HIGHLY SPECIFIC BUT EDGILY EFFECTIVE DATA-PROCESSING (HI-SPEED) SOFTWARE PACKETS
Version: 1.04
Release Date: 01/01/2019
May Peace Prevail
Welcome.
Many computational tools developed in conjunction with my research are collected in HI-SPEED software packets for several reasons:
1. To serve as a resource to the clinical and the research communities.
2. To enable others to validate, test and use the tools.
3. To help me organize my codes and projects :)
Reproducible research in the sense as advocated by Donoho and colleagues (Donoho et al., 2009) in which "researchers publish the article along with the full computational environment that produces the results" is a very encouraging research and pedagogical trend in computational science and engineering. I have taken similar initiative very early in my scientific career to make my research work accessible and reproducible through sharing of codes and sometimes source codes developed in the course of my research in MRI and diffusion MRI.
You may take a look at a slightly dated but relevant poster presented at the 17th ISMRM on 04/18/09 to get a general overview of HI-SPEED software packets. Of course, many more computational tools have been added since then and many more will be added in the future. If you want to take an in-depth look at the capabilities of HI-SPEED software packets, please refer to the Java documentation within the archive file.
This archive does not contain the jar file, which is needed to "run" the software packets.
To obtain this jar file (compiled using Java JDK 1.7), your statement of consent to the software agreement is needed. Below is a template you may use to fill out the details and send to me via email:
HI-SPEED software packets
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I accept the Software agreement set by the Provider: STBB/NICHD, National Institutes of Health.
Name:
Formal (Institutional) Affiliation:
(Institutional) Contact info and email:
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Once I receive your statement of consent, I will send you the Jar file.
IMPORTANT NOTE TO MATLAB USERS: If you are a Matlab user who is interested in calling HI-SPEED routines from the Matlab environment, please state this in your email because I will have to send you a different jar file that is compiled using Java JDK 1.5 and lower.
Please look into the command "javaaddpath" to see how it should be called. The script I have on this site uses the old convention of javaadddpath (pre R2012), see the examples below.
Old convention for Windows:
javaaddpath c:\hispeed\hispeed\JarFile\hispeed.jar;
New convention for Windows:
javaaddpath('c:\hispeed\JarFile\hispeed.jar');
Old convention for Linux/Mac:
javaaddpath /home/hispeed/JarFile/hispeed.jar;
New convention for Linux/Max:
javaaddpath('/home/hispeed/JarFile/hispeed.jar');
Parlez-vous HI-SPEED? You can learn it through these examples! Click here.
Thanks for your interest and please feel free to contact me with your comments and suggestions.
For your kind consideration:
If you appreciate the software or other materials posted here and if you are able and willing, please support: Berea College , Beloit Regional Hospice or DRBU. THANK YOU VERY MUCH!!!
Contact Info:
Emails: cgkoay AT uwalumni DOT com or guankoac AT mail DOT nih DOT gov (replace "AT" with "@" and "DOT" with "." )
Computational Tools Available For Educational, Research and Non-commercial Use
(Click on the links to go to the relevant pages created for these topics)
1. Analysis of MR signal and noise
Analytically exact correction scheme, Ref[1].
Probabilistic Identification and Estimation of Noise (PIESNO), Ref.[8].
Breaking the noise floor, Ref.[7], and with new one-dimensional basis adapted for MR signal Ref.[13]
2. Diffusion Tensor MRI (Refs.[2-4,6,9])
Ordinary and Constrained Nonlinear Least Squares Estimations of Diffusion Tensor, Refs.[2,3].
Error Propagation Framework via Diffusion Tensor Representations, Ref.[4].
Elliptical Cones of Uncertainty and Related Measures, Ref.[6].
3. 2D and 3D Shepp-Logan phantom in the Fourier and the image domains, Ref.[5].
4. Deterministic approaches for distributing points nearly uniformly on the unit sphere that satisfy
non-antipodal symmetry, Ref.[10].
antipodal symmetry, Ref.[11].
5. Optimal ordering of diffusion MRI measurements, Ref.[12].
6. Sparse and optimal acquisition design for diffusion MRI and beyond, Ref.[14].
7. Pseudometrically constrained centroidal Voronoi tessellations, Ref.[15].
8. Uniformly distributed and mirror-symmetric points on the unit sphere, Ref. [16]
9. Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI). Ref.[17]
10. Exact p-value computation of the Wilcoxon-Mann-Whitney test with and without ties. Ref.[17]
(For those who have access to hispeed.jar and have a need to use the above point sets (#4 or #5 above) in Matlab, Mathematica or Java, click here)
References:
[17] Koay CG, Yeh P-H, Ollinger JM, İrfanoğlu MO, Pierpaoli C, Basser PJ, Oakes TR, Riedy G. Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging. NeuroImage 2016; 126: 151-163 DOI: 10.1016/j.neuroimage.2015.11.046 . arXiv:1510.02934
[16] Koay CG. Distributing points uniformly on the unit sphere under a mirror reflection symmetry constraint. Journal of Computational Science 2014; 5: 696-700.
[15] Koay CG. Pseudometrically Constrained Centroidal Voronoi Tessellations: Generating uniform antipodally symmetric points on the unit sphere with a novel acceleration strategy and its applications to diffusion and 3D radial MRI. Magnetic Resonance in Medicine 2014; 71: 723-734.
[14] Koay CG, Özarslan E, Johnson KM, Meyerand ME. Sparse and optimal acquisition design for diffusion MRI and beyond. Medical Physics 2012; 39(5):2499-2511.
[13] Özarslan E, Shepherd TM, Koay CG, Blackband SJ, Basser PJ. Temporal scaling characteristics of diffusion as a new MRI contrast: Findings in rat hippocampus. NeuroImage 2012; 60 (2):1380-1393.
[12] Koay CG, Hurley SA, Meyerand ME. Extremely efficient and deterministic approach to generating optimal ordering of diffusion MRI measurements. Medical Physics 2011; 38 (8): 4795-4801.
[11] Koay CG. A simple scheme for generating nearly uniform distribution of antipodally symmetric points on the unit sphere. Journal of Computational Science 2011; 2: 376-380.
[10] Koay CG. Analytically exact spiral scheme for generating uniformly distributed points on the unit sphere. Journal of Computational Science 2011; 2: 88-91.
[9] Koay CG. Least squares approaches to diffusion tensor estimation. In Derek K. Jones, PhD (Ed.), Diffusion MRI: Theory, Methods, and Applications. Oxford University Press, 2010. (ISBN 0195369777).
[8] Koay CG, Özarslan E and Pierpaoli C. Probabilistic Identification and Estimation of Noise (PIESNO): A self-consistent approach and its applications in MRI. Journal of Magnetic Resonance 2009; 199: 94-103.
[7] Koay CG, Özarslan E and Basser PJ. A signal transformational framework for breaking the noise floor and its applications in MRI. Journal of Magnetic Resonance 2009; 197: 108-119.
[6] Koay CG, Nevo U, Chang LC, Pierpaoli C and Basser PJ. The elliptical cone of uncertainty and its normalized measures in diffusion tensor imaging. IEEE Transactions on Medical Imaging 2008; 27(6): 834-846.
[5] Koay CG, Sarlls JE and Özarslan E. Three dimensional analytical magnetic resonance imaging phantom in the Fourier domain. Magnetic Resonance in Medicine. 2007; 58: 430-436.
[4] Koay CG, Chang LC, Pierpaoli C and Basser PJ. Error propagation framework for diffusion tensor imaging via diffusion tensor representations. IEEE Transactions on Medical Imaging 2007; 26(8): 1017-1034. Erratum in 2007; 26(10): 1424.
[3] Koay CG, Chang LC, Carew JD, Pierpaoli C and Basser PJ. A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. Journal of Magnetic Resonance 2006; 182: 115-125.
[2] Koay CG, Carew JD, Alexander AL, Basser PJ and Meyerand ME. Investigation of anomalous estimates of some tensor-derived quantities in diffusion tensor imaging. Magnetic Resonance in Medicine. 2006; 55: 930-936.
[1] Koay CG and Basser PJ. Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. Journal of Magnetic Resonance 2006; 179: 317-322 .