This website has been deprecated and exists as an archive only.

The TORTOISE website has been moved to https://tortoise.nibib.nih.gov.

For any information about TORTOISE generation V3.x.x, please refer to the new website.

3.1.1.1 Advanced Optimize Configfile

This page is intended for future use only. It contains tags for functions which are not currently available in the public release. Viewing of this page is restricted to the website administrators only.

How to Optimize Your Configfile

The default configfile as shown in section 3.1 contains only the minimum tags required for proper functioning of DIFF_CALC. Do not remove any of the default tags, or the program may give you errors. However, there are a number of other tags that can be copied and pasted into your configfile in order to set some default settings to suit your needs. I have included some extra descriptions for the tags which are not self-explanatory. Please copy and paste the tags and comments that you desire exactly as they appear below. Do not include the extra descriptions, only the comments surrounded by .

Tensor Fitting Options


            <!-- Default tensor fitting option: 0 = linear, 1 = non-linear, 5 = newton, 6 = Newton constrained -->

            <diff_par_a>1</diff_par_a>

            

            <!-- Use a robust tensor fitting algorithm? 0 = non-robust 1 = robust -->;

            <robust_par_a>0</robust_par_a>

There are 2 robust tensor fitting algorithms implemented in this software. This tag allows the user to switch between methods. The default method is GMM. The other option is RESTORE (Chang, L-C, RESTORE: Robust Estimation of Tensors by Outlier Rejection. MRM 53:1088-1095(2005))


            <!-- determines the method of robust fitting 0 = Restore, 1 = GMM -->

            <!-- this tag is ignored if robust fitting is set to off -->

            <robust_gmm>0</robust_gmm>

            

            <!-- If running robust tensor fit, save non-linear and non-linear robust? 0 = save robust only, 1 = save both -->;

            <save_both_robust>1</save_both_robust>

By default, all diffusion weighted images and also non-diffusion weighted (b0) volumes are considered in the robust fitting algorithm. However, if you want to exclude the b0 volumes from the outlier rejection (meaning all points from the b0 volumes will be used for the tensor fitting), use the following tag.


            <!-- this option allows you to restrict the restore algorithm to only the diffusion weighted images -->

            <!-- 1 = do not check the b0 images, all b0 images will be included -->

            <!-- 0 = allow b0 images to be outliers -->

            <robust_check_no_b0>0</robust_check_no_b0>

The following tag has to do with how the b0 volumes are treated within the restore tensor fitting algorithm. The b0 volumes have different noise characteristics than the diffusion weighted volumes, and so a coefficient is used to weight the noise differently for the b0 volumes. The "proper" value is going to depend on your data.


            <!-- this coefficient determines the signal standard deviation to apply specifically to the b0 images -->

            <noise_kappa>2.0</noise_kappa>

A histogram of the residuals for each slice is displayed by default during the tensor fitting. Use this tag if you would like to turn off this histogram. Note that the residuals can clearly show if there are any problems with your data, and so can be very helpful.


            <!-- Display residuals? 0 = yes, 1 = no -->

            <residue_par_a>0</residue_par_a>

For the non-linear, and robust tensor fitting methods, a cumulative histogram of the residuals is displayed by default. Use this if you do not want to display the residuals. See the above discussions for reasons to display the histogram. In a few cases, with very very large data sets (256 x 256 x 150 x 120 directions for example) IDL may run out of memory. If this happens, set the value of this tag to 0.


            <!-- Display residual histogram? 0 = no, 1 = yes -->

            <residual_stat_flag>1</residual_stat_flag>

This option will allow you to save the cumulative histogram. Please heed the warning included in the tag description. Having this tag set to 1 has caused IDL to run out of memory, as it can be a very large array. The default for this tag is off.


            <!-- save the residual histogram as an IDL array. WARNING! This array can be huge -->

            <!-- 1 = save. Note: this is only effective if residual_stat_flag = 1 -->

            <!-- 0 = don't save -->

            <save_global_residual_array_flag>0</save_global_residual_array_flag>

Other Options

When masking the data, one can choose either to use BET (S.M. Smith, Fast robust automated brain extraction, HBM 17(3):143-155, Nov. 2002) or a simple threshold. Default is to use BET.


            <!-- 0 = simple thresholding, 1 = bet -->

            <mask_par_a=1>1</mask_par_a=1>

Noise estimation can be done automatically, or by drawing an ROI in the background. Default is ROI.


            <!-- Default noise estimation: 0 = ROI, 2 = robust automatic method -->

            <noise_par_b>0</noise_par_b>

Saving Options


            <!-- if this keyword is set processed tensor data are masked before being saved -->

            <!-- in addition, when exporting raw images, it masks the raw images being exported if a mask is defined -->

            <!-- set to 1, or comment out -->

            <mask_save_data>1</mask_save_data>

There are a large number of computed quantities that can be saved. Set the following tags to 0 for off or 1 for on. The default on/off value is reflected below. These only need to be included in your configfile if you wish to change from the default value.

<!-- Save Image Options -->
<!-- 1 = save, 0 = don't save -->
s
<save_difften>1</save_difften>

<save_mask>1</save_mask>

<save_mask_eroded>1</save_mask_eroded>

<save_mask_plain>1</save_mask_plain>

<!-- b0 image -->
<save_ampimages>1</save_ampimages>

<save_trace>1</save_trace>

<!-- eigenvalues -->
<save_segval>1</save_segval>

<!-- eigenvectors -->
<save_segvec>1</save_segvec>

<!-- chi square map -->
<save_dchisq>1</save_dchisq>

<!-- Volume fraction for dual compartment tensor fitting -->
<save_volume_fraction>1</save_volume_fraction>

<!-- Volume Ratio Anisotropy Index -->
<save_aniso4>1</save_aniso4>

<!-- Relative Anisotropy -->
<save_aniso5>1</save_aniso5>

<!-- Fractional Anisotropy -->
<save_aniso6>1</save_aniso6>

<!-- Lattice Index -->
<save_organ3>1</save_organ3>

<save_skewness>0</save_skewness>

<save_schwartz_array>0</save_schwartz_array>

<save_akaike_array>0</save_akaike_array>

<save_schwartz_array2>0</save_schwartz_array2>

<save_akaike_array2>0</save_akaike_array2>

<save_difften2>0</save_difften2>

<save_trace2>0</save_trace2>

<save_segval2>0</save_segval2>

<save_segvec2>0</save_segvec2>

<save_aniso5_2>0</save_aniso5_2>

<save_aniso6_2>0</save_aniso6_2>

<save_lattice_2>0</save_lattice_2>

<save_skewness_2>0</save_skewness_2>

<save_n_iter_map>1</save_n_iter_map>

<save_error_info_map>0</save_error_info_map>

<save_color_map>1</save_color_map>

<save_outlier_map>1</save_outlier_map>

<save_westin_linear>0</save_westin_linear>

<save_westin_planar>0</save_westin_planar>

<save_residuals_first_moment>0</save_residuals_first_moment>

<save_residuals_second_moment>0</save_residuals_second_moment>

<save_residuals_third_moment>0</save_residuals_third_moment>

<save_residuals_forth_moment>0</save_residuals_forth_moment>

<save_residuals_median_absolute_dev_from_median>

</save_residuals_median_absolute_dev_from_median>

<save_residuals_median_absolute_dev_from_zero>0</save_residuals_median_absolute_dev_from_zero>