Inter-Subject MRI Co-Registration and ROI Analysis: MRIStudio

Developed by the Laboratory of Brain Anatomical MRI, these tools can process DTI data and reconstruct three-dimensional fiber trajectories. Please use this website's sidebar menu to find the following:

• MRIStudio: This MRIStudio software package incorporates the former DTIStudio, which can be used to load images, rotate volumes, create color maps, find tracts through a region of interest, etc.
• Platform: Windows
• Supported Data Types: GE, PHilips, Siemens, Toshiba, DICOM, raw, Analyze, NIfTI, NRRD

Drs. Bennett Landman and Jonathan Farrell have also developed software for analyzing DTI data:

• CATNAP: Coregistration, Adjustment, and Tensor-solving, a Nicely Automated Program
• The BIRN site displays details about this particular tool, as well.

• Simulate CEST Z-spectra using Bloch Equations: Users can input several properties of the water pool, solute pool, and experimental parameters, then see how each variable affects the the Z-spectra.
• Platform: Web-based (runs online using python)
• Supported Data Types: Users input individual parameters to create a simulation

QSM Data Processing Tools

The QSM Data Processing Tools were developed for doing Quantitative Susceptibility Mapping (QSM) analysis using MR magnitude and phase data acquired using gradient echo (GRE) sequence. Two packages are currently available:

• JHU/KKI QSM Toolbox V3.0:  a Matlab based software package with GUI developed by Dr. Xu Li, Dr. Jiri van Bergen, Dr. Lijun Bao and others for doing QSM. It has different pipelines developed by us and other groups for doing multi-step QSM , including phase unwrapping (Laplacian/Path/Nonlinear fitting), background field removal (PDF/VSHARP/LBV/iRSHARP) and QSM dipole inversion (TKD/iTKD/iLSQR/MEDI/SFCR).
• Supported Data Types: GE, Philips, Siemens, Par/Rec, DICOM, Enhanced DICOM.
• A PDF manual is included.
• Two example datasets (1 mm isotropic, whole brain coverage, 6echoes with TE1/deltaTE/TR=6/6/46ms) acquired at 3T Philips scanner are included for testing.
• Note: this toolbox uses a processing routine from FSL, thus FSL must be installed.
• iRSHARP Background Field Removal Tool: Matlab scripts developed by Dr. Lijun Bao for background field removal with an example data set.

Learned Proximal Convolutional Neural Network (LPCNN) for QSM

The LPCNN was developed by Kuo-Wei Lai, Dr. Xu Li and Dr. Jeremias Sulam, for solving the ill-posed dipole deconvolution problem in Quantitative Susceptibility Mapping (QSM) as detailed in (MICCAI paper). By integrating proximal gradient descent with deep learning, it is the first deep learning based QSM method that can handle an arbitrary number of phase input measurements. The official implementation of LPCNN network can be found on GitHub, which includes QSM training datasets (n=8, with local phase data acquired at 7T and 4-5 orientations COSMOS) and PyTorch implementations of LPCNN that offer the following functions:

• Create default training dataset including patched local phase image and QSM target pairs
• Conduct single or multiple orientation dipole deconvolution training using LPCNN
• Reconstruct QSM maps with user’s own data using trained LPCNN model

Blood T1, T2, Hct and Oxygenation Calculator

This tool was developed based on recently published (1,2) integrative models to describe the relationship between blood relaxation time (T1 and T2) and the blood physiological parameters (Hct and Oxygenation). This tool is provided through a webpage and offers the following service: 

• Predict T1 and CPMG-based T2 of the blood based on filed strength,  inter-echo spacing in CPMG experiment, Hct and blood oxygenation.
• Predict Hct based on field strength and blood T1.  
• Predict Oxygenation based on filed strength,  inter-echo spacing in CPMG experiment and blood T2.

1. Quantitative Theory for the Transverse Relaxation Time of Blood Water, Wenbo Li, Peter van Zijl, NMR in Biomedicine, 2020 Feb 5:e4207 [Epub ahead of print], PMID: 32022362
2. Quantitative Theory for the Longitudinal Relaxation Time of Blood Water, Wenbo Li, Ksenija Grgac, Alan Huang, Nirbhay Yadav, Qin Qin, Peter van Zijl, MRM, 76:270, PMID: 26285144

These software packages were developed by the Center for Imaging Science at Johns Hopkins University.

• CAportal: The Computational Anatomy Portal: The Computational Anatomy Portal provides access to the Atlases, Pipelines, Projects and Tools developed at the Johns Hopkins University Center for Imaging Science. Links are also provided to Computational Anatomy Processing Portals.
• Large Deformation Diffeomorphic Metric Mapping (LDDMM) Suite: These algorithms incorporate the volume, surface, similitude, landmarks, and DTI information in a dataset.
• CIS Software: Other packages from the CIS include a Rigid Landmark Matching (RLM) tool, CAWorks, BrainWorks, and MindView.
• Overall Platforms: Linux, Windows, Mac OSX
• Supported Data Types: DICOM, raw, Analyze, NIfTI

XNAT is an open source imaging informatics platform, developed by the Neuroinformatics Research Group at Washington University. XNAT can be used to support a wide range of imaging-based projects to facilitate common management, productivity, and quality assurance tasks for imaging and associated data. The Center for Imaging Science presented a poster in 2011 with additional information for accessing XNAT in CA Works.

• Accessing XNAT in CA Works: Accessing XNAT through CA Works to support imaging-based projects
• Platforms: Linux, Windows, Mac OSX
• Supported Data Types: VTK MultiBlock

These software packages were written by members of the Kirby Center and their collaborators. They are available for use on systems associated with the F. M. Kirby Center and the JHU Magnetic Resonance Division. Registered users may download them for free. 

• CSX 
  The CSX program was developed for the display and analysis of in vivo spectroscopy and spectroscopic imaging datasets.
• CSX: First Generation CSX
• Initially developed in the OpenWindows environment of Sun Microsystems SunOS 4.x on Sparc computers.
• Code compiled for Silicon Graphics IRIX6 system.
• CSX2: Second Generation CSX
• Built for Sun UltraSparc Solaris 2.x and Linux systems
• CSX3: Third Generation CSX
• Built for Sun UltraSparc Solaris 2.x and MacOSX systems
• Platforms: Unix, MacOSX
• Supported Data Types: GE, Philips, Siemens, DICOM, raw
• IMAX 
  The IMAX program displays images, and is to be used in conjunction with CSX. This program allows users to overlay images with MRSI "grids," with a "point-and-click" selection of spectra from regions of interest. Regions of interest can also be measured, added, subtracted, or combined. IMAX supports the following file formats: General Electric SIGNA (3.X, 4.X, or 5.X image files), DICOM, and SMIS (.sur). A general import tool allows the importing of data in other formats, such as ASCII, short integer, integer, floating point, double, and any header offset or matrix size.
• DSX 
  The DSX program combines the functionality of CSX and IMAX. It was based on the CSX processing and analysis code, but has a new graphical user interface (GUI). It was developed for x86 Windows and Linux systems. Developed by Drs. Peter Barker and David Bonekamp at Johns Hopkins Hospital in Baltimore, MD, USA.
• Platforms: X86 Windows, Linux
• Supported Data Types: GE, Philips, Siemens, DICOM, raw
• Gannet: GABA-Edited MRS Analysis
  This software package was written by Richard Edden, PhD and John Evans, PhD of Cardiff University, UK. Gannet is a batch analysis tool for GABA-edited MRS, written in MATLAB (so it can be implemented in any operating system, so long as MATLAB is installed with Optimization and Statistics tollboxes). It is freely available to registered users through the blog http://gabamrs.blogspot.com.
  • Platform: Cross-Platform in MATLAB
  • Supported Data Types: GE, Philips, Siemens
• Osprey (Open-Source Processing, Reconstruction & Estimation of MRS Data)
  Osprey is an all-in-one software suite for state-of-the art processing and quantitative analysis of in-vivo magnetic resonance spectroscopy (MRS) data.
• Platform: Cross-Platform in MATLAB
• Supported methods: Conventional MRS (STEAM, PRESS, semi-LASER, LASER, MEGA editing, Hadamard-encoded editing (HERMES, HERCULES)

Statistical Analysis: SMART Software and Tutorials

Downloadable Documents