Main article: DTI Pipeline.
The MIPAV DTI pipeline allows the user to upload and process diffusion weighted images (DWIs), and then it computes voxel-wise diffusion tensors (DT) for the further |analysis of diffusion tensor imaging (DTI) data. It determines an anatomical correspondence between DTI and structural MRI images (T2) of the same sample. As an output, the pipeline computes maps of diffusion eigenvalues and eigenvectors and creates visualization of fibers tracts going through a specific VOIs.
The MIPAV DTI Pipeline interface contains 6 tabs:
This panel is for uploading DW and T2 images, calculating gradients,uploading B-matrix information, and creating the B-matrix/Gradient table. DTI Pipeline reads raw data in all MIPAV supported formats and DICOM files.
DWI Image Browse – check this option if you would like to upload your image of interest from your computer.
Use Active DWI Image – check this option to use an active image, which is already opened in MIPAV.
Note: in MIPAV, an active image is the one that has a red frame.
This option allows a user to manually upload B-Value/Gradient File or B-Matrix file. See also: Table Options.
Gradient vector file is a 3D set of coordinates representing the direction (on the form of a vector) along which a magnetic gradient has been applied. Each DTI pulse sequence has a set of gradient vectors associated with it. This set is required for subsequent analysis.
B-value is a scaling factor used to describe the attenuating effect of the MR signal on the different gradients. A diffusion gradient can be represented as a 3D vector, where the direction of vector is in the direction of diffusion and its length is proportional to the gradient strength. The gradient strength, or more often used diffusion weighting parameter term, is expressed in terms of the B-value parameter, which is proportional to the product of the square of the gradient strength (q) and the diffusion time interval (b ~ q2 • Δt). Read more: MRI TIP database.
The Philips Gradient Creator computes the correct gradient table which is necessary to calculate diffusion tensors. The table lists gradient vectors that describe the diffusion weighting directions, which are later used for analysis and computing diffusion tensor. The Gradient Creator works with data acquired by Philips MRI scanners. Note: the Philips Gradient Creator options would not appear for images from other scanners.
It uses the source code from DTI_gradient_table_creator created by Jonathan Farrell, Ph.D.
The code that computes the gradient table based on minimal user input. The list of input parameters is show below, see Gradient Creator input parameters. You can also refer to Jonathan Farrell's web site for additional information.
The specifications of the gradient table created for the image taken on Philips MRI scanner(s) are determined by:
In diffusion tensor imaging (see [1]), tensors are constructed by collecting a series of direction-sensitive diffusion images. MRI scanners save these directions along with the images and later they are used to reconstruct the diffusion properties of the images.
Depending on the scanner, the vectors are recorded with reference either to the scanner bore, or to the imaging grid. This should not a problem if the images are acquired precisely orthogonal to the scanner bore, because in that case the image and the scanner share the same frame of reference.
Problems can arise in oblique acquisitions when the image plane is not aligned with the scanner bore. In this situation, it is important that the gradient vectors used in the imaging software are in the same frame of reference as the image. This requires conversion or re-ordering, otherwise we will get angulation errors.
Note: these angulation errors have little influence on the DTI parameters, which are invariant to tensor rotation - ADC, MD and FA. However, they do affect calculation of the eigenvectors of the tensor.
In the Gradient or B-Matrix table, if gradients are displayed, the 2nd column contains B values, while X Gradient, Y Gradient and Z gradient columns contain the diffusion gradients applied along the x, y, and z axis - Gx, Gy, and Gz correspondingly.
If B-matrix is displayed, 6 b-values are displayed per volume including bxx, byy, bxy, bxz, byz, and bzz.
Gradient Creator Parameters | Options | Philips PAR/REC Version |
---|---|---|
Fatshift | R: Right L: Left A: Anterior P: Posterior H: Head F: Feet |
V3 & V4 V4.1 & V4.2 |
Jones30 or Kirby | Specify which MRI scanner was used to acquire DW images. This option works for KKI scanners only. | V3 & V4 V4.1 & V4.2 |
Gradient Resolution | Low: used for 8 DWI volumes Medium: used for 17 DWI volumes High: used for 34 DWI volumes |
V3 & V4 V4.1 & V4.2 |
Gradient Overplus | Yes No |
V3 & V4 V4.1 & V4.2 |
Philips Release | Rel_1.5 Rel_1.7 Rel_2.0 Rel_2.1 Rel_2.5 Rel_11.x |
V3 & V4 V4.1 & V4.2 |
Patient Position | Head First Feet First |
V3 & V4 |
Patient Orientation | SP: Supine PR: Prone RD: Right Decubitus LD: Left Decubitus |
V3 & V4 |
Fold Over | AP: Anterior-Posterior RL: Right-Left FH: Head-Feet/ Superior-Inferior |
V3 & V4 |
OS (Operating System) | Windows VMS |
V3 & V4 V4.1 & V4.2 (for KKI scanners only) |
Inverted | No Yes |
V3 & V4 V4.1 & V4.2 (for KKI scanners only) |
Compute Gradient Table - use this button to compute the gradient table based on data you entered into Gradient Input Parameters.
"Gradient Table Creator Image dimensions 17 are not consistent with gradient table choice - exacted 8 dimensions" or a similar message means that Gradient Resolution (e.g. Low, Medium, High) did not correspond to the volume number of the image you uploaded to the pipeline. In this example, the “Gradient Resolution” was set to “Low” which corresponds to 8 volumes/dimensions, while the image had 17 volumes. In order to calculate the gradient table, we needed to change the “Gradient Resolution” parameter to “Medium".
"Gradient Table Creator R: is not consistent with Anterior-Posterior fold over" or a similar message means that the Fold Over option selected does not match the image specification. In order to solve that problem, select the correct fold over option.
The Table Options box allows the user to directly edit the values in the the Gradient table and to save the updated table. To edit the table, press the Edit button. To save the table, press Save Table As. The table could be save in various formats, including Plain text (.txt), FSL, dtistudio and MIPAV standard format.
When a user entered all parameters needed for the Philips Gradient Table Creator, the Apply Table button becomes available to apply the changes and save the DTI parameters in the file header (if the file is saved in MIPAV XML format).
For more information regarding the importing DW images, refer to Jonathan Farrell's web site - http://godzilla.kennedykrieger.org/~jfarrell/software_web.htm.
The gradient and B-matrix table files are the output files from the Import Data tab. They can be saved in a user specified folder in different formats, e.g. dtistudio, FSL, or MIPAV XML format.
Note: Siemens Mosaic 3D DICOM files if used as an input, would be saved as 4D volumes based on corresponding B-matrix volumes.
In this tab, the user can specify parameters for running OAR 3D (or OAR 3.5D) algorithm that will perform the rigid registration of B0 to T2 and then DWI to rigidly aligned B0. OAR 3.5D is used to correct the spatial mis-registration of DWI volumes originating from both - subject motion and eddy current-induced distortions. The user has a choice to run OAR 3D or OAR 3.5D. For more information about both algorithms, refer to the OAR documentation.
Note: if a used doesn't have a T2 image, the registration could still be performed based on the B0 volume, but the user choice would be limited by OAR 3.5 D algorithm only.
The output files are as follows:
Clinical diffusion MR studies are mostly based on single-shot echo-planar imaging (EPI) acquisitions. This method is very sensitive to static magnetic field inhomogeneities and artifacts, which appear due to imperfection of the gradient waveforms, and eddy currents during the long readout time. These issues are primarily responsible for creating nonlinear geometric distortion along the phase-encoding direction. Artifacts often appear at air-tissue border and also in the images of the ventral portions of the frontal and temporal lobes. They become even more severe with increasing magnetic field. The mis-registration among a set of DWI volumes due to geometric distortions leads in turn to spatial inaccuracies in the derivation of the diffusion tensor, ADC, and FA since they are typically computed on a pixel-by-pixel basis combining all diffusion-weighting directions.
To correct geometric distortions due to B0 inhomogeneities, MIPAV uses the special image registration technique, where the distorted EPI image is registered to a corresponding anatomically correct MR image with an intensity based least-squares similarity metric and the VABRA modeled deformation field to improve the sensitivity in areas of low EPI signal.
All fields except "Compute deformation field" are populated automatically with data from the Pre-processing tab. However, the user has the option to manually upload previously saved files.
VABRA note: VABRA stands for Vectorized Adaptive Bases Registration Algorithm - a deformable registration algorithm, which is an intensity-based nonrigid registration algorithm. As an output, it returns a registered image and the applied deformation field. For more information, refer to the VABRA web site.
MIPAV API note: for API information regarding MIPAV VABRA algorithm, refer to VABRA API.
After computing the deformation field, MIPAV creates the following files:
They make the input for the EPI distortion correction algorithm.
This module estimates the diffusion tensor model from a given DWI Volume. The result is a tensor volume that can be used to compute different anisotropy measurements, for example Fractional Anisotropy, and also perform fiber tractography.
The following algorithms (available via the DTI algorithm drop-down menu) are used in the pipeline:
Weighed, noise-reduction - this is the default method used in MIPAV DTI pipeline. It is based on the book by S. Mori, "Introduction to Diffusion Tensor Imaging".
LLMSE - is based on CATNAP pipeline.
The following 3 methods have been ported from Camino. Camino is a fully-featured toolkit for Diffusion MR processing and reconstruction. For more information, refer to: Cook et al (2006).
CAMINO: Linear - this algorithm uses unweighted linear least-squares to fit the diffusion tensor to the log measurements. The algorithm is based on the method explained in the paper by Basser P.J., Mattielo J., and Lebihan D. (1994).
CAMINO: Non-linear - this method provides more accurate noise modelling than Linear fitting. The algorithm is based on the method explained in papers by Jones and Basser (2004) and Alexander and Barker (2005).
CAMINO: Restore - this algorithm fits the diffusion tensor robustly in the presence of outliers, for more information refer to the CAMINO Tutorial. The algorithm is based on the method explained in the paper by Chang, Jones and Pierpaoli (2005).
CAMINO: Weighed linear - this algorithm uses a weighted linear fit, as originally proposed by Jones and Basser (2004).
The following output options available for all tensor estimation algorithms:
The following output options available only for tensor estimation algorithms based on CAMINO code:
The user could use the following check boxes to specify what kind of images they want to create/display.
Output options | |||||
---|---|---|---|---|---|
Create ADC image | Display ADC image | ||||
Create Color image | Display Color image | ||||
Create Eigen Value image | Display Eigen Value image | ||||
Create Eigen Vector image | Display Eigen Vector image | ||||
Create RA image | Display RA image | ||||
Create FA image | Display FA image | ||||
Create Trace image | Display Trace image | ||||
Create VR image | Display VR image |
Please refer to the GPU computing page in order to learn how to enable MIPAV GPU Volume Renderer required for DTI visualization.
This is the final step of the MIPAV DTI pipeline. It allows the user to specify Volumes of Interests (VOIs) and to display fibers tracts going through a specific VOI. The users can also seed individual fiber tracts based on mouse clicks. In visualization, local diffusivity of the white matter is represented by glyphs including ellipses cylinders, lines, tubes, and arrows.
In this panel, the user is asked to upload the following images created in the Tensor Statistics panel: tensor image, color image, EVector image, FA image EValue image, and T2 image, which is the original T2 image. Note that in the most cases, MIPAV will upload these images automatically.
After uploading all images, press the Load button to start visualization.