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

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].

• • Practical questions about multi-shell acquisition

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 .