HI-SPEED Software Packets

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HIGHLY SPECIFIC BUT EDGILY EFFECTIVE DATA-PROCESSING (HI-SPEED) SOFTWARE PACKETS

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 myself to 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. If you want to take an in-depth look at the capabilities of HI-SPEED software packets, please refer to the Java documentation in the archive file.

This archive does not contain the jar file, which is needed to "run" the software packets.

To obtain this jar file, 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

I accept the Software agreement set by the Provider: STBB/NICHD, National Institutes of Health.

Name:

Formal (Institutional) Affiliation:

(Institutional) Contact info and email:

Once I receive your statement of consent, I will send you the Jar file and invite you to a secure google site where you can receive the latest HI-SPEED software packets without having to make another software request again.

Thanks for your interest and please feel free to contact me with your comments and suggestions.

Contact Info:

Cheng Guan Koay, PhD

Department of Medical Physics
University of Wisconsin School of Medicine and Public Health
1161 Wisconsin Institutes for Medical Research
1111 Highland Avenue
Madison, WI 53705

Email: cgkoay AT wisc DOT edu (replace "AT" with "@" and "DOT" with "." )

URL: http://sites.google.com/site/cgkoay/pr

Computational Tools Available For 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].

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 on Sphere, that satisfy

non-antipodal symmetry, Ref.[10].
antipodal symmetry, Ref.[11].

5. Optimal Ordering of Diffusion MRI Measurements, Ref.[12].

References:

[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: 477-482.