<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div>Thank you very much,</div><div>I found many steps in the user manual, but I understood that you recommended that its better for me to do linear transformation ( step 1. ) in the user manual. Is it right?. </div><div>And what is difference between linear And non linear registration regarding their applications .</div><div><br>On Feb 24, 2013, at 6:58 PM, "susumu mori" <<a href="mailto:susumu@mri.jhu.edu">susumu@mri.jhu.edu</a>> wrote:<br><br></div><blockquote type="cite"><div>1) Conventional method (all steps will be performed in the native space)<div><br></div><div>>Load DTI data to DtiStudio, do tractography based on a manual protocol.</div><div>>Use "Statistics" function to quantify pixel values along the reconstructed tract. If you load an FA map, you'd get one averaged FA value of the entire tract.</div>
<div>>Use "Profile" function to obtain the report of averaged FA values at each slice (axial, sagittal, coronal). This method works only when your tract is more or less straight. For example, if you are measuring the corticospinal tract (CST), the axial slice-by-slice report is most informative.</div>
<div>>Support the CST spans axial slice #5 (pons) to slice #50 (motor cortex), you have 46 values. Save this information in the first column of a Excel sheet. This is the result from Patient A.</div><div>>Repeat the process for Patient B. You may find the tract runs slice #8 - #51. You have 44 values, which are copied to the second column.</div>
<div>>Now you have an issue; the slice locations are different and the tract length are different. This is a typical "registration" problem.</div><div>>Manually identify landmark slices. For example, for Patient A, slice#5 is pons, #12 is midbrain, #30 is internal capsule. For Patient B, the corresponding slides are 8-13-27. Then you can align these slice levels between A and B inside the Excel sheet. Slice locations are adjusted but you still have tract length problem; pons-midbrain of A is 7 slices and B is 5 slices. You can do interpolation calculation inside Excel to extend the 5 slice values in B to 7 slices.</div>
<div><br></div><div>This is what we did in Stieltjes paper. Obviously a lot of work. The problem is cross-subject registration. This registration issue doesn't exist if you average the entire FA values of a tract. As soon as you try to recover location information, the registration issue arises. </div>
<div><br></div><div>2) Tractography after registration</div><div><br></div><div>Things get much easier if you do brain-to-brain registration first and then do tractography. For this purpose, you use linear registration that adjust brain locations, rotations, and sizes (12-parameter or 9-parameter (recommended) affine transformation) . After this registration procedure, the slice locations and tract lengths are equal (almost) across all subject. So you won't need the registration procedure described above. </div>
<div><br></div><div>You need to use DiffeoMap for this registration. You need to register two tensor fields. After two tensor fields are aligned, you calculate FA and vectors for tractography. The procedures are described in "Getting Started" in <a href="http://www.mristudio.org">www.mristudio.org</a>.</div>
<div><br></div><div>3) Some more thoughts</div><div><br></div><div>As mentioned above, you don't need brain registration if you use only the overall FA value from a tract. As soon as you try to retrieve more location information, you have to worry about location registration. If you want even finer location information beyond slice-by-slice report, or if your tract is not linear (U-shape) and slice-by-slice report is not enough, you need to further divide the tract into smaller units. The smallest unit is a voxel. The ultimate goal of image registration is voxel-to-voxel registration. For any arbitrary voxel you pick in one brain, you can find a corresponding voxel in the other. If your voxel-to-voxel registration is perfect, you don't need tractography; you can compare FA values at each pixel and find abnormalities; why do you bother spending time for tractography if you can find a cluster of voxels with abnormal FA values without tractography?</div>
<div><br></div><div>There are two reasons. First you can't always assume the brain registration is accurate after the automated brain-to-brain registration. You can argue tractography can identify corresponding pixels between two brains more accurately. <u>Namely, tractography is a cross-subject registration tool with which you can extract corresponding group of voxels from multiple subjects.</u> Another point is that voxel-to-voxel FA comparison is too noisy and needs voxel-averaging to enhance SNR. Usually the voxel-based anlaysis applies isotropic voxel-grouping filter, like 5x5x5 filters. Instead of applying the blind isotropic filtering, tractography groups only voxels that belong to the same tract. <u>Namely, tractography is structure-specific smart filter to enhance SNR.</u> Therefore, the automated voxel-based and tractography-based approaches could have different accuracy and sensitivity.</div>
<div><br></div><div>On the other hand, tractgraphy is also a noisy tool; the number of voxels clustered by this method is highly variable among subjects. So, there is no perfect solution. If you used a probabilistic tract information stored in our atlas, you are not affected by the tractography variability because you apply the same probabilistic map to all subjects. However, you invite the inaccuracy of the brain-to-brain registration. In other words, you throw away the "registration function" of the tractography I described above, but you still retain its "filtering function".</div>
<div><br></div><div>I hope these arguments are helpful.</div><div><br></div><div>Susumu</div><div><br></div><div><br><br><div class="gmail_quote">On Sun, Feb 24, 2013 at 10:57 AM, Shaimaa Abdelsattar <span dir="ltr"><<a href="mailto:shaimaa96@hotmail.com" target="_blank">shaimaa96@hotmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="auto"><div>Thank you very much for your detailed explanation</div><div>Primarily, I would like to master the first approach ( manual), how can I load multiple patients for registration and then slice by slice processing to do group analysis, is AIR program included within DTI studio, or is a separate program. Please can you tell me step by step like 1, 2, ..... </div>
<div><br></div><div>Thank you very much</div><div><br>On Feb 23, 2013, at 5:07 PM, "susumu mori" <<a href="mailto:susumu@mri.jhu.edu" target="_blank">susumu@mri.jhu.edu</a>> wrote:<br><br></div><blockquote type="cite">
<div><br><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
I would like to ask if 1. I can measure FA value, ADC or MD in a specific ROI or along a specified tract in order to have data in an objective way?<br>
<br></blockquote><div><br></div><div>There are multiple approaches;</div><div><br></div><div>1) Manual approach in the native space:</div><div><br></div><div>You need to establish a protocol for manual placement of ROIs. After manual fiber reconstruction, as Dorian pointed out, DtiStudio can report averaged pixel intensities (e.g. FA, MD, etc) of the all pixels that contain the fibers or report slice-by-slice numbers.</div>
<div><br></div><div>You can find some protocols in this paper;</div><div><span style="font-size:11.0pt;font-family:"Times New Roman","serif"">Wakana S, Caprihan A,
Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey
P, Blitz A, van Zijl P, Mori S. Reproducibility of quantitative tractography
methods applied to cerebral white matter. Neuroimage 2007;36(3):630-644,
PMC2350213</span></div><div><span style="font-size:11.0pt;font-family:"Times New Roman","serif""><br></span></div><div>When you use the slice-by-slice reports, instead of one value averaged over an entire tract, you have to "register" data from different subjects because, say, an averaged FA of a tract at "axial slice#10" of one subject may not be the same anatomical slice of "axial slice#10" of the other subject. An example of this operation can be found in this paper;</div>
<div><br></div><div><span style="font-size:11.0pt;font-family:"Times New Roman","serif"">Stieltjes B, Kaufmann WE, van
Zijl PC, Fredericksen K, Pearlson GD, Solaiyappan M, Mori S. Diffusion tensor
imaging and axonal tracking in the human brainstem. Neuroimage
2001;14(3):723-735</span></div><div><br></div><div>2) Automated approach in the native or MNI space:</div><div><br></div><div>These papers describe automated ROI placements;</div><div><br></div><div><span style="font-size:11.0pt;font-family:"Times New Roman","serif"">Zhang W, Olivi A, Hertig SJ,
van Zijl P, Mori S. Automated fiber tracking of human brain white matter using
diffusion tensor imaging. Neuroimage 2008;42(2):771-777, PMC2585359</span></div><div><span style="font-size:11.0pt;font-family:"Times New Roman","serif""><br></span></div><div><span style="font-size:11.0pt;font-family:"Times New Roman","serif"">Zhang Y, Zhang J, Oishi K, Faria
AV, Jiang H, Li X, Akhter K, Rosa-Neto P, Pike GB, Evans A, Toga AW, Woods R,
Mazziotta JC, Miller MI, van Zijl PC, Mori S. Atlas-guided tract reconstruction
for automated and comprehensive examination of the white matter anatomy.
Neuroimage 2010;52(4):1289-1301, PMC2910162</span></div><div><span style="font-size:11.0pt;font-family:"Times New Roman","serif""><br></span></div><div>The idea is, you can define ROIs (or use our pre-defined brain parcellation maps) once in an MNI atlas and warp these ROIs to each subject for automated tracking. Yajing Zhang has many pre-defined ROI sets in our brain parcellation maps. I believe you can download them from our websites.</div>
<div><br></div><div>3) Probabilistic approach:</div><div><br></div><div>One issue of #1 and #2 approaches is that the streamline generation has a large amount of variability. Another common issue is, if there are lesions with low FA, tractography is influenced by that. Our atlases have probabilistic tract locations. For example, we have reconstructed the corticospinal tracts in normal subjects and the results are registered into the MNI space, creating probabilistic map of the corticospinal tract in the MNI space. If you normalize your patient brains to the MNI space, you can superimpose these probabilistic maps on the patient brains and quantify averaged pixel intensities.</div>
<div><br></div><div>There are pre-defined probabilistic maps of many tracts in RoiEditor, which can be applied for automated pixel intensity calculation of various white matter tracts. Downside of this approach is, it assumes that tract locations of your patient population are not significantly altered and therefore after normalization to the MNI space, the probabilistic maps can accurately define the properties of each tract of interest.</div>
<div><br></div><div>These papers describe this approach;</div><div><br></div><div><span style="font-size:11.0pt;font-family:"Times New Roman","serif"">Hua K, Zhang J, Wakana S, Jiang
H, Li X, Reich DS, Calabresi PA, Pekar JJ, van Zijl PC, Mori S. Tract
probability maps in stereotaxic spaces: analyses of white matter anatomy and
tract-specific quantification. Neuroimage 2008;39(1):336-347, PMC2724595</span></div><div><br></div><div><span style="font-family:'Times New Roman',serif;font-size:14.399999618530273px">Zhang Y, Zhang J, Oishi K, Faria AV, Jiang H, Li X, Akhter K, Rosa-Neto P, Pike GB, Evans A, Toga AW, Woods R, Mazziotta JC, Miller MI, van Zijl PC, Mori S. Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy. Neuroimage 2010;52(4):1289-1301, PMC2910162</span></div>
<div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
2. Can I load an anatomical sequence over DTI data eg. Color map or fA map etc...?<br>
<br></blockquote><div><br></div><div>Of course, you first have to register your anatomical image (like T1) to your DTI (or register DTI to T1). Then you can load both T1 and DTI to RoiEditor. We don't have image-to-image overlay functions with transparency control. You have to define "object (ROI)" using one of the image (like tract locations from DTI or thresholded high FA regions) and then superimpose the object (ROI) to the other image (like T1). In RoiEditor, the tract streamline information have to be converted to 3D image format (like a masking file in which pixels that contain a fiber are "1" and all other are "0", or each pixel contains a probabilistic value). If you load the co-registered T1 to DtiStudio, you can superimpose streamline information on T1. For T1-DTI co-registration, you can use our DiffeoMap or other software like FSL and SPM. </div>
<div><br></div><div>Please be careful because all MriStudio family programs use Radiology convention (right is left) while many other programs follow Neurology convention (right is right).</div><div><br></div><div>susumu</div>
<div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Thanks<br>
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