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Advanced Real-Time Visualization for Robotic Heart Surgery

Advanced Real-Time Visualization for Robotic Heart Surgery

Advanced Real-Time Visualization for Robotic Heart Surgery

Researchers at the Harvard Biorobotics Laboratory are harnessing the power of GPUs to generate real-time volumetric renderings of patients’ hearts. The team has built a robotic system to autonomously steer commercially available cardiac catheters that can acquire ultrasound images from within the heart. They tested their system in the clinic and reported their results at the 2016 IEEE International Conference on Robotics and Automation (ICRA) in Stockholm, Sweden.

The team used an Intracardiac Echocardiography (ICE) catheter, which is equipped with an ultrasound transducer at the tip, to acquire 2D images from within a beating heart. Using NVIDIA GPUs, the team was able to reconstruct a 4D (3D + time) model of the heart from these ultrasound images.

Generating a 4D volume begins with co-registering ultrasound images that are acquired at different imaging angles but at the same phase of the cardiac cycle. The position and rotation of each image with respect to the world coordinate frame is measured using electromagnetic (EM) trackers that are attached to the catheter body. This point cloud is then discretized to lie on a 3D grid. Next, infilling is performed to fill the gaps between the slices, generating a dense volumetric representation of the heart. Finally, the volumes are displayed to the surgeon using volume rendering via raycasting, leveraging the CUDA – OpenGL interoperability. The team accelerated the volume reconstruction and rendering algorithms using two NVIDIA TITAN GPUs.

“ICE catheters are currently seldom used due to the difficulty in manual steering,” said principal investigator Prof. Robert D. Howe, Abbott and James Lawrence Professor of Engineering at Harvard University. “Our robotic system frees the clinicians of this burden, and presents them with a new method of real-time visualization that is safer and higher quality than the X-ray imaging that is used in the clinic. This is an enabling technology that can lead to new procedures that were not possible before, as well as improving the efficacy of the current ones.”

Providing real-time procedure guidance requires the use of efficient algorithms combined with a high-performance computing platform. Images are acquired at up to 60 frames per second from the ultrasound machine. Generating volumetric renderings from these images in real-time is only possible using GPUs.

Crytek Announces VR First Partnership with IEEE

Crytek Announces VR First Partnership with IEEE

Crytek Announces VR First Partnership with IEEE

The IEEE joins growing list of high-profile partners for Crytek’s academic virtual reality initiative

Expanding the reach of its VR First academic initiative,Crytek today announced a new strategic partnership with the Institute of Electrical and Electronics Engineers (IEEE).

The IEEE is the world’s largest professional association for the advancement of technology, and supports a network of over 2,300 universities worldwide by providing course accreditation and learning resources.

Yu Yuan, Chair of the IEEE Digital Senses Initiative, said: “The IEEE Digital Senses Initiative is pleased to be a partner in the VR First initiative. We believe that virtual reality, as well as augmented reality, will have a great impact on all aspects of industry.”

Tom Coughlin, Director of the IEEE Region 6 and Chair of the IEEE Consumer Electronics Society Future Directions Committee, said: “Providing laboratories at Universities and Colleges where students can learn and create using state-of-the-art VR tools will be important to prepare the next generation of innovators who will make these technologies a part of our daily lives.”

Welcoming the partnership, Ferhan Özkan, Crytek’s Senior Business Development Manager Partnerships & Alliances, said: “Collaborating with the IEEE dramatically broadens the potential reach of VR First, and emphasizes our commitment to ensuring the program makes a genuine impact on the landscape of VR research and development at the grassroots level.”

Crytek’s collaboration with the IEEE follows on from news that the VR First program – which provides state of the art VR research facilities on university campuses – is being rolled out to seven new academic institutions in North America with support from AMD, Leap Motion, OSVR, and Razer.

For more information on VR First, visit cryengine.com/vrfirst.

Crytek Announces VR First Partnership with IEEE

Crytek Announces VR First Partnership with IEEE

About the Institute of Electrical and Electronics Engineers

The IEEE is the world’s largest professional association advancing innovation and technological excellence for the benefit of humanity. IEEE and its members inspire a global community to innovate for a better tomorrow through its highly-cited publications, conferences, technology standards, and professional and educational activities. IEEE is the trusted “voice” for engineering, computing and technology information around the globe. There are more than 430,000 IEEE members in more than 160 countries.

About VR First

VR First is an academic initiative designed to provide state-of-the-art facilities to anyone interested in exploring the power and potential of virtual reality development. The program encourages educational institutions around the world to establish dedicated VR labs on their campuses by partnering with Crytek and leading VR hardware innovators. VR First participants have full access to Crytek’s CRYENGINE software alongside the latest technology required for VR development. The overarching goal of VR First is to nurture new talent in VR development and create a global community equipped to embrace this exciting field of technology.

About Crytek

 Crytek is an independent videogame developer, publisher, and technology provider with headquarters in Frankfurt am Main (Germany) and six other studios around the world. Established in 1999, Crytek has created multiple award-winning titles, including the original Far Cry, the Crysis series, Ryse: Son of Rome, and Warface. All of Crytek’s games are developed using CRYENGINE, the company’s cutting-edge 3D game technology, which is also the first choice of other leading developers and licensees when creating games for PC, Xbox One, PlayStation®4, and Android. Crytek’s ongoing growth in the games-as-a-service market has extended the company’s reach as they continue to deliver top quality interactive experiences to players through self-publishing platforms online.

A Deep Learning AI Chip for Your Phone

Neural networks learn to recognize objects in images and perform other artificial intelligence tasks with a very low error rate. (Just last week, a neural network built by Google’s Deep Mind lab in London beat a master of the complex Go game—one of the grand challenges of AI.) But they’re typically too complex to run on a smartphone, where, you have to admit, they’d be pretty useful. Perhaps no more. At the IEEE International Solid State Circuits Conference in San Francisco on Tuesday, MIT engineers presented a chip designed to use run sophisticated image-processing neural network software on a smartphone’s power budget.

A Deep Learning AI Chip for Your Phone

A Deep Learning AI Chip for Your Phone

The great performance of neural networks doesn’t come free. In image processing, for example, neural networks like AlexNet work so well because they put an image through a huge number of filters, first finding image edges, then identifying objects, then figuring out what’s happening in a scene. All that requires moving data around a computer again and again, which takes a lot of energy, says Vivienne Sze, an electrical engineering professor at MIT. Sze collaborated with MIT computer science professor Joel Emer, who is also a senior research scientist at GPU-maker Nvidia.

Eyeriss has 168 processing elements (PE), each with its own memory.

Eyeriss has 168 processing elements (PE), each with its own memory.

“On our chip we bring the data as close as possible to the processing units, and move the data as little as possible,” says Sze. When run on an ordinary GPU, neural networks fetch the same image data multiple times. The MIT chip has 168 processing engines, each with its own dedicated memory nearby. Nearby units can talk to each other directly, and this proximity saves power. There’s also a larger, primary storage bank farther off, of course. “We try to go there as little as possible,” says Emer. Furthering the limits on moving data, the hardware compresses the data it does send and uses statistics about the data to do fewer calculations on it than a GPU would.

All that means that when running a powerful neural network program the MIT chip, called Eyeriss, uses one-tenth the energy (0.3 watts) of a typical mobile GPU (5 – 10 W). “This is the first custom chip capable of demonstrating a full, state-of-the-art neural network,” says Sze. Eyeriss can run AlexNet, a highly accurate and computationally demanding neural network. Previous such chips could only run specific algorithms, says the MIT group; they chose to test AlexNet because it’s so demanding, and are confident it can run others of arbitrary size, they say.

Besides a use in smartphones, this kind of chip could help self-driving cars navigate and play a role in other portable electronics. At ISSCC, Hoi-Jun Yoo’s group at the Korea Advanced Institute of Science and Technology showed a pair of augmented reality glasses that use a neural network to train a gesture- and speech-based user interface to a particular user’s gestures, hand size, and dialect.

Yoo says the MIT chip may be able to run neural networks at low power once they’re trained, but he notes that the even more computationally-intensive learning process for AlexNet can’t be done on them. The MIT chip could in theory run any kind of trained neural network, whether it analyzes images, sounds, medical data, or whatever else. Yoo says it’s also important to design chips that may be more specific to a particular category of task—such as following hand gestures—and are better at learning those tasks on the fly. He says this could make for a better user experience in wearable electronics, for example. These systems need to be able to learn on the fly because the world is unpredictable and each user is different. Your computer should start to fit you like your favorite pair of jeans.



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My name is Sayed Ahmadreza Razian and I am a graduate of the master degree in Artificial intelligence .
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Related topics such as image processing, machine vision, virtual reality, machine learning, data mining, and monitoring systems are my research interests, and I intend to pursue a PhD in one of these fields.

جهت نمایش صفحه معرفی و رزومه کلیک کنید

My Scientific expertise
  • Image processing
  • Machine vision
  • Machine learning
  • Pattern recognition
  • Data mining - Big Data
  • CUDA Programming
  • Game and Virtual reality

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