JIST-CATNAP (Coregistration, Adjustment, and Tensor-solving – a Nicely Automated Program) is an end to end data processing pipeline. Although [initially developed] in Matlab for Philips PAR/REC Magnetic Resonance data files, this Java-based port for CATNAP supports the data formats supported by MIPAV. CATNAP performs motion correction for both diffusion and structural images using FSL FLIRT, adjusts the diffusion gradient directions for scanner settings (i.e., slice angulation, slice orientation, etc.) and motion correction (i.e., the rotational component of the applied transformation), and computes tensor and derived quantities (FA, MD, colormaps, eigenvalues etc) . The results are readily compatible with DTIStudio, FSL, and other tensor analysis packages.
JIST-RESTORE (Robust estimation of tensors by outlier rejection) is a popular method of tensor estimation. Here we show how the implementation from CAMINO can be used directly within JIST. http://www.cs.ucl.ac.uk/research/medic/camino/

JIST-CFARI The characterization of multi-fiber structures within a voxel has potential for broad clinical applications in advanced fiber tracking methods and tissue classifications approaches. Detailed characterization of this structure with q-ball [1] or diffusion spectrum imaging [2] is severely limited by practical considerations such as long scan times, low signal-to-noise ratio (SNR), and hardware constraints. Although typically considered an image reconstruction technology, compressed sensing is a promising technique for the identification of diffusion-inferred intra-voxel structure (e.g., Crossing Fiber Angular Resolution of Intra-voxel structure, CFARI [3,4]). Notably, compressed sensing requires less data (and thus less scan time) than q-ball imaging and can utilize typical diffusion tensor imaging (DTI) acquisitions. Preliminary software is explained here. The implementation is being improved as we prepare our journal publication.

Robust Estimation of Spatially Variable Noise Fields provides a simple mechanism to robustly estimate the voxel-wise noise level through limited repeated acquisitions of multiple contrast modalities. For example, this method would apply if two repetitions of DTI scans were acquired with the same gradient table. There would only be one pair of images for each diffusion weighted direction. However, this method can use the set of image pairs to accurately estimate the complete field.

T1 Mapping with Double Angle Experiment provides the functionality to estimate T1 tissue properties from 3D-GRE experiments.
T2 Mapping with Dual Echo Experiment provides the functionality to estimate T2 tissue properties from two echoes of a mutli-slice spin echo experiment.

Multi-Modal Cruise is a work in progress. Please stay tuned as we test our wiki-fying skills. --Bennett 05:07, 8 November 2009 (UTC)

JIST Development

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