Hi there,
I’m totally new to diffusion imaging and using MRtrix3.0.3 for the first time to preprocess some multi-shell dwi data.
Broadly, the dwi acquisition was done on a 7T Siemens Magnetom: b=2500 x 60dir, b=1000 x 30dir, b=0 x 6 dir all acquired in AP, and one b=0 acquired in PA direction.
The raw (bidsified) data I input look like this:
sub-081_ses-02_acq-b0orig_dir-PA_dwi.bval
sub-081_ses-02_acq-b0orig_dir-PA_dwi.bvec
sub-081_ses-02_acq-b0orig_dir-PA_dwi.json
sub-081_ses-02_acq-b0orig_dir-PA_dwi.nii.gz
sub-081_ses-02_acq-b25001000d060306orig_dir-AP_dwi.bval
sub-081_ses-02_acq-b25001000d060306orig_dir-AP_dwi.bvec
sub-081_ses-02_acq-b25001000d060306orig_dir-AP_dwi.json
sub-081_ses-02_acq-b25001000d060306orig_dir-AP_dwi.nii.gz
Plus a T1w image.
While running this command:
dwi2response dhollander dwi_den_preproc_unbiased.mif wm.txt gm.txt csf.txt -voxels voxels.mif
I’m running into this error:
dwi2response: [ERROR] mrcalc refined_gm.mif safe_sdm.mif 1.38035095 -subtract -abs 1 -add 0 -if - | mrthreshold - - -bottom 0 -ignorezero | mrcalc refined_gm.mif - 0 -if - -datatype bit | mrconvert - voxels_gm.mif -axes 0,1,2 (dhollander.py:220)
dwi2response: [ERROR] Information from failed command:
dwi2response:
]
mrthreshold: [ERROR] value supplied for option "bottom" is out of bounds (valid range: 1 to 9223372036854775807, value supplied: 0)
mrcalc: [ERROR] Could not interpret string "-" as either an image path or a numerical value
This is all the output from dwi2response immediately before the error:
dwi2response:
dwi2response: Note that this script makes use of commands / algorithms that have relevant articles for citation. Please consult the help page (-help option) for more information.
dwi2response:
dwi2response: Generated scratch directory: /scratch/cvl/uqywards/data/bidsdwi/sub-081/ses-02/dwi2response-tmp-ZJQS82/
dwi2response: Importing DWI data (/scratch/cvl/uqywards/data/bidsdwi/sub-081/ses-02/dwi_den_preproc_unbiased.mif)...
dwi2response: Changing to scratch directory (/scratch/cvl/uqywards/data/bidsdwi/sub-081/ses-02/dwi2response-tmp-ZJQS82/)
dwi2response: Computing brain mask (dwi2mask)...
dwi2response: -------
dwi2response: 3 unique b-value(s) detected: 0,1001,2500 with 6,30,60 volumes
dwi2response: -------
dwi2response: Preparation:
dwi2response: * Eroding brain mask by 3 pass(es)...
dwi2response: [ mask: 191895 -> 145250 ]
dwi2response: * Computing signal decay metric (SDM):
dwi2response: * b=0...
dwi2response: * b=1001...
dwi2response: * b=2500...
dwi2response: * Removing erroneous voxels from mask and correcting SDM...
dwi2response: [ mask: 145250 -> 145221 ]
dwi2response: -------
dwi2response: Crude segmentation:
dwi2response: * Crude WM versus GM-CSF separation (at FA=0.2)...
dwi2response: [ 145221 -> 145204 (WM) & 17 (GM-CSF) ]
dwi2response: * Crude GM versus CSF separation...
dwi2response: [ 17 -> 14 (GM) & 3 (CSF) ]
dwi2response: -------
dwi2response: Refined segmentation:
dwi2response: * Refining WM...
dwi2response: [ WM: 145204 -> 119426 ]
dwi2response: * Refining GM...
dwi2response: [ GM: 14 -> 11 ]
dwi2response: * Refining CSF...
dwi2response: [ CSF: 3 -> 6949 ]
dwi2response: -------
dwi2response: Final voxel selection and response function estimation:
dwi2response: * CSF:
dwi2response: * Selecting final voxels (10.0% of refined CSF)...
dwi2response: [ CSF: 6949 -> 695 ]
dwi2response: * Estimating response function...
dwi2response: * GM:
dwi2response: * Selecting final voxels (2.0% of refined GM)...
The strange thing is that I’m only getting this error on roughly 1/6 datasets, all the other subjects’ data runs perfectly fine. All subjects’ data was acquired and handled in exactly the same way up until this point.
I’ve checked that the following mrstats output looks sensible (as per Dwi2response dhollander with single-shell data - #4 by ThijsDhollander) , and looks the more or less the same between the subjects that ran successfully vs the unsuccessful. I couldn’t see any obvious inconsistencies between those two groups. This is the output for an unsuccessful subject:
mrstats dwi_den_preproc_unbiased.mif
volume mean median std min max count
[ 0 ] 225.604 26.0696 428.513 -77.8928 4359.29 792000
[ 1 ] 33.5253 10.8098 46.7958 -9.72118 699.59 792000
[ 2 ] 68.5331 13.1319 98.4974 -18.7232 1447.52 792000
[ 3 ] 32.9465 10.9196 43.9799 -10.509 541.876 792000
[ 4 ] 33.6652 10.9169 46.0688 -18.6469 812.889 792000
[ 5 ] 68.0234 12.9603 98.8386 -41.9257 1429.11 792000
[ 6 ] 33.3437 11.0449 45.5428 -29.2986 586.589 792000
[ 7 ] 33.1947 11.0987 44.512 -17.8611 711.573 792000
[ 8 ] 68.8179 12.9325 99.3377 -46.1274 1206.68 792000
[ 9 ] 32.5754 10.7389 44.0108 -25.5638 608.957 792000
[ 10 ] 32.8976 10.8686 44.5712 -25.2744 660.122 792000
[ 11 ] 68.1469 12.4588 99.882 -68.533 1534.8 792000
[ 12 ] 33.3571 10.98 45.5524 -24.6983 672.529 792000
[ 13 ] 33.8293 10.9306 45.7618 -40.3466 500.997 792000
[ 14 ] 68.3923 12.7722 98.8272 -77.6986 1368.13 792000
[ 15 ] 33.1235 11.0074 44.7503 -34.2009 731.715 792000
[ 16 ] 225.372 26.0705 427.22 -161.473 4335.47 792000
[ 17 ] 32.5487 10.8458 43.7321 -36.7623 600.417 792000
[ 18 ] 68.4628 12.8421 98.8906 -93.4583 1139.42 792000
[ 19 ] 33.2623 10.9362 45.4955 -43.3604 569.888 792000
[ 20 ] 32.9939 10.9871 43.9228 -34.3732 501.919 792000
[ 21 ] 68.5864 12.557 100.2 -89.4346 1585.51 792000
[ 22 ] 33.6954 11.1862 45.2316 -40.8593 812.241 792000
[ 23 ] 33.1582 10.979 45.1631 -29.897 584.966 792000
[ 24 ] 67.462 12.4383 99.268 -94.2682 1453.97 792000
[ 25 ] 33.4161 10.817 46.3118 -30.7029 740.809 792000
[ 26 ] 33.4556 11.0815 45.1121 -26.036 688.536 792000
[ 27 ] 68.2906 12.823 98.5451 -68.325 1575.81 792000
[ 28 ] 33.4337 10.9955 44.932 -31.4385 768.656 792000
[ 29 ] 33.4882 10.9316 46.0712 -33.2577 478.714 792000
[ 30 ] 67.8546 12.6469 98.6953 -67.8498 1212.05 792000
[ 31 ] 33.2393 10.6802 46.2148 -34.8093 755.174 792000
[ 32 ] 224.674 25.8476 425.058 -123.557 4389.78 792000
[ 33 ] 33.8282 10.9166 45.9179 -38.0568 486.606 792000
[ 34 ] 68.59 12.8053 99.4243 -61.7393 1151.61 792000
[ 35 ] 33.1781 10.994 44.5938 -12.927 572.43 792000
[ 36 ] 33.8559 11.021 45.9852 -27.8023 501.257 792000
[ 37 ] 68.9705 12.9136 99.6649 -48.9331 1397.56 792000
[ 38 ] 33.2831 10.9104 45.4402 -13.1544 505.805 792000
[ 39 ] 33.3575 11.0789 44.5915 -24.3367 790.146 792000
[ 40 ] 67.2569 12.6381 97.3887 -55.1866 1172.4 792000
[ 41 ] 33.5339 11.0714 45.7987 -18.8319 507.55 792000
[ 42 ] 32.8478 11.0191 44.0817 -14.9116 649.516 792000
[ 43 ] 67.2741 12.5505 98.0491 -63.9307 1177.29 792000
[ 44 ] 33.8165 10.8566 46.9971 -21.057 804.612 792000
[ 45 ] 32.8388 10.8857 44.5418 -9.06007 688.542 792000
[ 46 ] 68.4657 12.9688 99.269 -41.7669 1608.21 792000
[ 47 ] 33.62 10.9822 45.5036 -24.8433 771.574 792000
[ 48 ] 225.496 26.2786 427.051 -155.858 4355.98 792000
[ 49 ] 33.77 10.9479 46.2539 -25.9326 507.89 792000
[ 50 ] 67.3933 12.6131 97.9132 -38.0978 1215.99 792000
[ 51 ] 33.0295 10.9499 43.9517 -11.3667 515.787 792000
[ 52 ] 33.5896 11.0367 45.2422 -14.3615 588.068 792000
[ 53 ] 67.2486 12.4537 98.5836 -38.036 1397.26 792000
[ 54 ] 32.6396 10.8519 44.3738 -11.3554 714.447 792000
[ 55 ] 33.1745 10.9821 44.8263 -23.6144 489.026 792000
[ 56 ] 67.8408 12.8236 97.9866 -46.3754 1155.67 792000
[ 57 ] 32.9807 11.0555 44.5212 -11.5739 540.78 792000
[ 58 ] 33.7414 10.9972 45.3545 -17.3297 506.522 792000
[ 59 ] 68.4033 12.8414 98.9654 -27.7487 1174.67 792000
[ 60 ] 32.9253 10.9542 44.1526 -7.96499 594.321 792000
[ 61 ] 33.4991 11.0728 45.3298 -9.00119 660.986 792000
[ 62 ] 68.6834 12.6427 100.182 -19.8394 1090.9 792000
[ 63 ] 33.516 10.9901 45.3049 -8.4031 756.794 792000
[ 64 ] 224.653 26.1813 425.723 -128.348 4373.91 792000
[ 65 ] 33.3461 10.9638 45.6967 -30.6652 497.677 792000
[ 66 ] 68.4454 12.6604 99.5934 -13.3198 1118.48 792000
[ 67 ] 33.3317 11.1403 44.5546 -11.6457 602.121 792000
[ 68 ] 33.5981 11.0683 45.3974 -8.46102 566.477 792000
[ 69 ] 68.5784 13.0115 98.7589 -15.3578 1567.76 792000
[ 70 ] 33.5136 11.0097 45.1391 -10.5454 667.103 792000
[ 71 ] 33.5131 10.9283 45.3518 -11.0038 757.323 792000
[ 72 ] 67.9993 12.8271 98.0638 -14.6371 1170.59 792000
[ 73 ] 33.1867 11.2213 43.9231 -8.08128 641.989 792000
[ 74 ] 33.1309 11.066 44.3074 -12.8869 683.116 792000
[ 75 ] 68.563 12.9466 98.6603 -21.376 1320.43 792000
[ 76 ] 33.04 10.861 45.4148 -12.9685 768.963 792000
[ 77 ] 32.6577 10.8141 44.3012 -11.8498 684.515 792000
[ 78 ] 68.1491 13.0069 98.698 -20.8514 1613.46 792000
[ 79 ] 33.4766 10.9863 45.2155 -8.6721 641.386 792000
[ 80 ] 225.241 26.3994 425.874 -148.569 4332.54 792000
[ 81 ] 33.8255 11.0089 45.6156 -15.2462 518.511 792000
[ 82 ] 68.0764 12.7456 98.7705 -20.5782 1131.55 792000
[ 83 ] 33.0691 11.0475 43.9437 -11.979 587.742 792000
[ 84 ] 33.7105 10.748 47.2111 -6.91505 789.475 792000
[ 85 ] 68.0934 13.0205 98.5841 -19.6995 1439.53 792000
[ 86 ] 33.2425 10.8682 45.653 -11.8344 704.633 792000
[ 87 ] 33.118 11.0392 44.426 -19.6083 787.107 792000
[ 88 ] 67.2247 12.4361 98.8212 -15.7834 1440.03 792000
[ 89 ] 32.9667 11.1284 44.1834 -12.3513 698.324 792000
[ 90 ] 33.1401 11.0704 44.9349 -36.0062 663.574 792000
[ 91 ] 68.042 12.7301 98.519 -32.6245 1182.3 792000
[ 92 ] 33.6192 10.8127 47.0035 -7.50583 741.346 792000
[ 93 ] 33.9159 10.9589 46.2415 -17.1549 542.396 792000
[ 94 ] 68.7463 12.7124 100.225 -20.9229 1667.04 792000
[ 95 ] 33.5328 10.8954 46.2297 -14.5156 759.26 792000
I’m not sure if this is relevant for my exact error, but this is a small snippet of the output from the “mrinfo dwi_den_preproc_unbiased.mif -dwgrad” command. I believe it also looks sensible, and again looks roughly the same for subjects that ran successfully vs unsuccessfully.
mrinfo dwi_den_preproc_unbiased.mif -dwgrad
-0.5855320966696912 0.5340646921723553 0.6098582362684321 0
-0.5766456784261192 -0.5762032649260992 -0.5791973403262348 2500
0.5784417000926612 -0.5756298712926968 -0.5779753029926671 995
-0.5782468337016529 -0.5788247019016545 0.5749717939016434 2510
-0.5765702972849575 -0.5757757828849782 -0.579697283184876 2490
0.5774875239074091 0.5803309004074456 -0.5742161664073671 995
0.5759777777091984 0.5750914549091842 0.580964214109278 2500
0.5775957047130471 0.5813047464131309 -0.5731212731129461 2495
-0.5763089908933897 0.5802078192933451 -0.5755230954933989 1000
0.5761960273095779 -0.5808815071096557 0.5749563573095573 2510
-0.57841485232055 -0.5808520206206366 0.5727540386203488 2505
-0.5779813888127195 -0.5815784520127987 0.5724543810125979 1000
0.5791337037825004 -0.5731292949826819 -0.5797645766824815 2495
0.5751383955640389 0.5735043105641411 0.5833597789635249 2510
0.5790893279149646 -0.572801649314802 -0.5801325890149915 1000
-0.5782977910987848 -0.5821028155987767 0.5716012393987988 2500
The issue happens regardless of whether I attempt to run several participants at the same time, or if I run them one by one. I don’t have any problems with storage space.
Do you have any idea why some datasets are running into this error and what I can do to further debug/fix the issue? I’d really appreciate any help.
Kind regards
Yohan