2D Reprojection of Plenoptic 3D Voxel Data Using Gaussian Intensity Spreading

2020 ◽  
Author(s):  
Seong-Jun Bae ◽  
Soowoong Kim ◽  
Hahyun Lee ◽  
Jinho Lee ◽  
Sung-Chang Lim ◽  
...  
Keyword(s):  
Voxel Data

scholarly journals Modified Regression Model for the Logan Plot

2002 ◽  
Vol 22 (2) ◽  
pp. 240-244 ◽  
Author(s):  
József Varga ◽  
Zsolt Szabo
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Graphical Model ◽  
Logan Plot ◽  
Positron Emission ◽  
Straight Line ◽  
Binding Data ◽  
Data Points ◽  
Voxel Data

Logan's graphical model is a robust estimation of the total distribution volume (DVt) of reversibly bound radiopharmaceuticals, but the resulting DVt values decrease with increasing noise. The authors hypothesized that the noise dependence can be reduced by a linear regression model that minimizes the sum of squared perpendicular rather than vertical ( y) distances between the data points and fitted straight line. To test the new method, 15 levels of simulated noise (repeated 2,000 times) were added to synthetic tissue activity curves, calculated from two different sets of kinetic parameters. Contrary to the traditional method, there was no ( P > 0.05) or dramatically decreased noise dependence with the perpendicular model. Real dynamic 11C (+) McN5652 serotonin transporter binding data were processed either by applying Logan analysis to average counts of large areas or by averaging the Logan slopes of individual-voxel data. There were no significant differences between the parameters when the perpendicular regression method was used with both approaches. The presented experiments show that the DVt calculated from the Logan plot is much less noise dependent if the linear regression model accounts for errors in both the x and y variables, allowing fast creation of unbiased parametric images from dynamic positron-emission tomography studies.

VALIDATION OF MRI-BASED MEASUREMENTS OF SUPRASPINATUS MORPHOLOGY

2003 ◽  
Vol 07 (01) ◽  
pp. 15-23
Author(s):  
Tomotaka Nakajima ◽  
Richard E. Hughes ◽  
Kai-Nan An
Keyword(s):  
Data Processing ◽  
Spin Echo ◽  
Three Dimensional ◽  
Image Data ◽  
Supraspinatus Tendon ◽  
Fast Spin Echo ◽  
Acquisition Time ◽  
Data Set ◽  
Tendon Length ◽  
Voxel Data

The goal of this study was to visualize the supraspinatus tendon three-dimensionally using fast spin-echo (FSE) MRI and validate the accuracy of measuring the dimensions of the supraspinatus tendon based on 3D reconstructed images. Nine cadaver shoulders (51–84 y/o, mean 70.0 y/o) were imaged at conventional T2-weighted spin-echo (CSE), gradient echo (GRE), and 3D T2-weighted FSE sequences. Each "object" of the supraspinatus muscle, tendon and scapula was three-dimensionally reconstructed using ANALYZE™ image data processing software. The FSE images revealed significantly higher contrast of the tendon and contrast-to-noise ratios of the fat-to-tendon and fat-to-muscle. The length of the anterior, middle, and posterior portions of the tendon were measured in two ways: (1) from the three-dimensional reconstructed images, and (2) directly from the cadaver specimen using calipers. No statistically significant differences were found between the ANALYZE™ and caliper measurements using a paired t-test for the anterior (p = 0.55), middle (p = 0.57) and posterior (p = 0.44) portions of the supraspinatus. The 3D FSE sequence exhibits higher spatial resolution, spends shorter acquisition time, and constructs a voxel data set. These advantages can prevent blurring artifacts when imaging the supraspinatus tendon of a human body. Tendon length measurements derived from three-dimensional reconstructions using ANALYZE™ were found to be good estimates of actual tendon length. Therefore, the combination of FSE sequence and 3D image data processing provides a method for noninvasive quantitative analysis of supraspinatus tendon morphology. The results lay the groundwork for future quantitative studies of cuff pathology.

A Graphical Approach for Freeform Surface Offsetting With GPU Acceleration for Subtractive 3D Printing

2016 ◽  
Author(s):  
Mohammad M. Hossain ◽  
Richard W. Vuduc ◽  
Chandra Nath ◽  
Thomas R. Kurfess ◽  
Thomas M. Tucker
Keyword(s):  
Free Form ◽  
Manufacturing Cost ◽  
Generation Process ◽  
Toolpath Planning ◽  
Planning Approach ◽  
Subtractive Manufacturing ◽  
Volumetric Representation ◽  
Processor Unit ◽  
Voxel Data ◽  
Graphics Processor Unit

The lack of plug-and-play programmability in conventional toolpath planning approach in subtractive manufacturing, i.e., machining leads to significantly higher manufacturing cost for CNC based prototyping. In computer aided manufacturing (CAM) packages, typical B-rep or NURBS based representations of the CAD interfaces challenge core computations of tool trajectories generation process, such as, surface offsetting to be completely automated. In this work, the problem of efficient generation of free-form surface offsets is addressed with a novel volumetric representation. It presents an image filter based offsetting algorithm, which leverages the parallel computing engines on modern graphics processor unit (GPU). The scalable voxel data structure and the proposed hardware-accelerated volumetric offsetting together advance the computation and memory efficiencies well beyond the capability of past studies. Additionally, in order to further accelerate the offset computation the problem of offsetting with a large distance is decomposed into successive offsetting using smaller distances. The accuracy of the offset algorithms is thoroughly analyzed. The developed GPU implementation of the offsetting algorithm is robust in computation, easy to comprehend, and achieves a 50-fold speedup on single graphics card (NVIDIA GTX780Ti) relative to prior best-performing dual socket quad-core CPU implementation.

Sign in / Sign up

Export Citation Format

Share Document