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Abstract #3540

TORTOISE v3: Improvements and New Features of the NIH Diffusion MRI Processing Pipeline

Mustafa Okan Irfanoglu1,2,3, Amritha Nayak1,2,3, Jeffrey Jenkins1,2, and Carlo Pierpaoli1,2

1Quantitative Medical Imaging Section, NIBIB/NIH, Bethesda, MD, United States, 2SQUITS/NICHD/NIH, Bethesda, MD, United States, 3Henry Jackson Foundation, Bethesda, MD, United States

Here we present a series of improvements and new features of the TORTOISE diffusion MRI data processing software ( 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.

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