Fod2fixel generates no output files


#1

Dear MRtrixters,

I am a relatively new MRtrix user and try to run a fixel-based analysis for the first time. So far I have been able to tackle all occuring problems by myself, thanks to your elaborate documentation! However, I am now encountering a problem and I simply can’t figure out what I might be doing wrong:
I am trying to create a template fixel analysis mask, as described in the FBA pipeline (step 13). The problem is, that the fod2fixel command does not produce any peak image. I run the command exactly as suggested in the documentation.
Below you find a screenshot of the command and debug messages.

EDIT: In fact I realized, that the FODs that I am producing in the course of the analysis look fine but they have a VERY low amplitude for all subjects and therefore also for the template. Might this be the reason for fod2fixel to fail? Also, what might be the reason for this?

Any help on this regard is greatly appreciated!

Greetings from Germany,
Bastian


#2

OK, if the issue is low FOD amplitude, that’s more likely to be due to the response function values being too large compared to the signal. This might happen for example if you computed the response function, then performed intensity normalisation, which will change the DW signal amplitudes quite radically, and then used the now incorrectly-scaled response functions in the dwi2fod command. Any chance you could post the full series of commands you used from start to finish…? Or at least until the dwi2fod stage?


#3

I basically followed step by step the FBA pipeline as suggested in the docs (so nothing fancy here). The data is denoised (dwidenoise) and distortion corrected using FSL routines. As I am interested in AFD differences, I corrected for the bias field using dwibiascorrect -ants followed by global intensity normalisation using dwiintensitynorm. After that I just determine the group average response function using dwi2response followed by average_response over all subjects.
The scale factors of the intensity normalisation between the groups do not differ too much, but they range from 2 up to 7 between subjects. Does that range seem too large? I might also need to add, that the dataset comprises of two slightly different DWI sequences (single shell, same b-value, same number of b0’s, same number of diffusion directions), but we already experienced before that there might be a difference in e.g. mean FA-values between the sequences. Therefore we paid attention to have the same ratio of both sequences in our control group as well. Is this maybe problematic for the intensity normalisation?


#4

OK, the main thing I’d be looking out for is exactly what happened between dwi2response and the subsequent dwi2fod using the resulting response. If anything was done to scale either the data themselves or the responses, this might have introduced the problem.

Maybe you can post the contents of your average response file, and the output of:

mrstats dwi.mif -mask mask.mif

where dwi.mif is the same file that you passed to dwi2fod, and mask.mif is your brain mask. That should give us a handle as to whether there is indeed a mismatch.

Could you also state the b-value used, and confirm that these are human in vivo data?


#5

Yes, that is correct, I am analyzing human in vivo data and the b-value is 1000.

This is the content of the group average response textfile:

2080.42411 -853.52666 214.60255 -26.16561 -6.49694 4.40063

And here you find the output of mrstats dwi.mif -mask mask.mif of the first subject dwi.mif that gets passed to dwi2fod (after dwiextract, so without b0 volumes, as suggested in the docs) :

first subject stats
volume       mean     median      stdev        min        max      count
       [ 0 ]    507.827 490.438293     274.46   -80.3497    3099.23     712152
       [ 1 ]    529.964 517.423584    270.497   -58.5235    3531.69     712152
       [ 2 ]    522.133 503.826965    276.183   -56.2529    3326.23     712152
       [ 3 ]    507.747 498.232727    269.114   -52.3703    3220.91     712152
       [ 4 ]    490.651 481.047333    263.366    -120.49    2678.32     712152
       [ 5 ]    519.448 512.203308    268.033   -58.1178    3393.55     712152
       [ 6 ]    484.409  478.68689    257.734   -81.0762    2663.86     712152
       [ 7 ]    513.157 507.696442    265.893   -65.8266    3490.17     712152
       [ 8 ]    503.515  490.36618    269.219   -58.7455    3028.57     712152
       [ 9 ]    521.404 508.235535    271.997   -53.0805    3472.53     712152
      [ 10 ]    521.551 508.541504    271.189   -49.5201    3277.83     712152
      [ 11 ]    527.563 514.597717    272.641   -92.4051    3628.53     712152
      [ 12 ]    527.784 510.015442    278.407   -78.0892    3643.74     712152
      [ 13 ]    508.095 497.968719    268.586    -53.902    2791.18     712152
      [ 14 ]    495.263  483.94104    265.513   -60.9498    2873.24     712152
      [ 15 ]     520.89  502.30127    278.065   -66.7391    3652.97     712152
      [ 16 ]    507.043  499.66333    265.401   -77.0895    2857.22     712152
      [ 17 ]    499.426 492.180267    264.538   -74.3982    2950.81     712152
      [ 18 ]     523.68   502.9776     279.72   -61.0276    3555.34     712152
      [ 19 ]    515.363  497.34436    277.033   -56.6087    3511.47     712152
      [ 20 ]    505.673 494.587372    268.383   -94.2953       2889     712152
      [ 21 ]    485.742 480.822205    257.743   -80.3387    2347.23     712152
      [ 22 ]    502.455 496.657349    264.094   -66.5082    2961.34     712152
      [ 23 ]    533.207 517.591553    276.289   -57.8096    3688.32     712152
      [ 24 ]    537.434  528.08844    268.965   -90.5923    3516.72     712152
      [ 25 ]    520.234 514.762085    266.834   -120.024    3390.59     712152
      [ 26 ]    530.917 528.988159    266.649   -89.5505    3466.78     712152
      [ 27 ]    521.089 507.088257    273.598   -57.4973    3439.88     712152
      [ 28 ]    511.766 506.912415    265.331   -59.2338    3297.04     712152
      [ 29 ]    497.229 494.757812    260.442   -74.8142    2743.09     712152
      [ 30 ]    537.315 523.370972    273.407   -85.4377    3704.62     712152
      [ 31 ]    541.408 528.596191    273.746   -83.1746    3750.45     712152
      [ 32 ]    522.198 518.576538    266.903    -84.978    3106.83     712152
      [ 33 ]     514.78 508.146667      267.7   -71.1017    3060.32     712152
      [ 34 ]    508.421 497.612122    269.436   -94.7188     3332.2     712152
      [ 35 ]    487.141 481.945801    258.647   -70.3798    2427.87     712152
      [ 36 ]    513.559 505.613281    268.157   -73.6421     3491.1     712152
      [ 37 ]    500.781 490.789856    267.587   -119.317     2851.2     712152
      [ 38 ]    506.046 504.536682    263.369   -53.0378    2852.72     712152
      [ 39 ]    533.841 530.468262    267.919   -62.9853       3292     712152
      [ 40 ]    497.217 491.205444    262.019   -55.9709     2436.1     712152
      [ 41 ]    533.801 517.139343    277.611    -65.413    3496.85     712152
      [ 42 ]    492.821 487.402527    261.271   -113.572    2681.14     712152
      [ 43 ]    489.421  486.91452    257.441   -128.186    2530.04     712152
      [ 44 ]    520.592 507.728943    274.424   -105.904    3591.64     712152
      [ 45 ]    509.727 499.015961     269.94   -89.2779    3212.69     712152
      [ 46 ]    530.096 530.847534    264.928   -99.6467    3200.83     712152
      [ 47 ]    503.144 495.619781    266.241   -128.316    2827.26     712152
      [ 48 ]    518.177 504.122681    273.373   -64.7316    3475.91     712152
      [ 49 ]    493.812 486.261719    262.791   -98.2346    2550.97     712152
      [ 50 ]    513.214 509.042908    266.721   -69.5732     3098.9     712152
      [ 51 ]    521.046 507.843628     275.04   -144.113    3550.38     712152
      [ 52 ]    524.228   523.9021    265.413    -87.281    3405.62     712152
      [ 53 ]    493.887 492.836365    259.258   -120.952     2379.3     712152
      [ 54 ]    497.783 493.943207    261.848   -75.5536    2435.62     712152
      [ 55 ]     514.99  506.72641    268.841   -99.3307    3250.61     712152
      [ 56 ]    500.678 496.201904    263.712    -98.068    2745.49     712152
      [ 57 ]    526.273 524.574585    267.211    -63.402    3523.98     712152
      [ 58 ]      530.1  525.62439     267.63   -73.2229    3330.73     712152
      [ 59 ]    502.147 498.816589    262.255   -83.0168     2482.2     712152

Between dwi2response and dwi2fod there is actually not much more happening apart from upsampling the data to 1.25 mm (forgot to mention that in my earlier post). The native resolution is 1.7 mm.

I am happy to provide more information if needed, I can also upload the mrstats output of all subjects (they are all very similar though).


#6

OK, that doesn’t look too bad. Slightly surprised to find negative minimum values in your mrstats output, but that might just be due to the upsampling (which was presumably performed using cubic interpolation?). What does the mrstats output look like when applied to the fod image rather than the DWI (within the brain mask once again)? Could you also give us a feel for how low those FOD values are, with a command like this:

sh2peaks fod.mif -mask mask.mif -num 1 - | mrmath - rms -axis 3 - | mrstats - -mask mask.mif

#7

The negative minimum intensities I already see in the raw data, only in a few voxels though. I used the cubic interpolation method for upsampling.

Here the output of the FOD image:

first subject FOD stats
volume       mean     median      stdev        min        max      count
       [ 0 ] 0.00643504 0.0063350955 0.00321782 -7.08121e-09  0.0367352     712152
       [ 1 ] 5.93547e-05 2.04614498e-05 0.00405906 -0.0456137  0.0305389     712152
       [ 2 ] -6.30812e-06 -6.43457406e-06 0.00347312 -0.0282046  0.0196061     712152
       [ 3 ] -0.00166753 -0.00198418228 0.00392866 -0.0336771  0.0211051     712152
       [ 4 ] -0.000117985 -6.47250999e-05 0.00392564  -0.024667  0.0275005     712152
       [ 5 ] 0.00153106 0.00117762736 0.00443135 -0.0234402  0.0518743     712152
       [ 6 ] -2.46394e-05 -4.78770634e-11 0.00301392 -0.0258597  0.0281236     712152
       [ 7 ] -0.000127561 -3.58730103e-05 0.00251951 -0.0274521   0.019686     712152
       [ 8 ] 5.02033e-05 4.07642219e-05 0.00245464 -0.0182714  0.0249637     712152
       [ 9 ] 0.000125433 5.58218453e-05 0.00220021 -0.0160517   0.014301     712152
      [ 10 ] 0.000416363 0.000410317385 0.00249668 -0.0137791  0.0222898     712152
      [ 11 ] -2.39951e-06 2.99001206e-11 0.00263947 -0.0195124  0.0172404     712152
      [ 12 ] -0.000400915 -0.000326917274 0.00268311 -0.0284327  0.0167977     712152
      [ 13 ] 8.8433e-06 1.45006238e-11 0.00259797 -0.0189564   0.022786     712152
      [ 14 ] 0.000585689 0.000360291684 0.00306075 -0.0272446  0.0344797     712152
      [ 15 ] -4.86166e-05 -6.82655991e-06 0.00154302 -0.0171748  0.0206507     712152
      [ 16 ] -1.50241e-05 -5.62217156e-06 0.00130734 -0.0113156   0.012485     712152
      [ 17 ] 2.99584e-05 1.30556136e-05 0.00128647 -0.0107171  0.0102371     712152
      [ 18 ] -2.04768e-05 -8.64767753e-06 0.00121404 -0.00977478  0.0109237     712152
      [ 19 ] -9.16932e-06 -2.18681089e-06 0.00117647 -0.0115346  0.0120744     712152
      [ 20 ] -3.44532e-05 -2.13693475e-05 0.00107199 -0.0119531  0.0107112     712152
      [ 21 ] -0.000146783 -0.000140826291 0.00121008  -0.010827 0.00840159     712152
      [ 22 ] -1.44554e-05 -4.83418808e-06 0.00131717 -0.0111827  0.0120829     712152
      [ 23 ] 0.000139783 9.91700799e-05 0.00127472 -0.0113549  0.0134595     712152
      [ 24 ] 1.44081e-05 6.17111709e-06 0.00126199 -0.0107823  0.0127186     712152
      [ 25 ] -0.000140636 -9.67574451e-05 0.00131053 -0.0119828  0.0108973     712152
      [ 26 ] 4.34668e-06 1.90992569e-06 0.00132162 -0.0117945  0.0112685     712152
      [ 27 ] 0.000174586 6.18674312e-05 0.00153571 -0.0163996  0.0176836     712152
      [ 28 ] -4.01355e-06 2.0268856e-11 0.000639334 -0.00915319 0.00977385     712152
      [ 29 ] 2.01881e-05 2.91985066e-06 0.000569782 -0.00871291 0.00950381     712152
      [ 30 ] 5.86662e-06 4.25611421e-11 0.000533822 -0.00727504 0.00814711     712152
      [ 31 ] -3.05216e-06 4.13260849e-11 0.000500782 -0.00697423 0.00686528     712152
      [ 32 ] 8.77545e-06 1.3352676e-06 0.000478809 -0.0057233   0.006767     712152
      [ 33 ] -4.68732e-06 -1.34704692e-10 0.000455746 -0.00528046 0.00543472     712152
      [ 34 ] 1.89504e-05 5.5346668e-06 0.000436329 -0.00671021 0.00651644     712152
      [ 35 ] 7.33803e-06 2.76761421e-06 0.000387572 -0.00713965 0.00678556     712152
      [ 36 ] 1.8648e-05 1.39397298e-05 0.000438994 -0.00676201 0.00553996     712152
      [ 37 ] 9.34479e-06 3.60616787e-06 0.000488483 -0.0107203 0.00734046     712152
      [ 38 ] -1.85262e-05 -1.32907044e-05 0.000463448 -0.00538998 0.00704914     712152
      [ 39 ] 2.74683e-06 6.08094126e-07 0.000470665 -0.00553428 0.00597728     712152
      [ 40 ] 1.94992e-05 1.48504769e-05 0.000479533 -0.00646335    0.00674     712152
      [ 41 ] -2.89978e-06 -5.12708018e-12 0.000497448 -0.00644075 0.00647236     712152
      [ 42 ] -6.36815e-06 -5.38688437e-06 0.000524843 -0.00742928 0.00820259     712152
      [ 43 ] 1.78358e-06 1.87033e-10 0.000550642 -0.00711865 0.00758185     712152
      [ 44 ] 1.06046e-05 9.75084504e-07 0.000650714 -0.00989082  0.0133607     712152

And this is the output of the sh2peaks-command above:

first subject peaks
volume       mean     median      stdev        min        max      count
       [ 0 ] 0.00974909 0.00946084224 0.00500929 4.5628e-12  0.0700648     712152

This seems weirdly low, doesn’t it? If I estimate the FODs without bias field correction, global intensity normalization and group average response function (but just use the response function for the respective subject), the output of sh2peaks is nearly 100-times higher.


#8

Yep. Your maximim FOD peak amplitude is 0.07 – below the default 0.1 threshold. Something clearly not right there.

OK, so that at least provides a means of narrowing down the issue. I suggest you look into which steps modify the amplitude of the DW signal (most likely global intensity normalisation, although bias field correction may make a minor difference), and check exactly at what point your response function is estimated at the subject level relative to these steps. My guess is (as before) that you’re estimating the response functions before intensity normalisation, then applying them for the CSD after that step. This would mean that the response function would be scaled appropriately for non-intensity-normalised data, but completely off after than point. I really do think it would help if you could post the exact steps and commands you used in the exact order you ran through them, so that we can double-check all this…


#9

… although bias field correction may make a minor difference

Actually, dwibiascorrect -ants can have quite a significant effect on intensity magnitude: there’s no constraint for the geometric mean of the bias field within the brain mask to be 1. I’ve seen it applying values of ~ 100-300 before. If this factor varies considerably between subjects, and dwiintensitynorm is not used, this could have a significant effect. Have thought about explicitly scaling the bias field within the script after N4 is completed, but seemingly never listed it on GitHub.


#10

Alright, sorry for the late response, your concern about the correct sequence of commands made me doubt myself, so I repeated the analysis steps (until dwi2fod) again. I also noticed two control subjects behaving weirdly during registration so I excluded them for now. I followed meticulously the pipeline as suggested in the docs.

This was my sequence of commands
   cd sbjs

   echo "######################## BIAS CORRECTION ######################"

   foreach * : dwibiascorrect -ants -mask IN/dwi_mask.mif IN/dwi_denoised_preproc.mif IN/dwi_denoised_preproc_bias.mif

   mkdir -p ../dwiintensitynorm/dwi_input
   mkdir ../dwiintensitynorm/mask_input

   echo "######################## INTENSITY NORMALIZATION ######################"

   foreach * : ln -sr IN/dwi_denoised_preproc_bias.mif ../dwiintensitynorm/dwi_input/IN.mif
   foreach * : ln -sr IN/dwi_mask.mif ../dwiintensitynorm/mask_input/IN.mif

   dwiintensitynorm ../dwiintensitynorm/dwi_input/ ../dwiintensitynorm/mask_input/ ../dwiintensitynorm/dwi_output/ ../dwiintensitynorm/fa_template.mif ../dwiintensitynorm/fa_template_wm_mask.mif

   foreach ../dwiintensitynorm/dwi_output/ * : ln -sr IN PRE/dwi_denoised_preproc_bias_norm.mif
# space added before the wildcard only to improve readibility in this textbox

   echo "######################## RESPONSE FUNCTION ######################"

   foreach * : dwi2response tournier IN/dwi_denoised_preproc_bias_norm.mif IN/response.txt
   average_response */response.txt ../group_average_response.txt


   echo "######################## UPSAMPLING ######################"

   foreach * : mrresize IN/dwi_denoised_preproc_bias_norm.mif -vox 1.25 IN/dwi_denoised_preproc_bias_norm_upsampled.mif

   foreach * : mrresize IN/dwi_mask.mif -vox 1.25 IN/dwi_mask_upsampled.mif


   echo "######################## CONSTRAINED SPHERICAL DECONVOLUTION ######################"

   foreach * : dwiextract IN/dwi_denoised_preproc_bias_norm_upsampled.mif - \| dwi2fod msmt_csd - ../group_average_response.txt IN/wmfod.mif -mask IN/dwi_mask_upsampled.mif

The results actually differ from before:

group average response file
 2083.31075 -855.12128 215.55285 -26.24549 -6.68408 4.50399
first subject stats of dwi2fod input file
 volume       mean     median      stdev        min        max      count
   [ 0 ]    506.905 489.547241    273.962   -80.2037     3093.6     712152
   [ 1 ]    529.001 516.483521    270.006   -58.4171    3525.27     712152
   [ 2 ]    521.184 502.911499    275.681   -56.1507    3320.19     712152
   [ 3 ]    506.825 497.327423    268.625   -52.2752    3215.06     712152
   [ 4 ]    489.759  480.17334    262.887   -120.271    2673.45     712152
   [ 5 ]    518.505 511.272705    267.546   -58.0122    3387.39     712152
   [ 6 ]    483.528   477.8172    257.265   -80.9289    2659.02     712152
   [ 7 ]    512.225 506.774048     265.41    -65.707    3483.83     712152
   [ 8 ]      502.6  489.47522     268.73   -58.6387    3023.06     712152
   [ 9 ]    520.457 507.312134    271.503    -52.984    3466.22     712152
  [ 10 ]    520.603 507.617554    270.696   -49.4302    3271.87     712152
  [ 11 ]    526.604  513.66272    272.146   -92.2372    3621.94     712152
  [ 12 ]    526.825 509.088745    277.901   -77.9474    3637.12     712152
  [ 13 ]    507.171 497.063965    268.098   -53.8041    2786.11     712152
  [ 14 ]    494.363 483.061768    265.031   -60.8391    2868.02     712152
  [ 15 ]    519.943  501.38858    277.559   -66.6179    3646.33     712152
  [ 16 ]    506.122 498.755463    264.919   -76.9495    2852.03     712152
  [ 17 ]    498.519 491.286011    264.057    -74.263    2945.45     712152
  [ 18 ]    522.728 502.063721    279.212   -60.9168    3548.88     712152
  [ 19 ]    514.426 496.440704     276.53   -56.5058    3505.09     712152
  [ 20 ]    504.754 493.688782    267.896    -94.124    2883.75     712152
  [ 21 ]    484.859 479.948608    257.275   -80.1927    2342.96     712152
  [ 22 ]    501.542 495.755035    263.615   -66.3874    2955.96     712152
  [ 23 ]    532.238 516.651062    275.787   -57.7046    3681.62     712152
  [ 24 ]    536.457 527.128906    268.476   -90.4277    3510.33     712152
  [ 25 ]    519.288 513.826721    266.349   -119.806    3384.43     712152
  [ 26 ]    529.952 528.027039    266.165   -89.3878    3460.48     712152
  [ 27 ]    520.142  506.16687    273.101   -57.3928    3433.63     712152
  [ 28 ]    510.836 505.991333    264.849   -59.1262    3291.05     712152
  [ 29 ]    496.326 493.858856    259.968   -74.6783    2738.11     712152
  [ 30 ]    536.339 522.420044     272.91   -85.2825    3697.89     712152
  [ 31 ]    540.424 527.635742    273.249   -83.0235    3743.63     712152
  [ 32 ]    521.249 517.634399    266.418   -84.8236    3101.19     712152
  [ 33 ]    513.844 507.223419    267.214   -70.9725    3054.76     712152
  [ 34 ]    507.497 496.707977    268.946   -94.5467    3326.15     712152
  [ 35 ]    486.256 481.070129    258.177   -70.2519    2423.46     712152
  [ 36 ]    512.626 504.694611     267.67   -73.5083    3484.76     712152
  [ 37 ]    499.872 489.898132    267.101     -119.1    2846.02     712152
  [ 38 ]    505.127 503.619934     262.89   -52.9415    2847.54     712152
  [ 39 ]    532.871 529.504395    267.432   -62.8709    3286.01     712152
  [ 40 ]    496.314 490.312927    261.543   -55.8692    2431.67     712152
  [ 41 ]    532.831 516.199707    277.107   -65.2941     3490.5     712152
  [ 42 ]    491.926 486.516998    260.796   -113.366    2676.27     712152
  [ 43 ]    488.532 486.029877    256.974   -127.953    2525.45     712152
  [ 44 ]    519.646 506.806396    273.925   -105.711    3585.11     712152
  [ 45 ]    508.801 498.109253     269.45   -89.1157    3206.86     712152
  [ 46 ]    529.133 529.883057    264.446   -99.4657    3195.02     712152
  [ 47 ]     502.23 494.719299    265.757   -128.083    2822.13     712152
  [ 48 ]    517.236 503.206726    272.876    -64.614     3469.6     712152
  [ 49 ]    492.914 485.378265    262.313   -98.0561    2546.34     712152
  [ 50 ]    512.281 508.118073    266.237   -69.4468    3093.27     712152
  [ 51 ]    520.099 506.920929    274.541   -143.852    3543.93     712152
  [ 52 ]    523.276 522.950134    264.931   -87.1224    3399.44     712152
  [ 53 ]     492.99 491.940948    258.787   -120.733    2374.98     712152
  [ 54 ]    496.879 493.045776    261.372   -75.4164     2431.2     712152
  [ 55 ]    514.055 505.805695    268.353   -99.1502    3244.71     712152
  [ 56 ]    499.768 495.300323    263.233   -97.8898     2740.5     712152
  [ 57 ]    525.317 523.621521    266.725   -63.2868    3517.57     712152
  [ 58 ]    529.137 524.669373    267.144   -73.0899    3324.68     712152
  [ 59 ]    501.235 497.910278    261.778    -82.866    2477.69     712152
first subject FOD stats
 volume       mean     median      stdev        min        max      count
   [ 0 ]   0.246062 0.242168039   0.123242 -1.21047e-06    1.40547     712152
   [ 1 ] 0.000704112 0.000253242499  0.0677545  -0.518442   0.399922     712152
   [ 2 ] 0.00146226 -1.62822467e-08  0.0660593  -0.483906   0.412824     712152
   [ 3 ] -0.0280566 -0.0286889449  0.0755786  -0.959948    0.50377     712152
   [ 4 ] -0.00200365 -0.00102236588  0.0730182  -0.493734   0.870991     712152
   [ 5 ]  0.0221211 0.0164849497  0.0786315  -0.503422     1.7246     712152
   [ 6 ] -0.00524982 -0.00383628439  0.0741038  -0.881571   0.608548     712152
   [ 7 ] -3.62694e-05 1.90809288e-08  0.0617274  -0.629511   0.578725     712152
   [ 8 ] 0.00548397 0.00522170262  0.0622589  -0.459529   0.450555     712152
   [ 9 ] 0.00340302 0.00219685794  0.0539149  -0.329736   0.385614     712152
  [ 10 ] 0.00327722 0.00237579201  0.0604039  -0.450022   0.555937     712152
  [ 11 ] -0.00117094 -0.00075992936  0.0628149  -0.642574    0.53009     712152
  [ 12 ] -0.0035941 -0.00295428257  0.0635662  -0.766827    0.62017     712152
  [ 13 ] 0.00259942 0.0020810056  0.0596989  -0.490573   0.526188     712152
  [ 14 ]  0.0135822 0.0101279523  0.0724922   -0.65831    1.01618     712152
  [ 15 ] -0.00492966 -0.00291372417  0.0530466  -0.637259   0.475016     712152
  [ 16 ] -0.000969687 -0.000141588389  0.0472709  -0.432165   0.442662     712152
  [ 17 ] 0.00185733 0.00129981642  0.0492435  -0.422543   0.514631     712152
  [ 18 ] -0.00317589 -0.00211325404  0.0458563  -0.390533    0.41144     712152
  [ 19 ] 0.00120177 0.000576774066  0.0441494  -0.399735   0.416407     712152
  [ 20 ] -0.000945908 -0.00071807683  0.0421829  -0.373864   0.360957     712152
  [ 21 ] -0.00676554 -0.00588874845  0.0444572  -0.365893   0.384133     712152
  [ 22 ] 0.00123481 0.000654623727  0.0475055  -0.579266   0.385016     712152
  [ 23 ] 0.00392409 0.00292718643  0.0455281   -0.46979     0.4672     712152
  [ 24 ] 0.00199852 0.00104615558  0.0471879  -0.558439   0.506676     712152
  [ 25 ] -0.00552484 -0.00421489449  0.0487409  -0.455725   0.446856     712152
  [ 26 ] 0.000547852 0.00044011022  0.0464869  -0.467776   0.485717     712152
  [ 27 ] 0.00689332 0.00442822883  0.0514464  -0.560887   0.615824     712152
  [ 28 ] -0.000136915 -1.01215525e-09  0.0272373   -0.28427   0.404843     712152
  [ 29 ] -0.000407091 -8.80946027e-05  0.0252623  -0.218868   0.375264     712152
  [ 30 ] -0.00019012 -1.1754846e-08  0.0248387  -0.300451   0.334473     712152
  [ 31 ]  0.0015493 0.00067578   0.024186  -0.244207   0.274234     712152
  [ 32 ] 0.000587512 0.000196796202  0.0231356  -0.278687   0.276632     712152
  [ 33 ] -0.000223987 4.8436446e-09  0.0223502   -0.24193   0.217364     712152
  [ 34 ] 8.03562e-05 2.09758539e-08  0.0219526  -0.211773   0.242288     712152
  [ 35 ] 0.000528263 0.000227183569  0.0203001  -0.267492   0.195041     712152
  [ 36 ] 0.00245615 0.00133598549  0.0218802  -0.196612   0.281207     712152
  [ 37 ] -0.000243247 -0.000101946694  0.0232006  -0.213491   0.248042     712152
  [ 38 ] -0.000804036 -0.000665134285  0.0224402  -0.185637   0.235582     712152
  [ 39 ] 0.000621964 0.000284874783  0.0227212  -0.213654   0.282768     712152
  [ 40 ] 0.000412708 0.00041280844  0.0229125   -0.22189   0.245559     712152
  [ 41 ] -0.000233566 -6.10678867e-08  0.0240116  -0.296751   0.228732     712152
  [ 42 ] -0.0023722 -0.00139294204  0.0246037  -0.283638   0.283494     712152
  [ 43 ] 0.000720139 0.000233168568  0.0249999  -0.298483   0.342081     712152
  [ 44 ] -8.23215e-05 0.000143957834  0.0271732  -0.368357   0.285055     712152
first subject peaks
 volume       mean     median      stdev        min        max      count
   [ 0 ]   0.227285 0.218588561   0.111426 3.50133e-09    2.17287     7121529

Do these amplitudes now look more normal to you? If so, I guess you were right in assuming that I must have mixed up some files during the process :man_facepalming:t2:


#11

Yes, these numbers look similar to what I get with some examples I have here. Does this solve your original problem?


#12

Again sorry for the late reply, the creation of the population template took quite a while. I produced the template now and was finally also able to create a template AFD peak fixel image, that looks reasonable:

So I can now go on with the standard pipeline. I am extremely curious about the results!

Thanks again a lot for your help and patience in this matter!