Mustafa Okan Irfanoglu^{1,2,3}, Amritha Nayak^{1,2,3}, Jeffrey Jenkins^{1,2}, and Carlo Pierpaoli^{1,2}

Here we present a series of improvements and new features of the TORTOISE diffusion MRI data processing software (www.tortoisedt.org). TORTOISEv3 has been programmed in C++ and it is now significantly faster, can be batched and it fully benefits from modern multi-core CPU architectures. The DIFFPREP module brings a multitude of new and state-of-art features including DWI denoising, Gibbs ringing removal, and the ability to perform motion and eddy currents distortion correction for very high b-value data. The new DIFFCALC module can perform MAP-MRI propagator estimation and the output can be easily imported in other software packages for statistical analysis and atlas creation.

Table 1 summarizes the new features of TORTOISEv3. Among these new features, the remainder of this manuscript will focus on DIFFPREP’s new strategies to perform high b-value motion & eddy currents distortion correction and its MAP-MRI features.

** High
b-value motion & eddy currents distortion correction:**
DIFFPREP’s strategy for low-shell motion & eddy currents
distortion is to register the DWIs to a b=0 s/mm

* MAP-MRI features*: MAP-MRI [7] is a diffusion propagator estimation methodology, which uses an analytical, orthonormal basis set to describe the probabilities for the positions of water molecules. With this strategy, once the diffusion propagator is estimated, it is straightforward to derive several maps/stains about the tissue microstructure. Non-gaussianity map, which describes the deviations from a gaussian diffusion described by the tensor model, the propagator anisotropy, which describes deviations from an isotropic propagator and return to the origin probability, which describes the probability that a particle will return to its starting position and which is directly inversely proportional to pore size are examples of such maps. Figure 2 displays a non-gaussianity map, return to origin, return to axis and return to plane maps.

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2. Wu, M., Chang, L.-C., Walker, L., Lemaitre, H., Barnett, A.S., Marenco, S., and Pierpaoli, C. (2008) Comparison of EPI Distortion Correction Methods in Diffusion Tensor MRI Using a Novel Framework. MICCAI 2008, Part II, LNCS 5242, pp. 321-329.

3. C. Pierpaoli, L. Walker, M. O. Irfanoglu, A. Barnett, P. Basser, L-C. Chang, C. Koay, S. Pajevic, G. Rohde, J. Sarlls, and M. Wu. (2010) TORTOISE: an integrated software package for processing of diffusion MRI data. In ISMRM 18th Annual Meeting and Exhibition, Stockholm, Sweden. May 1-7, 2010.

4. Veraart, J.; Novikov, D.S.; Christiaens, D.; Ades-aron, B.; Sijbers, J. & Fieremans, E. Denoising of diffusion MRI using random matrix theory. NeuroImage, 2016, , doi: 10.1016/j.neuroimage.2016.08.016.

5. Kellner, E., Dhital, B., Kiselev, V. G. and Reisert, M. (2016), Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med., 76: 1574–1581.

6. Irfanoglu MO, Modi P, Nayak A, Hutchinson EB, Sarlls J, and Pierpaoli C. DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions. NeuroImage 106 (2015) 284–299.

7. Özarslan E, Koay CG, Shepherd TM, Komlosh ME, Irfanoglu MO, Pierpaoli C, and Basser PJ. (2013) Mean Apparent Propagator (MAP) MRI: a novel diffusion imaging method for mapping tissue microstructure. Neuroimage 78:16-32. 8.

8. Jesper L. R. Andersson and Stamatios N. Sotiropoulos. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 125:1063-1078, 2016.

9. Mark S. Graham, Ivana Drobnjak and Hui Zhang. Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques. NeuroImage, 125:1079-1094, 2015.

Table 1. The major improvements and new functionalities in the TORTOISEv3 pipeline.