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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.

Scientists Gather at University of Delaware for OpenACC Hackathon

Scientists Gather at University of Delaware for OpenACC Hackathon

Scientists Gather at University of Delaware for OpenACC Hackathon

Oak Ridge National Lab, NVIDIA and PGI launched the OpenACC Hackathon initiative last year to help scientists accelerate applications on GPUs. OpenACC was selected as a primary tool since it offers acceleration without significant programming effort and works great with existing application codes.

University of Delaware (UDEL) hosted a five-day Hackathon last week. Selected teams of scientific application developers hacked away under close mentorship of GPU experts and others with extensive experience in programming with OpenACC.

Watch the recap video by Sunita Chandrasekaran, professor at UDEL and organizer of the Hackathon.

The Hackathon at UDEL brought together teams from NASA Langley Research Center, Oak Ridge National Laboratory, National Cancer Institute, National Institutes of Health (NIH), Brookhaven National Laboratory and the UDEL College of Engineering to accelerate their codes across medical imaging, chemical and bio-molecular engineering, computational fluid dynamics, quantum physics and computer science.

Scientists Gather at University of Delaware for OpenACC Hackathon

Scientists Gather at University of Delaware for OpenACC Hackathon

Codes represented at the Hackathon:

  • FUN3D: Large-scale computational fluid dynamics solver for complex aerodynamic flows seen in a broad range of aerospace applications.
  • Lattice QCD: Numerical simulation to solve the high-dimensional non-linear problems in strong interactions for nuclear and particle physics.
  • Application that determines RNA structure using data from small angle x-ray scattering experiments.
  • Application that identifies malicious software based on graph analysis
  • Kinetic Model Builder application that builds models based on chemical engineering principles.
  • DARTS: Open-source dataflow based runtime system to support heterogeneous architectures.

The highlight from the Hackathon was by the team from the National Cancer Institute who achieved a 13x performance increase during the UDEL Hackathon.

Check out the 2016 Hackathons page if you want to be a mentor or to register your team.

GPU-Accelerated Model Reveals Details of Nuclear Fission

GPU-Accelerated Model Reveals Details of Nuclear Fission

GPU-Accelerated Model Reveals Details of Nuclear Fission

Scientists from University of Washington, Warsaw University of Technology in Poland, Pacific Northwest National Laboratory, and Los Alamos National Laboratory, have developed a model that provides a detailed look at what happens during the last stages of the fission process.

According to their research paper, nuclear fission has almost reached the venerable age of 80 years and yet we still lack an understanding in terms of a fully quantum microscopic approach.

Using the new model, the scientists determined that fission fragments remain connected far longer than expected before the daughter nuclei split apart and the predicted kinetic energy agrees with results from experimental observations. This discovery indicates that complex calculations of real-time fission dynamics without physical restrictions are feasible and opens a pathway to a theoretical microscopic framework with abundant predictive power.

Snapshots of the total density profile of the 240Pu fission process.

Snapshots of the total density profile of the 240Pu fission process.

Evaluating the theory amounted to solving about 56,000 complex coupled nonlinear, time-dependent, three-dimensional partial differential equations for a 240Pu nucleus using a highly efficient parallelized GPU code. The calculations required nearly 2,000 NVIDIA GPUs on the Titan supercomputer at Oak Ridge National Lab.

By accurately modeling fission dynamics, the work will impact research areas such as future reactor fuel compositions, nuclear forensics, and studies of nuclear reactions.

Assessing Traumatic Brain Injuries with Deep Learning

Assessing Traumatic Brain Injuries with Deep Learning

Assessing Traumatic Brain Injuries with Deep Learning

More than one million athletes experience a concussion each year in the United States.

Researchers from a California-based startup Neural Analytics have designed a portable headset device that maps blood flow in the brain, which may make it easier to recognize concussions.

“There is growing evidence that concussions can change the blood flow in the brain,” said study author Robert Hamilton, PhD, co-founder of Neural Analytics and a member of the American Academy of Neurology. “While such changes may be detected with MRI, we believe there may be a less expensive and portable way to measure these changes with a transcranial Doppler (TCD) device.”

Using NVIDIA GPUs and deep learning, the device is able to distinguish the brains of young high school athletes who had recently suffered a traumatic brain injury from those of healthy subjects with 83 percent accuracy.

Assessing Traumatic Brain Injuries with Deep Learning

Assessing Traumatic Brain Injuries with Deep Learning

“This research suggests that this advanced form of ultrasound may provide a more accurate diagnosis of concussion,” said Hamilton. “While more research is needed, the hope is such a tool could one day be used on the sidelines to help determine more quickly whether an athlete needs further testing.”

Detecting and Labeling Diseases in Chest X-Rays with Deep Learning

Detecting and Labeling Diseases in Chest X-Rays with Deep Learning

Detecting and Labeling Diseases in Chest X-Rays with Deep Learning

Researchers from the National Institutes of Health in Bethesda, Maryland are using NVIDIA GPUs and deep learning to automatically annotate diseases from chest x-rays.

Accelerated by Tesla GPUs, the team trained their convolutional neural networks on a publicly available radiology dataset of chest x-rays and reports to describe the characteristics of a disease, such as location, severity and the affected organs.

Examples of annotation generations (light green box) compared to true annotations (yellow box) for input images in the test set.

Examples of annotation generations (light green box) compared to true annotations (yellow box) for input images in the test set.

The researchers mention this is the first study (to the best of their knowledge) that mines from a publicly available radiology image and report dataset, not only to classify and detect disease in images, but also to describe their context similar to how a human observer would read.



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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.

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  • Data mining - Big Data
  • CUDA Programming
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