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Dear Jesper,
Thank you for your prompt reply, I ran it again with your suggestions (very_verbose; remove ".nii" from output), here is the log file (and I attached my bvals and bvecs files with this email as well).
Georg
uqgkerbl@nessa:/storage/georg/hiRes3T/P01/test/corrected/EDDY$ eddy --imain=rawdata.nii --mask=rrawnodif_brain_mask.nii --acqp=acqparams.txt --index=index.txt --bvecs=bvecs --bvals=bvals --flm=quadratic --niter=5 --very_verbose --out=EDDY_corrected
Reading images
Running Register
Loading prediction maker
Scan: 32 0 46 16 52 30 40 26 58 38 12 62 24 18 22 14 60 2 44 34 6 28 4 42 8 36 50 20 54 56 10 48 1 33 13 27 25 21 29 11 23 19 3 7 63 5 53 39 49 43 55 9 59 51 45 37 17 61 47 35 57 31 15 41
Evaluating prediction maker model
Calculating parameter updates
Iter: 0, scan: 2, mss = 5.721480
Iter: 0, scan: 36, mss = 5.618684
Iter: 0, scan: 22, mss = 5.676166
Iter: 0, scan: 0, mss = 5.732208
Iter: 0, scan: 62, mss = 5.658479
Iter: 0, scan: 26, mss = 5.642103
Iter: 0, scan: 52, mss = 5.683663
Iter: 0, scan: 56, mss = 5.717030
Iter: 0, scan: 60, mss = 5.794613
Iter: 0, scan: 18, mss = 5.625125
Iter: 0, scan: 30, mss = 5.715617
Iter: 0, scan: 14, mss = 5.747114
Iter: 0, scan: 10, mss = 5.663995
Iter: 0, scan: 44, mss = 5.685893
Iter: 0, scan: 16, mss = 5.748338
Iter: 0, scan: 48, mss = 5.699906
Iter: 0, scan: 58, mss = 5.739412
Iter: 0, scan: 40, mss = 5.728986
Iter: 0, scan: 38, mss = 5.689863
Iter: 0, scan: 6, mss = 5.760512
Iter: 0, scan: 12, mss = 5.736676
Iter: 0, scan: 24, mss = 5.726184
Iter: 0, scan: 8, mss = 5.652415
Iter: 0, scan: 46, mss = 5.647322
Iter: 0, scan: 50, mss = 5.634292
Iter: 0, scan: 34, mss = 5.677262
Iter: 0, scan: 42, mss = 5.668443
Iter: 0, scan: 4, mss = 5.797951
Iter: 0, scan: 20, mss = 5.627582
Iter: 0, scan: 54, mss = 5.768896
Iter: 0, scan: 32, mss = 5.748619
Iter: 0, scan: 28, mss = 5.728630
Iter: 0, scan: 3, mss = 5.663502
Iter: 0, scan: 37, mss = 5.650311
Iter: 0, scan: 23, mss = 5.657318
Iter: 0, scan: 1, mss = 5.717710
Iter: 0, scan: 63, mss = 5.693309
Iter: 0, scan: 53, mss = 5.703315
Iter: 0, scan: 49, mss = 5.761735
Iter: 0, scan: 61, mss = 5.768566
Iter: 0, scan: 27, mss = 5.734609
Iter: 0, scan: 25, mss = 5.679650
Iter: 0, scan: 39, mss = 5.741126
Iter: 0, scan: 19, mss = 5.682616
Iter: 0, scan: 35, mss = 5.685178
Iter: 0, scan: 41, mss = 5.736926
Iter: 0, scan: 11, mss = 5.671216
Iter: 0, scan: 45, mss = 5.761335
Iter: 0, scan: 15, mss = 5.656815
Iter: 0, scan: 57, mss = 5.701430
Iter: 0, scan: 7, mss = 5.686036
Iter: 0, scan: 59, mss = 5.700872
Iter: 0, scan: 17, mss = 5.686486
Iter: 0, scan: 43, mss = 5.740903
Iter: 0, scan: 31, mss = 5.805535
Iter: 0, scan: 5, mss = 5.751788
Iter: 0, scan: 33, mss = 5.789402
Iter: 0, scan: 13, mss = 5.623732
Iter: 0, scan: 47, mss = 5.648686
Iter: 0, scan: 9, mss = 5.596861
Iter: 0, scan: 51, mss = 5.704996
Iter: 0, scan: 55, mss = 5.675425
Iter: 0, scan: 29, mss = 5.633173
Iter: 0, scan: 21, mss = 5.712806
Iter: 0, Total mss = 5.70136
Loading prediction maker
Scan: 48 10 24 20 4 32 22 58 26 30 34 16 2 60 40 8 50 12 6 18 0 56 28 62 14 36 38 42 52 44 54 46 41 25 1 51 33 11 43 29 19 23 59 37 27 15 61 53 35 45 7 31 3 47 49 13 55 5 21 9 57 39 63 17
Evaluating prediction maker model
Calculating parameter updates
Iter: 1, scan: 0, mss = 5.732208
Iter: 1, scan: 4, mss = 5.797951
Iter: 1, scan: 30, mss = 5.715617
Iter: 1, scan: 32, mss = 5.748619
Iter: 1, scan: 42, mss = 5.668443
Iter: 1, scan: 46, mss = 5.647322
Iter: 1, scan: 16, mss = 5.748338
Iter: 1, scan: 28, mss = 5.728630
Iter: 1, scan: 52, mss = 5.683663
Iter: 1, scan: 50, mss = 5.634292
Iter: 1, scan: 12, mss = 5.736676
Iter: 1, scan: 8, mss = 5.652415
Iter: 1, scan: 56, mss = 5.717030
Iter: 1, scan: 20, mss = 5.627582
Iter: 1, scan: 54, mss = 5.768896
Iter: 1, scan: 24, mss = 5.726184
Iter: 1, scan: 10, mss = 5.663995
Iter: 1, scan: 62, mss = 5.658479
Iter: 1, scan: 38, mss = 5.689863
Iter: 1, scan: 6, mss = 5.760512
Iter: 1, scan: 22, mss = 5.676166
Iter: 1, scan: 2, mss = 5.721480
Iter: 1, scan: 14, mss = 5.747114
Iter: 1, scan: 18, mss = 5.625125
Iter: 1, scan: 34, mss = 5.677262
Iter: 1, scan: 58, mss = 5.739412
Iter: 1, scan: 60, mss = 5.794613
Iter: 1, scan: 26, mss = 5.642103
Iter: 1, scan: 40, mss = 5.728986
Iter: 1, scan: 44, mss = 5.685893
Iter: 1, scan: 36, mss = 5.618684
Iter: 1, scan: 48, mss = 5.699906
Iter: 1, scan: 1, mss = 5.717710
Iter: 1, scan: 31, mss = 5.805535
Iter: 1, scan: 5, mss = 5.751788
Iter: 1, scan: 43, mss = 5.740903
Iter: 1, scan: 17, mss = 5.686486
Iter: 1, scan: 13, mss = 5.623732
Iter: 1, scan: 47, mss = 5.648686
Iter: 1, scan: 55, mss = 5.675425
Iter: 1, scan: 33, mss = 5.789402
Iter: 1, scan: 51, mss = 5.704996
Iter: 1, scan: 29, mss = 5.633173
Iter: 1, scan: 9, mss = 5.596861
Iter: 1, scan: 21, mss = 5.712806
Iter: 1, scan: 19, mss = 5.682616
Iter: 1, scan: 63, mss = 5.693309
Iter: 1, scan: 23, mss = 5.657318
Iter: 1, scan: 7, mss = 5.686036
Iter: 1, scan: 61, mss = 5.768566
Iter: 1, scan: 39, mss = 5.741126
Iter: 1, scan: 3, mss = 5.663502
Iter: 1, scan: 45, mss = 5.761335
Iter: 1, scan: 11, mss = 5.671216
Iter: 1, scan: 37, mss = 5.650311
Iter: 1, scan: 27, mss = 5.734609
Iter: 1, scan: 57, mss = 5.701430
Iter: 1, scan: 41, mss = 5.736926
Iter: 1, scan: 25, mss = 5.679650
Iter: 1, scan: 53, mss = 5.703315
Iter: 1, scan: 59, mss = 5.700872
Iter: 1, scan: 35, mss = 5.685178
Iter: 1, scan: 49, mss = 5.761735
Iter: 1, scan: 15, mss = 5.656815
Iter: 1, Total mss = 5.70136
Loading prediction maker
Scan: 58 28 54 40 14 26 48 4 30 18 8 46 50 0 6 20 38 60 10 16 2 44 42 62 36 22 52 56 12 24 34 32 27 1 33 7 43 63 51 39 47 23 49 37 15 29 3 55 25 9 5 17 35 53 59 61 57 45 31 19 21 41 11 13
Evaluating prediction maker model
Calculating parameter updates
Iter: 2, scan: 38, mss = 5.689863
Iter: 2, scan: 22, mss = 5.676166
Iter: 2, scan: 12, mss = 5.736676
Iter: 2, scan: 6, mss = 5.760512
Iter: 2, scan: 0, mss = 5.732208
Iter: 2, scan: 34, mss = 5.677262
Iter: 2, scan: 50, mss = 5.634292
Iter: 2, scan: 14, mss = 5.747114
Iter: 2, scan: 60, mss = 5.794613
Iter: 2, scan: 2, mss = 5.721480
Iter: 2, scan: 30, mss = 5.715617
Iter: 2, scan: 54, mss = 5.768896
Iter: 2, scan: 58, mss = 5.739412
Iter: 2, scan: 18, mss = 5.625125
Iter: 2, scan: 20, mss = 5.627582
Iter: 2, scan: 42, mss = 5.668443
Iter: 2, scan: 52, mss = 5.683663
Iter: 2, scan: 48, mss = 5.699906
Iter: 2, scan: 36, mss = 5.618684
Iter: 2, scan: 10, mss = 5.663995
Iter: 2, scan: 24, mss = 5.726184
Iter: 2, scan: 44, mss = 5.685893
Iter: 2, scan: 26, mss = 5.642103
Iter: 2, scan: 28, mss = 5.728630
Iter: 2, scan: 46, mss = 5.647322
Iter: 2, scan: 4, mss = 5.797951
Iter: 2, scan: 40, mss = 5.728986
Iter: 2, scan: 32, mss = 5.748619
Iter: 2, scan: 8, mss = 5.652415
Iter: 2, scan: 56, mss = 5.717030
Iter: 2, scan: 62, mss = 5.658479
Iter: 2, scan: 16, mss = 5.748338
Iter: 2, scan: 23, mss = 5.657318
Iter: 2, scan: 3, mss = 5.663502
Iter: 2, scan: 7, mss = 5.686036
Iter: 2, scan: 39, mss = 5.741126
Iter: 2, scan: 35, mss = 5.685178
Iter: 2, scan: 15, mss = 5.656815
Iter: 2, scan: 61, mss = 5.768566
Iter: 2, scan: 59, mss = 5.700872
Iter: 2, scan: 11, mss = 5.671216
Iter: 2, scan: 49, mss = 5.761735
Iter: 2, scan: 31, mss = 5.805535
Iter: 2, scan: 53, mss = 5.703315
Iter: 2, scan: 27, mss = 5.734609
Iter: 2, scan: 13, mss = 5.623732
Iter: 2, scan: 45, mss = 5.761335
Iter: 2, scan: 37, mss = 5.650311
Iter: 2, scan: 55, mss = 5.675425
Iter: 2, scan: 57, mss = 5.701430
Iter: 2, scan: 41, mss = 5.736926
Iter: 2, scan: 25, mss = 5.679650
Iter: 2, scan: 19, mss = 5.682616
Iter: 2, scan: 51, mss = 5.704996
Iter: 2, scan: 21, mss = 5.712806
Iter: 2, scan: 43, mss = 5.740903
Iter: 2, scan: 1, mss = 5.717710
Iter: 2, scan: 17, mss = 5.686486
Iter: 2, scan: 9, mss = 5.596861
Iter: 2, scan: 5, mss = 5.751788
Iter: 2, scan: 33, mss = 5.789402
Iter: 2, scan: 63, mss = 5.693309
Iter: 2, scan: 29, mss = 5.633173
Iter: 2, scan: 47, mss = 5.648686
Iter: 2, Total mss = 5.70136
Loading prediction maker
Scan: 44 50 0 8 32 4 52 56 40 18 16 36 42 20 28 30 38 48 54 6 58 46 22 60 62 14 2 26 34 12 10 24 1 45 51 57 27 29 63 11 7 59 9 13 3 39 19 17 41 5 47 43 33 37 15 25 21 49 53 31 35 55 23 61
Evaluating prediction maker model
Calculating parameter updates
Iter: 3, scan: 0, mss = 5.732208
Iter: 3, scan: 14, mss = 5.747114
Iter: 3, scan: 38, mss = 5.689863
Iter: 3, scan: 12, mss = 5.736676
Iter: 3, scan: 26, mss = 5.642103
Iter: 3, scan: 30, mss = 5.715617
Iter: 3, scan: 58, mss = 5.739412
Iter: 3, scan: 46, mss = 5.647322
Iter: 3, scan: 50, mss = 5.634292
Iter: 3, scan: 16, mss = 5.748338
Iter: 3, scan: 20, mss = 5.627582
Iter: 3, scan: 2, mss = 5.721480
Iter: 3, scan: 32, mss = 5.748619
Iter: 3, scan: 18, mss = 5.625125
Iter: 3, scan: 4, mss = 5.797951
Iter: 3, scan: 10, mss = 5.663995
Iter: 3, scan: 42, mss = 5.668443
Iter: 3, scan: 62, mss = 5.658479
Iter: 3, scan: 24, mss = 5.726184
Iter: 3, scan: 36, mss = 5.618684
Iter: 3, scan: 56, mss = 5.717030
Iter: 3, scan: 28, mss = 5.728630
Iter: 3, scan: 8, mss = 5.652415
Iter: 3, scan: 34, mss = 5.677262
Iter: 3, scan: 22, mss = 5.676166
Iter: 3, scan: 60, mss = 5.794613
Iter: 3, scan: 48, mss = 5.699906
Iter: 3, scan: 6, mss = 5.760512
Iter: 3, scan: 40, mss = 5.728986
Iter: 3, scan: 44, mss = 5.685893
Iter: 3, scan: 54, mss = 5.768896
Iter: 3, scan: 52, mss = 5.683663
Iter: 3, scan: 1, mss = 5.717710
Iter: 3, scan: 13, mss = 5.623732
Iter: 3, scan: 39, mss = 5.741126
Iter: 3, scan: 47, mss = 5.648686
Iter: 3, scan: 31, mss = 5.805535
Iter: 3, scan: 21, mss = 5.712806
Iter: 3, scan: 37, mss = 5.650311
Iter: 3, scan: 17, mss = 5.686486
Iter: 3, scan: 63, mss = 5.693309
Iter: 3, scan: 33, mss = 5.789402
Iter: 3, scan: 15, mss = 5.656815
Iter: 3, scan: 5, mss = 5.751788
Iter: 3, scan: 19, mss = 5.682616
Iter: 3, scan: 51, mss = 5.704996
Iter: 3, scan: 43, mss = 5.740903
Iter: 3, scan: 55, mss = 5.675425
Iter: 3, scan: 29, mss = 5.633173
Iter: 3, scan: 11, mss = 5.671216
Iter: 3, scan: 9, mss = 5.596861
Iter: 3, scan: 25, mss = 5.679650
Iter: 3, scan: 41, mss = 5.736926
Iter: 3, scan: 59, mss = 5.700872
Iter: 3, scan: 27, mss = 5.734609
Iter: 3, scan: 57, mss = 5.701430
Iter: 3, scan: 7, mss = 5.686036
Iter: 3, scan: 61, mss = 5.768566
Iter: 3, scan: 45, mss = 5.761335
Iter: 3, scan: 3, mss = 5.663502
Iter: 3, scan: 23, mss = 5.657318
Iter: 3, scan: 35, mss = 5.685178
Iter: 3, scan: 53, mss = 5.703315
Iter: 3, scan: 49, mss = 5.761735
Iter: 3, Total mss = 5.70136
Loading prediction maker
Scan: 48 38 52 58 22 34 60 16 32 36 30 24 0 14 44 40 42 28 4 8 62 10 54 56 26 20 46 6 12 2 18 50 49 1 29 53 31 59 33 39 57 3 41 55 15 27 61 43 45 23 11 25 35 63 51 7 13 37 17 47 9 21 5 19
Evaluating prediction maker model
Calculating parameter updates
Iter: 4, scan: 2, mss = 5.721480
Iter: 4, scan: 18, mss = 5.625125
Iter: 4, scan: 52, mss = 5.683663
Iter: 4, scan: 8, mss = 5.652415
Iter: 4, scan: 46, mss = 5.647322
Iter: 4, scan: 42, mss = 5.668443
Iter: 4, scan: 26, mss = 5.642103
Iter: 4, scan: 48, mss = 5.699906
Iter: 4, scan: 24, mss = 5.726184
Iter: 4, scan: 54, mss = 5.768896
Iter: 4, scan: 38, mss = 5.689863
Iter: 4, scan: 28, mss = 5.728630
Iter: 4, scan: 20, mss = 5.627582
Iter: 4, scan: 62, mss = 5.658479
Iter: 4, scan: 6, mss = 5.760512
Iter: 4, scan: 50, mss = 5.634292
Iter: 4, scan: 32, mss = 5.748619
Iter: 4, scan: 4, mss = 5.797951
Iter: 4, scan: 36, mss = 5.618684
Iter: 4, scan: 12, mss = 5.736676
Iter: 4, scan: 30, mss = 5.715617
Iter: 4, scan: 40, mss = 5.728986
Iter: 4, scan: 56, mss = 5.717030
Iter: 4, scan: 44, mss = 5.685893
Iter: 4, scan: 14, mss = 5.747114
Iter: 4, scan: 16, mss = 5.748338
Iter: 4, scan: 22, mss = 5.676166
Iter: 4, scan: 10, mss = 5.663995
Iter: 4, scan: 0, mss = 5.732208
Iter: 4, scan: 58, mss = 5.739412
Iter: 4, scan: 60, mss = 5.794613
Iter: 4, scan: 34, mss = 5.677262
Iter: 4, scan: 19, mss = 5.682616
Iter: 4, scan: 9, mss = 5.596861
Iter: 4, scan: 3, mss = 5.663502
Iter: 4, scan: 7, mss = 5.686036
Iter: 4, scan: 29, mss = 5.633173
Iter: 4, scan: 63, mss = 5.693309
Iter: 4, scan: 1, mss = 5.717710
Iter: 4, scan: 53, mss = 5.703315
Iter: 4, scan: 21, mss = 5.712806
Iter: 4, scan: 31, mss = 5.805535
Iter: 4, scan: 37, mss = 5.650311
Iter: 4, scan: 43, mss = 5.740903
Iter: 4, scan: 33, mss = 5.789402
Iter: 4, scan: 5, mss = 5.751788
Iter: 4, scan: 51, mss = 5.704996
Iter: 4, scan: 39, mss = 5.741126
Iter: 4, scan: 41, mss = 5.736926
Iter: 4, scan: 27, mss = 5.734609
Iter: 4, scan: 55, mss = 5.675425
Iter: 4, scan: 47, mss = 5.648686
Iter: 4, scan: 11, mss = 5.671216
Iter: 4, scan: 25, mss = 5.679650
Iter: 4, scan: 45, mss = 5.761335
Iter: 4, scan: 57, mss = 5.701430
Iter: 4, scan: 49, mss = 5.761735
Iter: 4, scan: 17, mss = 5.686486
Iter: 4, scan: 15, mss = 5.656815
Iter: 4, scan: 23, mss = 5.657318
Iter: 4, scan: 13, mss = 5.623732
Iter: 4, scan: 61, mss = 5.768566
Iter: 4, scan: 59, mss = 5.700872
Iter: 4, scan: 35, mss = 5.685178
Iter: 4, Total mss = 5.70136
Running Register
Loading prediction maker
Scan: 0
Evaluating prediction maker model
Calculating parameter updates
Iter: 0, scan: 0, mss = 312.456803
Iter: 0, Total mss = 312.457
Loading prediction maker
Scan: 0
Evaluating prediction maker model
Calculating parameter updates
Iter: 1, scan: 0, mss = 312.456803
Iter: 1, Total mss = 312.457
Loading prediction maker
Scan: 0
Evaluating prediction maker model
Calculating parameter updates
Iter: 2, scan: 0, mss = 312.456803
Iter: 2, Total mss = 312.457
Loading prediction maker
Scan: 0
Evaluating prediction maker model
Calculating parameter updates
Iter: 3, scan: 0, mss = 312.456803
Iter: 3, Total mss = 312.457
Loading prediction maker
Scan: 0
Evaluating prediction maker model
Calculating parameter updates
Iter: 4, scan: 0, mss = 312.456803
Iter: 4, Total mss = 312.457
Running sm.SetDWIReference
Running sm.Setb0Reference
Running sm.WriteParameterFile
Running sm.WriteRegisteredImages
Running sm.WriteECFields
On Mar 20, 2015, at 12:01 AM, Jesper Andersson wrote:
> Dear Georg,
>
>>
>> I am working on a DWI data set (n=20; 64 directions; one b0; b=3000; PE dir=A>>P; Echo spacing=0.82ms; EPI factor=128) and currently I'm trying to preprocess this data for later tractography analysis.
>>
>> Unfortunately there were no b0's with reversed phase-encoding collected for the data set, so I can't run topup (I have field maps however, so considering epi_reg later).
>> I have used eddy_correct and processed the data successfully, however now I want to use the new(er) EDDY to preprocess and then compare to the output to eddy_correct (are these expected to be significantly different?).
>
> I would expect them to be different.
>
>>
>> When I run EDDY I use the following command:
>>
>> eddy --imain=rawdata.nii --mask=rawnodif_brain_mask.nii --acqp=acqparams.txt --index=index.txt --bvecs=bvecs --bvals=bvals --flm=quadratic --niter=5 --verbose --out=EDDY_corrected_rawdata.nii
>>
>> My parameter files are:
>>
>> acqparams.txt
>>
>> 0 -1 0 0.104
>>
>> index.txt
>>
>> 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
>>
>> I don't get any errors when I run EDDY, but the output 4D is (visually) not any different from my input raw data file; whereas i.e. the output of eddy_correct is clearly different from the raw data.
>> What am I doing wrong?
>
> it is hard from this description to know what is going on. Maybe you can run it again, this time removing .nii from the —out parameter and add —very_verbose? This would give you lots of debug information to the screen that you could maybe then cut-and-paste into an email to me?
>
> Jesper
>
>
>>
>> Thank you for helping,
>> Best,
>>
>> Georg
>>
>> Georg Kerbler
>> Postdoctoral Research Fellow
>> Brain and Action Laboratory
>> Queensland Brain Institute
>> University of Queensland
>> Building 79 Upland Road, St Lucia QLD 4072
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