well mri is 3D volume, not surface ! also 7T-mri can provide amazing detail with resolution : fraction of the millimeter in voxel size. anatomical scans do not require toxic fluids for contrast etc. and it is noninvasive and safe. bad sides : digestive and respiratory systems are notoriously hard in MRI, though SWIFT seems to mitigate the issue. real-time is challenging due to immense post-processing, though using GPU and distributed-cluster should be a viable option for MRI too.
MRI is great.
MRI is also hideously expensive.
OCT is potentially a lot cheaper than MRI, with faster scanning, but it does have severe limitations as to the depth of objects it can sense.
It's another tool in the arsenal, not a magic bullet.
Agreed with SpeedEvil: it is an exciting but largely complementary technology to MRI or PET...I am editing a book on medical imaging for Wiley, looking for someone to write a chapter on OCT, if interested pls email: firstname.lastname@example.org, Kris
This is not true.
This is not the first real-time OCT.
Too bad they don't cite other works.
Graphics Processing Unit Accelerated Non-uniform fast Fourier transform for ultrahigh-speed, real-time Fourier-domain OCT,” Opt. Exp., vol. 18, Iss. 22, pp. 23472-23487, Oct., 2010
Real-time 4D signal processing and visualization using graphics processing unit on a regular nonlinear-k Fourier-domain OCT system,” Optics Express, Vol. 18, Issue 11, pp. 11772-11784, 2010
I checked the works cited by solarpower44 because I got interested in. Although they actually showed some real-time OCT, I think it is
insufficient for MEDICAL IMAGING both in the image size and the image penetration depth. It seems this work's designs fit a bit better to target medical use as the title shows.
I strongly agree with solarpower44. The FIRST realtime 3D (or 4D)OCT system was developed by the group at Johns Hopkins using GPU for both OCT image reconstruction (A-scan processing)and volume rendering. While this work uses FPGA for reconstruction and GPU volume rendering. The FPGA method has also been tried before [1,2]. Personally I prefer the GPU method with CUDA since it's the most cost effective approach. The FPGA module should also realize the function but it would cost much more money than a regular GPU.
 T. E. Ustun, N. V. Iftimia, R. D. Ferguson, and D. X. Hammer, “Real-time processing for Fourier domain optical coherence tomography using a field programmable gate array,” Review of Scientific Instruments 79, 114301 (2008).
 A. E. Desjardins, B. J. Vakoc, M. J. Suter, S. H. Yun, G. J. Tearney, B. E. Bouma, “Real-time FPGA processing for high-speed optical frequency domain imaging,” IEEE Transactions on Medical Imaging 28, 1468-1472 (2009).
This work is surely no better than the examples given by solarpower44, because of the physical/optical feature of OCT imaging itself.
For OCT imaging, which is limited by the scattering of tissue itself, the imaging penetration depth is up to 1~2mm for normal biological tissue and may be up to 10mm for ophthalmological structures like cornea.
The imaging size mentioned by particle60, which called lateral scanning range in OCT, is just determined by the number of A-scans in one volume, which is not related to the imaging processing part.
Overall, OCT is kind of novel and upcoming imaging modality, which high resolution but shallower imaging depth compared to US or CT or MRI. I think the most powerful field for OCT is the ophthalmology applications, since eye itself is the most transparent part within the body.
To @Benchuchu, interesting comments. I am editing a book on medical imaging for Wiley and planning to add OCT to standard modalities (MRI, US, CT etc). Would you be interested in contributing a chapter? email@example.com
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.