I have not seen the video, but if it is MRI (it is from what it sounds), CELL is exactly designed for this kind of applications. In MRI, the output of the sensor is actually a 2D FFT of the actual image. So, to get the actual image, you need to apply a 2D inverse-FFT to the output of the sensor. And, I do not have to say that 2D FFT's are highly computationally complex. If your image is -say- NxN, you have to make N*N FFT's each of whose complexity is N*log2(N). So, you do the math.. For high-res images, the complexity becomes enourmous.
Now, since you do the FFT's first row-wise, then column-wise, you can partition the data first into 8 segments, load each of these segments into a different SPE and do the row-wise FFT computation simulatenously in all 8 cores, move the data around the SPEs for column-wise operation, do the column-wise FFT operation, finally combine output data in each SPE to form the final image... In addition, FFT is also very well suited for SIMD architectures as well.
It is not suprising that CELL accelerates this operation significantly.