The core components of the system are a light-weight 7-DOF robot arm (LWR 4+, KUKA Robotics Corp., Augsburg, Germany), a surgical tool (Endo360, EndoEvolution, Raynham, MA), a plenoptic camera and software for quantitative 3D measurement applications (R12, Raytrix GmbH, Schauenburgerstrasse, Germany) with a custom field of view (FOV) of 70 × 65 × 30 mm [25 ], and custom software developed in Open RObot COntrol Software (OROCOS) [26 ] and Robot Operating System (ROS) [27 ] as shown in Fig. 1 . The KUKA Robot Controller (KRC) computer is in charge of the kinematics, dynamics, control and generating motion trajectories. A simple program written in KUKA Robot Language (KRL) enables the communication between the KRC and the OROCOS components and ROS packages via Ethernet using the Fast Research Interface (FRI) [28 ]. The Raytrix camera has an Application Programming Interface (API) with proprietary binary libraries and some read-only parameters. The API provides a virtual depth image and a focused image which are captured on a dedicated Windows machine with an NVidia GeForce GTX Titan graphics card. Virtual 3D depth is processed at 10 frames per second (fps) at a pixel resolution of 2008 × 1508.
Geforce gtx titan
Manufactured by NVIDIA
Sourced in United States
The GeForce GTX TITAN is a high-performance graphics processing unit (GPU) designed and manufactured by NVIDIA. It is part of the company's GeForce GTX product line. The GeForce GTX TITAN is a powerful graphics card intended for professional and enthusiast users who require advanced graphics processing capabilities.
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Lab products found in correlation
6 protocols using geforce gtx titan
1
Robotic Surgical Tool Measurement System
2
Efficient GPU-accelerated Reconstruction
3
Simulated Remote PPG Spectra Analysis
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For each simulated wavelength, λ ∈ <450,1000> nm, and skin layer, l = 1 … 6, the relevant outputs from MCML for expressing the simulated remote PPG spectra are the fraction of photons reaching the surface per cm as a function of radial distance from the origin, Rdr(r, λ, l), and the total diffuse reflectance, Rdt(λ, l), expressed as fraction of total emitted photons.
Matlab was used for further processing. The diastolic-systolic diffuse reflectance outputs were applied to mimic the PPG spectra for remote and for contact-based acquisition. The remote normalized pulsatile reflectance PPG, PPGREM, was AC/DC normalized for pulsating layer, l, as normalized fractions of the total incident photons; i.e., where RdTs, and RdTd denote the total diffuse reflectance during systole and diastole, respectively. Since each wavelength needs to be simulated under diastolic and systolic conditions, for a skin model with five pulsating layers at least six simulation runs were required, per wavelength. Each simulation run consisted of 10E8 to 40E8 photons and required approximately 10 min of processing time on a Linux server operating an NVIDIA GeForce GTX TITAN with compute capability 3.5 (14 SMs).
Matlab was used for further processing. The diastolic-systolic diffuse reflectance outputs were applied to mimic the PPG spectra for remote and for contact-based acquisition. The remote normalized pulsatile reflectance PPG, PPGREM, was AC/DC normalized for pulsating layer, l, as normalized fractions of the total incident photons; i.e., where RdTs, and RdTd denote the total diffuse reflectance during systole and diastole, respectively. Since each wavelength needs to be simulated under diastolic and systolic conditions, for a skin model with five pulsating layers at least six simulation runs were required, per wavelength. Each simulation run consisted of 10E8 to 40E8 photons and required approximately 10 min of processing time on a Linux server operating an NVIDIA GeForce GTX TITAN with compute capability 3.5 (14 SMs).
4
GPU-Accelerated Multi-Modal Microscopy Protocols
5
Improved U-Net for TRUS Image Segmentation
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The previously published U-Net 20 was implemented using Keras 21 with TensorFlow 22 and modified by adding 50% dropouts at every block on the expansion section of the network to increase regularization and prevent overfitting. In addition, transpose convolutions were used at each step in the expansion section instead of the standard upsampling followed by convolution, as this allowed for improved performance in preliminary experiments. Data augmentation from random combinations of horizontal flips, 2D shifts (up to 20%), rotations (up to 20°), and zooms (up to 20%) were employed to double the training dataset to 10 836 2D TRUS images. Preliminary experiments led to the selection of an Adam optimizer, 0.0001 learning rate, Dice-coefficient loss function, 200 epochs, and 200 steps per epoch. This network was trained and used for predicting unseen data on a personal computer with two Xeon E5645 central processing units at 2.40 GHz (Intel Corporation, Santa Clara, CA, USA), 24.0 GB of memory, and a 6 GB Ge-Force GTX TITAN (NVIDIA Corporation, Santa Clara, CA, USA) graphics processing unit (GPU).
6
Multicoil MRI Reconstruction Workflow
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At this stage, all data processing was done offline. Multicoil raw data for each slice were first corrected for gradient delays [31 ] and then compressed to 8 virtual channels using a principle component analysis. A convolution-based gridding [32 ] without density compensation was used to interpolate the radial samples onto a Cartesian grid on which all successive computations were performed. Gradient delay correction, channel compression, and gridding were done in MATLAB (MathWorks, Natik, MA), while the iterative optimization was implemented in C/CUDA using GeForce GTX TITAN (NVIDIA, Santa Clara, CA).
Regularization parameters α and β are initially set to 1 and subsequently reduced by a factor of 3 in each Gauss–Newton step. A minimum value of α was introduced to control the noise in higher Gauss–Newton steps. The chosen value of αmin = 0.0015 for applications to the brain was defined by optimizing SNR without compromising quantitative accuracy or delineation of structural details. With similar settings, αmin = 0.001 was chosen for abdominal studies. Constants in the Sobolev norm were the same as in [8 (link)]. As βmin was insensitive to the final results, no minimum value was set for β. Similar to [8 (link)], 10 Gauss–Newton steps were used for IRGNM to ensure convergence.
Regularization parameters α and β are initially set to 1 and subsequently reduced by a factor of 3 in each Gauss–Newton step. A minimum value of α was introduced to control the noise in higher Gauss–Newton steps. The chosen value of αmin = 0.0015 for applications to the brain was defined by optimizing SNR without compromising quantitative accuracy or delineation of structural details. With similar settings, αmin = 0.001 was chosen for abdominal studies. Constants in the Sobolev norm were the same as in [8 (link)]. As βmin was insensitive to the final results, no minimum value was set for β. Similar to [8 (link)], 10 Gauss–Newton steps were used for IRGNM to ensure convergence.
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