Cross-platform overlay tool to superimpose DTIStudio fiber tracking results on
of Platonic Solids –Toolbox for representation and tessellation of platonic
¨Philips PAR/REC File Format
Toolbox – Toolbox for reading, writing and
converting the Philips research file format to/from Analyze files (Matlab)
¨MIPAV Philips PAR/REC File Format
(JAVA) – Native support for PAR/REC within MIPAV
and the MIPAV development environment.
¨DTI_gradient_table_creator – Function to determine the diffusion weighting directions
relative to the voxel ordering for DTI experiments on Philips MRI scanners
¨Evaluation of DTI Methods – An open framework to evaluate tensor fitting methods
ViPAR (Visualization, Paint,
Allignment and Rotation) is a MRI visualization and manipulation tool that
enables real-time 3D transformation of MR volumes and delineation of regions of
interest on arbitrary planes. It is available as a plugin to MIPAV.
Adjustment, and Tensor-solving – a Nicely Automated Program) is an end to end
data processing pipeline for Philips PAR/REC files. CATNAP performs motion
correction for both diffusion and structural images using FSL FLIRT, adjusts
the diffusion gradient directions for scanner settings and motion correction,
and estimates tensor and derived quantities. The results are readily compatible
with DTIStudio, FSL, and other tensor analysis packages.
DTI-SPOT (DTI Superposition Of Tracts) is a cross-platform user
interface for superimposing DTIStudio filber tracking results on 3D PAR/REC and
Analyze Volumes. Combined images are available in DICOM and Analyze (RGB)
format and are readily compatible with general purpose DICOM visualization
Notes: If you find this software
useful, please let me know [landman (at) jhu.edu].
Platform: Matlab (tested on v
Philips PAR/REC File Format Toolbox
The PAR/REC file format toolbox enables robust parsing of
Philips MRI research file format volumes. As of this beta release, PAR versions
3 and 4 are supported. Direct visualization is integrated with the MRI_TOOLBOX
(Darren Webber). Multi-volume
and arbitrary slice ordering are correctly handled. PAR/REC files may be
written to or generated from Analyze format.
Functions of particular interest:
load PAR/REC files
Parse a par header into a maltab structure
Convert par/rec to analyze
Convert Analyze files to a par/rec
Save a Matlab structure to a par/rec file
Save a Matlab header to a par file
scripts containing “REC” have alternate versions with “fREC”. This file
format is identical to PAR/REC with the exception that the REC file is 32
bit floating point rather than 16 bit unsigned integer. The scaling
defined in the PAR file is automatic.
toolbox supports either par/rec or PAR/REC, but both the par and rec must
have the same capitalization structure.
par files generated by this program have “v2” appended to the end to
indicate that they were not generated by a Philips scanner. We attempt to
exactly preserve the format structure, but the formatting of the numbers
may be different (e.g., 0.00 versus 0).
other functions have been included in this toolbox that relate to various
MR formats. Please let me know if you can comment on any features, bugs,
or general concerns.
1.1 includes support for MIPAV’s minimal PAR/REC file format.
The PAR/REC file format support enables robust parsing of
Philips MRI research file format volumes. PAR versions 3 and 4 are supported.
Multi-volume with linear slice ordering is correctly handled. All files must be
Support has been release for internal use and will soon be
DTI_gradient_table_creator determines the correct gradient table for
a given DTI acquisition on Philips MR units.The code computes the gradient table based on minimal user input, and
provides a quick and easy method to determine the correct gradient table necessary
to calculate diffusion tensors (i.e., for FA, Colormaps, fiber tracking etc.)
Status: Public Release. Comments welcome. [Download]
Platform: Matlab (tested on v 7.0+)
Systematic Evaluation of Linear and Nonlinear
DTI Estimation Methods: An Open Framework
basic premise of DTI is that the tensor formalism (e.g., assumption of Gaussian
diffusion) meaningfully represents diffusion processes, and thus the derived
contrasts are relevant. Yet, not all tensors represent physically possible
processes (e.g., those with negative eigenvalues); typical log-linear mean
squared error methods can result in these non-physical solutions. Various
non-linear tensor estimation frameworks have been developed to prevent these
problems (Tschumperlé and Deriche 2003; Cox and Glen 2006; Niethammer, Estepar
et al. 2006), while regulation and robust tensor estimation methods employ
spatial correlations to lessen the effects of noise (Mangin, Poupon et al.
2002; Chang, Jones et al. 2005). Despite the emergence of new methods, little
evidence has been presented to provide equivalent experimental comparisons or
enable well-informed selection of the appropriate tensor estimation method for
a particular task.