all new or redesigned medical imaging systems: the need for maximum computing power to provide the highest-resolution processed images in the least amount of time. Typically, that means maximizing the number of cores and threads for the target form factor, since many imaging algorithms are parallel-processing friendly.

But before deciding which brand of multicore processor to use (see table), carefully consider the system's scalability and upgradability. Due to jumps in performance and data rates within the semiconductor and storage industries, it's important to be able to drop in the next-generation device or add more nodes to the system (when using clustering) without redesigning and retesting the entire system. If you can get away with only a recompile, you're ahead of the game.

"Scalability of solutions is key to enabling customers' reuse of software and algorithms across products," says Bob Ghaffari, manager of the Medical Segment for Intel. "Having a silicon architecture that can address a variety of performance and power bands ranging from high-end CT equipment down to a low-power portable ultrasound product requires an architecture that can scale."

Ghaffari said Intel is focused on meeting a variety of medical application requirements by providing highly functional and flexible system-level building blocks, thereby minimizing the cost of ownership and significantly accelerating time-to-market.

When attempting to determine just how many cores and threads are needed, try to make the data path the bottleneck, because there's really no point in processing data faster than it can be stored. If a local hard drive will be used, then serial ATA (SATA) or serial attached SCSI may be the limiting factor. Otherwise, if you're writing data to a device on the network, the network connection (Ethernet or wireless) will have a known maximum bandwidth.

Intel suggests some guidelines for choosing the number and type of processors, as well as how to tweak them. First, determine board performance and form-factor criteria. Next, run the code. Continue to optimize code, and stop after a reasonable number of iterations. Then, adjust the performance. If this is adequate, you're done with architecture selection. If greater system performance is required, add external devices for acceleration offload.