scholarly journals Interactive OCT-Based Tooth Scan and Reconstruction

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4234 ◽  
Author(s):  
Yu-Chi Lai ◽  
Jin-Yang Lin ◽  
Chih-Yuan Yao ◽  
Dong-Yuan Lyu ◽  
Shyh-Yuan Lee ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
User Study ◽  
Data Transfer ◽  
Boundary Detection ◽  
Optical Methods ◽  
Optical Rectification ◽  
Immediate Feedback ◽  
Low Coherence ◽  
Infrared Rays ◽  
Graphics Processing

Digital dental reconstruction can be a more efficient and effective mechanism for artificial crown construction and period inspection. However, optical methods cannot reconstruct those portions under gums, and X-ray-based methods have high radiation to limit their applied frequency. Optical coherence tomography (OCT) can harmlessly penetrate gums using low-coherence infrared rays, and thus, this work designs an OCT-based framework for dental reconstruction using optical rectification, fast Fourier transform, volumetric boundary detection, and Poisson surface reconstruction to overcome noisy imaging. Additionally, in order to operate in a patient’s mouth, the caliber of the injector is small along with its short penetration depth and effective operation range, and thus, reconstruction requires multiple scans from various directions along with proper alignment. However, flat regions, such as the mesial side of front teeth, may not have enough features for alignment. As a result, we design a scanning order for different types of teeth starting from an area of abundant features for easier alignment while using gyros to track scanned postures for better initial orientations. It is important to provide immediate feedback for each scan, and thus, we accelerate the entire signal processing, boundary detection, and point-cloud alignment using Graphics Processing Units (GPUs) while streamlining the data transfer and GPU computations. Finally, our framework can successfully reconstruct three isolated teeth and a side of one living tooth with comparable precisions against the state-of-art method. Moreover, a user study also verifies the effectiveness of our interactive feedback for efficient and fast clinic scanning.

MULTIBEAM GPU TRANSIENT PIPELINE FOR THE MEDICINA BEST-2 ARRAY

2013 ◽  
Vol 02 (01) ◽  
pp. 1350008 ◽  
Author(s):  
A. MAGRO ◽  
J. HICKISH ◽  
K. Z. ADAMI
Keyword(s):  
Event Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Data Transfer ◽  
Digital Signal ◽  
Dispersion Measure ◽  
Radio Telescopes ◽  
Arrival Times ◽  
Detection Techniques ◽  
Graphics Processing

Radio transient discovery using next generation radio telescopes will pose several digital signal processing and data transfer challenges, requiring specialized high-performance backends. Several accelerator technologies are being considered as prototyping platforms, including Graphics Processing Units (GPUs). In this paper we present a real-time pipeline prototype capable of processing multiple beams concurrently, performing Radio Frequency Interference (RFI) rejection through thresholding, correcting for the delay in signal arrival times across the frequency band using brute-force dedispersion, event detection and clustering, and finally candidate filtering, with the capability of persisting data buffers containing interesting signals to disk. This setup was deployed at the BEST-2 SKA pathfinder in Medicina, Italy, where several benchmarks and test observations of astrophysical transients were conducted. These tests show that on the deployed hardware eight 20 MHz beams can be processed simultaneously for ~640 Dispersion Measure (DM) values. Furthermore, the clustering and candidate filtering algorithms employed prove to be good candidates for online event detection techniques. The number of beams which can be processed increases proportionally to the number of servers deployed and number of GPUs, making it a viable architecture for current and future radio telescopes.

scholarly journals Integrating GPU support for FreeSurfer with OpenACC

2020 ◽  
Author(s):  
Jingcheng Shen ◽  
Jie Mei ◽  
Marcus Walldén ◽  
Fumihiko Ino
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Data Transfer ◽  
Target Function ◽  
Brain Anatomy ◽  
Transfer Scheme ◽  
Accelerated Program ◽  
The Core ◽  
Computationally Expensive ◽  
Graphics Processing

AbstractFreeSurfer is among the most widely used suites of software for the study of cortical and subcortical brain anatomy. However, analysis using FreeSurfer can be time-consuming and it lacks support for the graphics processing units (GPUs) after the core development team stopped maintaining GPU-accelerated versions due to significant programming cost. As FreeSurfer is a large project with millions of source lines, in this work, we introduce and examine the use of a directive-based framework, OpenACC, in GPU acceleration of FreeSurfer, and we found the OpenACC-based approach significantly reduces programming costs. Moreover, because the overhead incurred by CPU-to-GPU data transfer is the major challenge in delivering GPU-based codes of high performance, we compare two schemes, copy- and-transfer and overlapped-fully-transfer, to reduce such data transfer overhead. Exper-imental results show that the target function we accelerated with overlapped-fully-transfer scheme ran 2.3 as fast as the original CPU-based function, and the GPU-accelerated program achieved an average speedup of 1.2 compared to the original CPU-based program. These results demonstrate the usefulness and potential of utilizing the proposed OpenACC-based approach to integrate GPU support for FreeSurfer which can be easily extended to other computationally expensive functions and modules of FreeSurfer to achieve further speedup.

scholarly journals Identifying attributes of GPU programs for difficulty evaluation

2014 ◽  
Vol 53 ◽  
Author(s):  
Dale Tristram ◽  
Karen Bradshaw
Keyword(s):  
Classification System ◽  
Graphics Processing Units ◽  
Data Transfer ◽  
General Purpose ◽  
Scientific Models ◽  
Gpu Acceleration ◽  
Graphics Processing ◽  
Further Development

General-purpose computation on graphics processing units (GPGPU) has great potential to accelerate many scientific models and algorithms. However, some problems are considerably more difficult to accelerate than others, and it may be challenging for those new to GPGPU to ascertain the difficulty of accelerating a particular problem. Through what was learned in the acceleration of three problems, problem attributes have been identified that can assist in the evaluation of the difficulty of accelerating a problem on a GPU. The identified attributes are a problem's available parallelism, inherent parallelism, synchronisation requirements, and data transfer requirements. We envisage that with further development, these attributes could form the foundation of a difficulty classification system that could be used to determine whether GPU acceleration is practical for a candidate GPU acceleration problem, aid in identifying appropriate techniques and optimisations, and outline the required GPGPU knowledge.

scholarly journals FAST CONTEXTUAL VIEW GENERATION AND REGION OF INTEREST SELECTION IN 3D MEDICAL IMAGES VIA SUPERELLIPSOID MANIPULATION, BLENDING AND CONSTRAINED REGION GROWING

2021 ◽  
Author(s):  
Ken Lagos
Keyword(s):  
Graphics Processing Units ◽  
User Study ◽  
Medical Images ◽  
Region Of Interest ◽  
Region Growing ◽  
Image Features ◽  
Visualization System ◽  
Time Interaction ◽  
Series Of Experiments ◽  
Graphics Processing

This thesis presents a 3D widget user-interface (UI), super-ellipsoid shape primitives and a customized volume rendering algorithm that together create a system effective for exploring 3D medical images and for selecting a 3D region within these images. Using a “painting” metaphor, the widget UI supports the fast and precise positioning of a super-ellipsoid shaped paint “blob”. The paint blob can be “deposited” and automatically blended with previously deposited blobs to form arbitrarily-shaped regions enclosing target image features. The rendering of these “focus” regions can be controlled separately from the surrounding contextual region, allowing medical experts to examine and measure image features relative to the context. The system’s core algorithms are designed to execute on Graphics Processing Units (GPUs), resulting in real-time interaction and high-quality visualizations. The focus plus context visualization system presented in this thesis is validated via a user study and a series of experiments.

scholarly journals FAST CONTEXTUAL VIEW GENERATION AND REGION OF INTEREST SELECTION IN 3D MEDICAL IMAGES VIA SUPERELLIPSOID MANIPULATION, BLENDING AND CONSTRAINED REGION GROWING

2021 ◽  
Author(s):  
Ken Lagos
Keyword(s):  
Graphics Processing Units ◽  
User Study ◽  
Medical Images ◽  
Region Of Interest ◽  
Region Growing ◽  
Image Features ◽  
Visualization System ◽  
Time Interaction ◽  
Series Of Experiments ◽  
Graphics Processing

This thesis presents a 3D widget user-interface (UI), super-ellipsoid shape primitives and a customized volume rendering algorithm that together create a system effective for exploring 3D medical images and for selecting a 3D region within these images. Using a “painting” metaphor, the widget UI supports the fast and precise positioning of a super-ellipsoid shaped paint “blob”. The paint blob can be “deposited” and automatically blended with previously deposited blobs to form arbitrarily-shaped regions enclosing target image features. The rendering of these “focus” regions can be controlled separately from the surrounding contextual region, allowing medical experts to examine and measure image features relative to the context. The system’s core algorithms are designed to execute on Graphics Processing Units (GPUs), resulting in real-time interaction and high-quality visualizations. The focus plus context visualization system presented in this thesis is validated via a user study and a series of experiments.

Accelerating 3D Fourier migration with graphics processing units

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCA129-WCA139 ◽  
Author(s):  
Jin-Hai Zhang ◽  
Shu-Qin Wang ◽  
Zhen-Xing Yao
Keyword(s):  
Fourier Transform ◽  
Graphics Processing Units ◽  
Data Transfer ◽  
Computational Cost ◽  
Velocity Model ◽  
Time Shift ◽  
Computing Device ◽  
Depth Migration ◽  
3D Velocity Model ◽  
Graphics Processing

Computational cost is a major factor that inhibits the practical application of 3D depth migration. We have developed a fast parallel scheme to speed up 3D wave-equation depth migration on a parallel computing device, i.e., on graphics processing units (GPUs). The third-order optimized generalized-screen propagator is used to take advantage of the built-in software implementation of the fast Fourier transform. The propagator is coded as a sequence of kernels that can be called from the computer host for each frequency component. Moving the wavefield extrapolation for each depth level to the GPUs allows handling a large 3D velocity model, but this scheme can be speeded up to a limited degree over the CPU implementation because of the low-bandwidth data transfer between host and device. We have created further speedup in this extrapolation scheme by minimizing the low-bandwidth data transfer, which is done by storing the 3D velocity model and imaged data in the device memory, and reducing half the memory demand by compressing the 3D velocity model and imaged data using integer arrays instead of float arrays. By incorporating a 2D tapered function, time-shift propagator, and scaling of the inverse Fourier transform into a compact kernel, the computation time is reduced greatly. Three-dimensional impulse responses and synthetic data examples have demonstrated that the GPU-based Fourier migration typically is 25 to 40 times faster than the CPU-based implementation. It enables us to image complex media using 3D depth migration with little concern for computational cost. The macrovelocity model can be built in a much shorter turnaround time.

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