Difference between revisions of "DTI Pipeline"

Latest revision as of 16:43, 29 May 2012

Diffusion tensor imaging (DTI) analyzes the tissues that have an internal fibrous structure, which is analogous to the anisotropy of some crystals (e.g.neural axons of white matter or muscle fibers in the heart). In those tissues, the diffusion of water displays anisotropy in certain directions. That means that molecules of water diffuse more rapidly in the direction aligned with the fibrous structure, and more slowly in the direction perpendicular to it. Thus, the measured rate of diffusion will differ depending on the fiber direction and an observer's point of view.

In diffusion-weighted imaging (DWI), at least 3 gradients are applied in 3 different directions, which is sufficient to estimate the trace of the diffusion tensor. From the diffusion tensor, diffusion anisotropy measures such as the fractional anisotropy (FA) eigenvalues and eigenvectors can be computed and displayed. The principal direction of the diffusion tensor can be also used to visualize the white matter connectivity of the brain. DTI has been proven to be very useful to diagnose vascular strokes in the brain and in other clinical applications.

Note: this diffusion model is a rather simplified model of the diffusion process. It assumes that diffusion within each image voxel is linear and homogeneous.

Introduction

The DTI pipeline of MIPAV prepares diffusion weighted images (DWIs) and computes voxel-wise diffusion tensors (DT) for the further analysis of diffusion tensor imaging (DTI) data, see MIPAV DTI Color Display. The pipeline computes maps of diffusion eigenvalues and eigenvectors. It also determines an anatomical correspondence between DTI and structural MRI images (T2) of the same sample.

MIPAV DWI pipeline overview

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MIPAV DTI Pipeline schematic

Brain MRI introduction

Brain MRI is usually performed with a 1.5- or 3- T MRI machine, with a gradient strength in the range of 20-60 mT/m and a slew rate of 120 T/m/s. Parameters for a single-shot spin-echo echo-planar imaging (EPI) sequence include: a repetition time (TR) of 6000 ms, an echo time (TE) of 100 ms, a field of view (FOV) of 24 cm. These parameters are typically used to obtain 3- to 5-mm axial or coronal sections with a 5-mm intersection gap. The acquisition matrix is usually 96 × 96 with a reconstruction matrix set to 128 × 128. The DWIs are obtained by using 4 linearly increasing b values in 6-7 non-collinear directions (bmax ~ 703-1000 s/mm2). In addition, a T2-weighted (T2W) reference image is obtained without diffusion weighting. Read more: [1], [2].

Determining an anatomical correspondence

between DTI and structural MRI images of the same sample. In MIPAV we use an image-based registration scheme mainly because i) it doesn't require a field map, which is usually not available for DWIs, and ii) it allows one to correct for artifacts produced by a patient motion. For more information, refer to [3], [4].

At least one of the following images is required as a reference in DTI pipeline:

T2 image - MIPAV uses the T2 as a reference image because it is usually less distorted and has a higher signal-to-noise ratio (SNR) than DWIs.

B0 - which is a DWI volume with no gradient applied. It can be used instead of T2, if T2 is not available.

MIPAV aligns all other images to the reference image (either T2 or B0 volume) by optimizing the cost function, which represents the measure of how well the images are spatially aligned.

Note: In MIPAV, we use the term "cost function" to refer to the negative cost function.

MIPAV DTI pipeline outline

The user uploads a DWI image and T2 image to the pipeline using the Import Data panel. A DWI image can be acquired from many different MRI scanners (including Philips, Siemens, GE, etc.) and in various formats. MIPAV reads the gradient information from the image header, or from the B-matrix file uploaded by the user. The gradient (or B-matrix) information is then displayed in the Gradient table. For the list of image types and how MIPAV reads the header information, refer to Image Types section.
In the Pre-processing step, the B0 volume in DWI is detected (or entered by user), and then rigidly aligned to the T2 image. The DW image is then registered to rigidly aligned B0 using the Optimized Automatic Registration 3.5 D algorithm. These steps are necessary to perform a motion correction and eddy current distortion correction.
In the EPI Distortion Correction step, MIPAV calculates deformation vector fields for rigidly aligned B0 and T2, which came from the Pre-processing step. MIPAV then uses both: the transformation matrices obtained in the Pre-processing step, and deformation vector field values to create a corrected DWI image.
MIPAV then calculates a diffusion tensor using pre-processing DWI and the gradient/B-value information and a mask image uploaded by the user.
MIPAV uses the tensor information to create a tensor statistics, including ADC, color map, eigenvalue, eigenvector, FA, RA, and volume ratio images.
MIPAV creates a 3D visualization of fiber bundle tracts in the brain's white matter using the information from the previous step. The user can save fiber tracts information as .vtk and .dat files. See also: Image formats descriptions.

Image types

Image file type	Auto Population of Bvals	Auto Population of Gradients	Auto Population of Bmatrix	Philips Gradient Creator Utility
Philips PAR/REC V3 &V4	Yes	Yes	No	Yes
Philips PAR/REC 4.1 &4.2	Yes	Yes	No	Yes
Philips DCM V3 &V4	Yes	Yes	No	Yes
Philips DCM 4.1 &4.2	Yes	Yes	No	Yes
Siemens Mosaic DCM, see also output files	No	No	Yes	No
Nifti w/ Philips Par File	Yes	Yes	No	Yes
GE DCM	Yes	Yes	No	No
Text File Type	Auto Population of Bvals	Auto Population of Gradients	Auto Population of Bmatrix	Philips Gradient Creator Utility
fslBvalGrad.txt	Yes	Yes	No	No
dtiStudioBvalGrad.txt	Yes	Yes	No	No
mipavStandardBvalGrad.txt	Yes	Yes	No	No
dcm2nii.bvec	Yes	Yes	No	No
fslBmatrix.txt	No	No	Yes	No
mipavStandardBmatrix.txt	No	No	Yes	No

MIPAV DTI pipeline interface

Main article: DTI pipeline interface.

The MIPAV DTI Pipeline interface contains 6 tabs:

In the Import Data the user uploads DW and T2 image (optional), and the gradient information (optional). MIPAV could also read the gradient information from the image header. The Phillips Gradient Creator will then calculate the gradient table based on the information provided.
In the Pre-processing tab, the user is asked to specify the parameters required for aligning B0 to T2, and then DWI to the rigidly aligned B0, and also parameters for motion correction and eddy current distortion correction.
In the EPI Distortion Correction, the user enters parameters needed for calculation of deformation vector fields for rigidly aligned B0 and T2 from the Pre-processing step.
In the Tensor Estimation tab, the user uploads the mask image, selects the tensor estimation algorithm and specifies the output options.
In the Tensor Statistics the user can upload the tensor image and specify which kind of images (e.g. ADC, color map, Eigen value, Eigen vector, FA, RA, and Volume Ratio) he/she would like to have as an output.
In the Visualization, the user can upload images needed for a 3D visualization of fiber bundle tracts in the brain's white matter using the provided information. The user can then save fiber tracts information as .vtk and .dat files.

DTI Pipeline Tutorials

Refer to DTI Pipeline Tutorials.

TBD.

@@ Line 1: / Line 1: @@
-This article is  '''a stub'''. It needs improvement.
+Diffusion tensor imaging (DTI) analyzes the tissues that have an internal fibrous structure, which is analogous to the anisotropy of some crystals (e.g.neural axons of white matter or muscle fibers in the heart). In those tissues, the diffusion of water displays anisotropy in certain directions. That means that molecules of water diffuse more rapidly in the direction aligned with the fibrous structure, and more slowly in the direction perpendicular to it. Thus, the measured rate of diffusion will differ depending on the fiber direction and an observer's point of view.
+In diffusion-weighted imaging (DWI), at least 3 gradients are applied in 3 different directions, which is sufficient to estimate the trace of the diffusion tensor. From the diffusion tensor, diffusion anisotropy measures such as the fractional anisotropy (FA) eigenvalues and eigenvectors can be computed and displayed. The principal direction of the diffusion tensor can be also used [[DTI pipeline interface#VisualizationTab |to visualize the white matter connectivity of the brain]]. DTI has been proven to be very useful to diagnose vascular strokes in the brain and in other clinical applications.
+'''Note:''' this diffusion model is a rather simplified model of the diffusion process. It assumes that diffusion within each image voxel is linear and homogeneous.
 == Introduction ==
-The DTI pipeline of MIPAV prepares diffusion weighted images (DWI) and computes voxel-wise diffusion tensors (DT) for the further analysis of diffusion tensor imaging (DTI) data, see [[DTI Color Display| MIPAV DTI Color Display]]. The pipeline computes maps of diffusion eigenvalues and eigenvectors. It also determines an anatomical correspondence between DTI and structural MRI images of the same sample.
+The DTI pipeline of MIPAV prepares [http://www.mr-tip.com/serv1.php?type=db1&dbs=Diffusion%20Tensor%20Imaging diffusion weighted images (DWIs)] and computes voxel-wise diffusion tensors ([http://en.wikipedia.org/wiki/Diffusion_MRI#Diffusion_tensor_imaging DT]) for the further analysis of diffusion tensor imaging (DTI) data, see [[DTI Color Display| MIPAV DTI Color Display]]. The pipeline computes maps of diffusion [http://en.wikipedia.org/wiki/Diffusion_MRI#Measures_of_anisotropy_and_diffusivity eigenvalues] and [http://en.wikipedia.org/wiki/Diffusion_MRI#Measures_of_anisotropy_and_diffusivity eigenvectors]. It also determines an anatomical correspondence between DTI and structural MRI images (T2) of the same sample.
 == MIPAV DWI pipeline overview ==
-This section is correct.
 [[File:DtiPipelineWikiSmall.jpg‎ |400px|thumb|right|MIPAV DTI Pipeline schematic]]
@@ Line 10: / Line 15: @@
 Brain MRI is usually performed with a 1.5- or 3- T MRI machine, with a gradient strength in the range of 20-60 mT/m and a slew rate of 120 T/m/s. Parameters for a single-shot spin-echo echo-planar imaging (EPI) sequence include: a repetition time (TR) of 6000 ms, an echo time (TE) of 100 ms, a field of view (FOV) of 24 cm. These parameters are typically used to obtain 3- to 5-mm axial or coronal sections with a 5-mm intersection gap. The acquisition matrix is usually 96 × 96 with a reconstruction matrix set to 128 × 128. The DWIs are obtained by using 4 linearly increasing b values in 6-7 non-collinear directions (bmax ~ 703-1000 s/mm2). In addition, a T2-weighted (T2W) reference image is obtained without diffusion weighting. Read more: [http://www.ajnr.org/content/26/6/1455.full], [http://radiology.rsna.org/content/217/2/331.full].
-=== Determining an anatomical correspondence between DTI and structural MRI images of the same sample ===
+=== Determining an anatomical correspondence ===
+between DTI and structural MRI images of the same sample. In MIPAV we use [http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=941749&tag=1 an image-based registration scheme] mainly because i) it doesn't require [http://www.mr-tip.com/serv1.php?type=db1&dbs=High%20Field%20MRI a field map], which is usually not available for DWIs,  and ii) it allows one to correct for artifacts produced by a patient motion. For more information, refer to [http://ee.sharif.edu/~miap/Files/Medical%20Image%20Registration.pdf], [http://www.sciencedirect.com/science/article/pii/S0165027007002270].
-[http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=941749&tag=1 MIPAV uses  an image-based registration scheme] mainly because i) it doesn't require a field map, which is usually not available for DWIs,  and ii) it allows one to correct for mis-registration produced by a patient motion. For more information, refer to [http://ee.sharif.edu/~miap/Files/Medical%20Image%20Registration.pdf], [http://www.sciencedirect.com/science/article/pii/S0165027007002270].
+At least one of the following images is required as a reference in DTI pipeline:
-To measure how well the images are spatially aligned MIPAV uses one of [http://mipav.cit.nih.gov/documentation/HTML%20Algorithms/CostFunctions.html the cost functions], e.g., Correlation Ratio, Least Squares, Normalized Cross Correlation, or Normalized Mutual Information.
+*'''[http://www.mr-tip.com/serv1.php?type=db1&dbs=T2%20Weighted%20Image T2 image]''' - MIPAV uses the T2 as a reference image because it is usually less distorted and has a higher signal-to-noise ratio (SNR) than DWIs.
-A [http://www.mr-tip.com/serv1.php?type=db1&dbs=T2%20Weighted%20Image T2 image] (uploaded by a user) is chosen as a reference for all other images in the dataset. We use the T2 as a target image because it is usually less distorted and has a higher signal-to-noise ratio (SNR) than DWIs. Then, using a spatial transformation model, MIPAV aligns all other images to the target image by optimizing the cost function.
+*'''B0''' - which is a DWI volume with no gradient applied. It can be used instead of T2, if T2 is not available.
-'''Note:''' In MIPAV, we use the term cost function to refer to the negative cost function.
+MIPAV aligns all other images to the reference image (either T2  or B0 volume) by optimizing [[Cost functions used in MIPAV algorithms | the cost function]], which represents the measure of  how well the images are spatially aligned.
+'''Note:''' In MIPAV, we use the term "cost function" to refer to [[Cost functions used in MIPAV algorithms |the negative cost function]].
 === MIPAV DTI pipeline outline ===
 <ol>
-<li>A user uploads a DWI image and [http://en.wikipedia.org/wiki/Spin-spin_relaxation_time T2 image] to the pipeline using the Import Data panel. A DWI image can be acquired from many different MRI scanners (including [http://www.healthcare.philips.com/us_en/products/mri/ Philips], [http://www.medical.siemens.com/webapp/wcs/stores/servlet/CategoryDisplay~q_catalogId~e_-1~a_categoryId~e_12754~a_catTree~e_100010,1007660,12754~a_langId~e_-1~a_storeId~e_10001.htm Siemens], [http://www.gehealthcare.com/euen/mri/index.html GE], etc.) and in [[Supported Formats | various formats]]. MIPAV reads the gradient information from the image header, or from the B-matrix file uploaded by the user. The gradient (or B-matrix) information is then displayed in the Gradient table. For the list of image types and how MIPAV reads the header information, refer to [[#ImageTypes | Image Types]] section.</li>
+<li>The user [[DTI pipeline interface#ImportData | uploads a DWI image]] and [http://en.wikipedia.org/wiki/Spin-spin_relaxation_time T2 image] to the pipeline using the Import Data panel. A DWI image can be acquired from many different MRI scanners (including [http://www.healthcare.philips.com/us_en/products/mri/ Philips], [http://www.medical.siemens.com/webapp/wcs/stores/servlet/CategoryDisplay~q_catalogId~e_-1~a_categoryId~e_12754~a_catTree~e_100010,1007660,12754~a_langId~e_-1~a_storeId~e_10001.htm Siemens], [http://www.gehealthcare.com/euen/mri/index.html GE], etc.) and in [[Supported Formats | various formats]]. MIPAV reads the gradient information from the image header, or from the B-matrix file uploaded by the user. The gradient (or B-matrix) information is then displayed in the Gradient table. For the list of image types and how MIPAV reads the header information, refer to [[#ImageTypes | Image Types]] section.</li>
-<li>In the Pre-processing step, the B0 slice in DWI is detected, and then rigidly registered to the T2 image. The DW image is then registered to rigidly aligned B0 using the[[Optimized automatic registration 3D | Optimized Automatic Registration 3.5 D]] algorithm. These steps are necessary to perform a motion correction and eddy current distortion correction.</li>
+<li>In the Pre-processing step, the B0 volume in DWI is detected (or entered by user), and then rigidly aligned to the T2 image. The DW image is then registered to rigidly aligned B0 using the[[Optimized automatic registration 3D | Optimized Automatic Registration 3.5 D]] algorithm. These steps are necessary to perform a motion correction and eddy current distortion correction.</li>
 <li>In the EPI Distortion Correction step, MIPAV calculates deformation vector fields for rigidly aligned B0 and T2, which came from the Pre-processing step. MIPAV then uses both: the transformation matrices obtained in the Pre-processing step, and deformation vector field values to create a corrected DWI image.</li>
-<li>MIPAV then creates a tensor using pre-processing DWI and the gradient/B-value information  and a mask image uploaded by the user.</li>
-<li>MIPAV uses the tensor information to create a whole bunch of images, including ADC, color map, Eigen value, Eigen vector, FA, RA, and Volume Ratio.</li>
+<li>MIPAV then calculates a diffusion tensor using pre-processing DWI and the gradient/B-value information  and a mask image uploaded by the user.</li>
-<li>MIPAV creates a 3D visualization of fiber bundle tracts in the brain's white matter using the information from the previous step. The user can save fiber tracts information as <span style="font-family:courier">[[Image formats descriptions#VtkXml |.vtk]]</span> and [http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Diffusion .dat]</span> files.
+<li>MIPAV uses the tensor information to create a tensor statistics, including ADC, color map, eigenvalue, eigenvector, FA, RA, and volume ratio images.</li>
+<li>MIPAV creates a 3D visualization of fiber bundle tracts in the brain's white matter using the information from the previous step. The user can save fiber tracts information as <span style="font-family:courier">[[Image formats descriptions#VtkXml |.vtk]]</span> and <span style="font-family:courier">[http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Diffusion .dat]</span> files. See also: [[Image formats descriptions]].
 </ol>
@@ Line 65: / Line 78: @@
 |Yes
 |-
-|Siemens Mosaic DCM
+|Siemens Mosaic DCM, see also [[#ImportDataOutput | output files]]
 |No
 |No
@@ Line 78: / Line 91: @@
 |-
 |GE DCM
-|In progress
+|Yes
-|In progress
+|Yes
-|In progress
+|No
-|In progress
+|No
 |-
 !align="left"|Text File Type
@@ Line 127: / Line 140: @@
 |}
-== MIPAV DTI Pipeline dialog box==
+== MIPAV DTI pipeline interface ==
-=== Import Data tab ===
+''Main article: [[DTI pipeline interface]].''
-This tab is for uploading DW and T2 images, calculating B-values and creating B-matrix (Gradient) table.
-==== Upload DWI Image box ====
+The MIPAV DTI Pipeline interface contains 6 tabs:
-DTI Pipeline reads raw data, all MIPAV supported formats and [http://medical.nema.org/ 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, [[Opening and loading image files#ActiveImage | an active image]] is the one that has a red frame.
-==== Upload B-Value/Gradient File or B-Matrix file ====
-This options allows a user to manually upload B-Value/Gradient File or B-Matrix file.
-<blockquote>'''Introducing B-value:''' - a diffusion gradient can be represented as a 3D vector q. The direction of vector q 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, is s 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: [http://www.mr-tip.com/serv1.php?type=db1&dbs=B-Value MRI TIP database].</blockquote>
-=== Gradient creator ===
-[[File:GradientTable.jpg|200px|thumb|right|The Gradient or B-matrix table]]
-The 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.
-It uses the source code from [http://godzilla.kennedykrieger.org/~jfarrell/software_web.htm DTI_gradient_table_creator] created by [http://godzilla.kennedykrieger.org/~jfarrell/index.htm 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 [[#GradientCreatorInput | Gradient Creator input parameters]]. You can also refer to [http://godzilla.kennedykrieger.org/~jfarrell/software_web.htm 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:
-*The 3 coordinate frames defined on the MR scanner: world, anatomical, and image coordinate system. For more information, refer to [http://www.slicer.org/slicerWiki/index.php/Coordinate_systems Slicer WIKI].
-*The rules on how imaging options (including slice orientation, [[#SliceAngulation | slice angulation]], phase encoding direction, etc.) impact the gradient directions.
-*The scanner software release.
-*Any motion correction, if performed.
-<div id="SliceAngulation"></div>
-==== Slice angulation ====
-[[File:SliceAngulationProblem.jpg|200px|thumb|right|Slice angulation: (a) - the image was acquired orthogonal to scanner bore; (b) -  the image was acquired with slice orientation rotated relative to the scanner bore (a roll has been applied in this example). Image courtesy: Chris Rorden, Michael Harms, Jolinda Smith, Nytavia Wallace.]]
-In diffusion tensor imaging (see [http://unfweb.unf-montreal.ca/jdoyon/cours_6032/Journal%20of%20Mag%20Res%20Imaging%202001.pdf]), 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 to 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.
-==== The Gradient or B-Matrix table ====
-In the Gradient or B-Matrix table the B 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.
-<div id="GradientCreatorInput"></div>
-==== Gradient creator input parameters ====
-{|style="color:black; background-color:#F8F8F8;" border="1" cellpadding="10" class="wikitable"
-! align="left"| Gradient Creator Parameters
-! align="left"| Options
-! align="left"| Philips PAR/REC Version
-|-
-|'''Fatshift'''
-|'''R:''' Right<br> '''L:'''  Left<br> '''A:''' Anterior<br> '''P:'''  Posterior<br> '''H:''' Head<br> '''F:'''  Feet
-|V3 & V4 <br> V4.1 & V4.2
-|-
-|'''Jones30 or Kirby'''
-|Specify which MRI scanner was used to acquire DW images. This option works for [http://mri.kennedykrieger.org/usersupport.html KKI scanners] only.
-|V3 & V4 <br> V4.1 & V4.2
-|-
-|'''Gradient Resolution'''
-|'''Low:''' used for 8 DWI volumes <br> '''Medium:''' used for 17 DWI volumes<br> '''High:''' used for 34 DWI volumes
-|V3 & V4 <br> V4.1 & V4.2
-|-
-|'''Gradient Overplus'''
-|Yes <br> No
-|V3 & V4 <br> V4.1 & V4.2
-|-
-|'''Philips Release'''
-|Rel_1.5 <br> Rel_1.7 <br> Rel_2.0 <br> Rel_2.1 <br> Rel_2.5 <br> Rel_11.x <br>
-|V3 & V4 <br> V4.1 & V4.2
-|-
-|'''Patient Position'''
-|Head First <br> Feet First
-|V3 & V4
-|-
-|'''Patient Orientation'''
-|'''SP:''' [http://en.wikipedia.org/wiki/Supine_position Supine] <br> '''PR:''' [http://en.wikipedia.org/wiki/Prone_position Prone]<br> '''RD:''' [http://en.wikipedia.org/wiki/Decubitus Right Decubitus]<br> '''LD:''' [http://en.wikipedia.org/wiki/Decubitus Left Decubitus]<br>
-|V3 & V4
-|-
-|'''Fold Over'''
-|'''AP:''' Anterior-Posterior <br> '''RL:''' Right-Left <br> '''FH:''' Head-Feet/ Superior-Inferior
-|V3 & V4
-|-
-|'''OS (Operating System)'''
-|Windows <br> VMS
-|V3 & V4 <br> V4.1 & V4.2 (for KKI scanners only)
-|-
-|'''Inverted'''
-|No<br> Yes
-|V3 & V4 <br> V4.1 & V4.2 (for KKI scanners only)
-|}
-==== Specials notes ====
+*In the [[DTI pipeline interface#ImportData |Import Data]] the user uploads DW and T2 image (optional), and the gradient information (optional). MIPAV could also read the gradient information from the image header. The Phillips Gradient Creator will then calculate the gradient table based on the information provided.
-For more information regarding the importing DW images, refer to Jonathan Farrell's web site - [http://godzilla.kennedykrieger.org/~jfarrell/software_web.htm http://godzilla.kennedykrieger.org/~jfarrell/software_web.htm].
+*In the [[DTI pipeline interface#PreProcessing | Pre-processing tab]], the user is asked to specify the parameters required for aligning B0 to T2, and then DWI to the rigidly aligned B0, and also parameters for motion correction and eddy current distortion correction.
+* In the [[DTI pipeline interface #EPIDistortionCorrection | EPI Distortion Correction]], the user enters parameters needed for calculation of deformation vector fields for rigidly aligned B0 and T2 from the Pre-processing step.
+* In the [[DTI pipeline interface #TensorEstimation |Tensor Estimation tab]], the user uploads the mask image, selects the tensor estimation algorithm and specifies the output options.
+* In the [[DTI pipeline interface#TensorStatistics |Tensor Statistics]] the user can upload the tensor image and specify which kind of images (e.g. ADC, color map, Eigen value, Eigen vector, FA, RA, and Volume Ratio) he/she would like to have as an output.
+* In the [[DTI pipeline interface#VisualizationTab |Visualization]], the user can  upload images needed for a 3D visualization of fiber bundle tracts in the brain's white matter using the provided information. The user can then save fiber tracts information as [[Image formats descriptions#VtkXml |.vtk and .dat]] files.
-*If your DTI data was acquired on a Philips MR Scanner with new software (release 1.5, 1.7, 2.0 or 2.1), the PAR file version may be V4.1.  You can verify this by looking at the top right hand corner of the file for the following tag:  <span style="font-family:courier">Research image export tool V4.1</span>.  The other known versions are V3 or V4.
+== DTI Pipeline Tutorials ==
-*The V4.1 PAR files list the diffusion weighting directions, however, these directions are provided not in the image space (see [[#SliceAngulation | Slice angulation]]).  Specifically, in the case [[#GradientCreatorInput | the Gradient Overplus option]] is set to YES, the directions need to be rotated and corrected for [[#SliceAngulation |slice angulation]].  This is automatically done by the Gradient Table creator.
-*The Gradient Table Creator does not read gradient directions from the PAR file because this information is not available for all PAR files (V3 or V4).  It uses the directions documented in the Philips source code instead.
-=== Re-Ordering data (from Camino) ===
+Refer to [[DTI Pipeline Tutorials]].
-Probably the most common task is to re-order data from image to voxel order. In scanner order or image order, multi-component images are stored as consecutive volumes. This is convenient for visualization, since you can easily render a particular 3D volume. It is inconvenient for parallel processing, as you must read the entire 4D image in order to get the components for a particular processing. Data in voxel order stores all components for a particular voxel together. Thus you can read the image one voxel at a time, or skip ahead to particular voxel, without reading the entire image into memory. Camino does most of its I/O in voxel order. You can get into and out of scanner order with the scanner2voxel and voxel2scanner commands. Since these commands expect to deal with mostly raw data, they read and write floats by default (see below). You can change this behaviour with -inputdatatype and -outputdatatype options. For example:
-scanner2voxel -voxels 983040 -components 60 -inputfile ScannerOrder.img -inputdatatype short > VoxelOrder.Bfloat
+TBD.
-where -components specifies the number of volumes in the 4D input and -voxels specifies the number of voxels (ie, X×Y×Z, where X, Y, Z are the dimensions of the image).
+== See also: ==
+*[[Understanding MIPAV capabilities]]
+*[[Developing new tools using the API]]
+*[[Using MIPAV Algorithms | Overview of MIPAV Algorithms]]
+*[[Cost functions used in MIPAV algorithms]]
+*[[Interpolation methods used in MIPAV]]
+*[[DTI Color Display]]
+*[[DTI Estimate tensor]]
+*[[Optimized automatic registration 3D]]
 [[Category:Help:Stub]]
+[[Category:Help:Algorithms]]

Difference between revisions of "DTI Pipeline"

Latest revision as of 16:43, 29 May 2012

Contents

Introduction

MIPAV DWI pipeline overview

Brain MRI introduction

Determining an anatomical correspondence

MIPAV DTI pipeline outline

Image types

MIPAV DTI pipeline interface

DTI Pipeline Tutorials

See also:

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