By How can GPUs help women who are facing a possible diagnosis of breast cancer? The same computational speed-ups that GPUs bring to everything from oil exploration to drug discovery are also taking place in the field of medical imaging. In the case of breast imaging, TechniScan Medical Systems is using parallel computing to give women and their doctors fast, accurate information about breast health.
Breast cancer detection today relies on 2D imaging systems and invasive procedures like biopsy. The former is often imprecise due to various factors such as operator expertise, the latter is painful and often unnecessary. But because doctors don’t want to be wrong on something like cancer, they’ll act aggressively on anything that looks suspicious – even though 80% of biopsies come back negative. That’s a lot of unnecessary fear and distress for patients.
TechniScan’s solution uses 3D ultrasound to create a detailed picture that may help doctors in the process of finding and treating breast cancer. (This video gives a detailed look at how the system works.) Basically, the machine’s scanner rotates all the way around a patient’s breast, capturing a scan every 2 degrees, and then composites a detailed 3D image. Each image is around 8 to 9 million voxels (the 3D equivalent of a pixel) and requires more than 120 million Fast Fourier Transform (FFT) calculations to build.
Now comes the hard part – tackling a computational load of this size in a real-world hospital environment. For 3D ultrasound to make sense for hospitals, the process needed to be both cost effective and time effective.
TechniScan realized that – great as 3D ultrasound promised to be – it wouldn’t get broad traction unless calculations could completed within 30 minutes. That’s the magic number that allows a patient to get her results during her appointment and keeps the device running every hour.
Throw CPUs at this problem, and they could work – but not in the footprint, not at the cost and definitely not at the same speed as GPUs. TechniScan’s system uses two Tesla C1060 GPUs to process images in less than 30 minutes. It would take more than an hour for a quad-core CPU cluster to do the same job. For TechniScan – and for patients everywhere – that’s not a viable result.
TechniScan Medical Systems is in the process of applying for 510(k) clearance of the WBU System. The system is not cleared for sale at this time.
Tagged: 3D, cancer, CUDA, gpu, Medical, Parallel World, Supercomputing, techniscan
