GetQCOutputDirectory 1 0 0 0 -1 0 0 0 1 1 0 0 0 1 0 0 0 1 valueRadius:3 valueRadius:3 valueRadius:0 Test in JointDenoising Filter:2 Test in JointDenoising Filter:2 Test in JointDenoising Filter:1 ================================ QC Protocol ================================ QCOutputDirectory: QCedDWIFileNameSuffix: _QCed.nrrd ReportFileNameSuffix: _QCReport.txt BadGradientPercentageTolerance: 0.2 reportType: SIMPLE ================================ Image Protocol ================================ bCheck: Yes type: unsigned short space: left-posterior-superior space directions: 1 0 0 0 -1 0 0 0 1 dimension: 3 size: 142 128 111 0 spacing: 1 1 1 origin: -108 95 -88 bCrop: Yes croppedDWIFileNameSuffix: _CroppedDWI.nrrd reportFileNameSuffix: _QCReport.txt reportFileMode: append bQuitOnCheckSpacingFailure: false bQuitOnCheckSizeFailure: false ================================ Diffusion Protocol ================================ bCheck: Yes measurementFrame: 1 0 0 0 1 0 0 0 1 DWMRI_b-value: 1000 DWMRI_gradient_0 [ 0.00000000000000000e+00, -1.84067000000000008e-01, 2.19216999999999995e-01 ] DWMRI_gradient_1 [ 4.23597000000000001e-01, -3.90456000000000025e-01, -1.95107000000000003e-01 ] DWMRI_gradient_2 [ -8.52404000000000051e-01, -7.21265000000000045e-01, -3.86577999999999977e-01 ] DWMRI_gradient_3 [ -7.16921999999999948e-01, -6.55004000000000031e-01, -3.24166000000000010e-01 ] DWMRI_gradient_4 [ -3.40532999999999975e-01, -6.61125999999999991e-01, -9.80709999999999971e-01 ] DWMRI_gradient_5 [ -8.63373999999999975e-01, -1.30969999999999993e-02, -2.96100000000000009e-03 ] DWMRI_gradient_6 [ 6.41083999999999987e-01, 3.11724999999999974e-01, 2.21010000000000012e-01 ] DWMRI_gradient_7 [ 1.81147000000000002e-01, 6.48745000000000016e-01, 3.24938999999999978e-01 ] DWMRI_gradient_8 [ 2.09740000000000010e-01, 4.19049000000000005e-01, 7.47805999999999971e-01 ] DWMRI_gradient_9 [ 7.41017000000000037e-01, 8.57037000000000049e-01, 8.44011999999999984e-01 ] DWMRI_gradient_10 [ 9.77009000000000016e-01, 0.00000000000000000e+00, 4.23570999999999975e-01 ] DWMRI_gradient_11 [ 4.23719999999999986e-01, 7.44370000000000032e-02, 6.64768000000000026e-01 ] DWMRI_gradient_12 [ 9.05823000000000045e-01, 5.04981000000000013e-01, 4.64926000000000006e-01 ] DWMRI_gradient_13 [ 7.39210000000000006e-02, 9.95999999999999941e-02, 7.45828000000000046e-01 ] DWMRI_gradient_14 [ 9.45998000000000006e-01, 5.96015999999999990e-01, 5.71234000000000020e-01 ] DWMRI_gradient_15 [ 1.82339000000000001e-01, 2.22882999999999998e-01, 7.95745000000000036e-01 ] DWMRI_gradient_16 [ 9.70423000000000036e-01, 5.71402999999999994e-01, 5.96088999999999980e-01 ] DWMRI_gradient_17 [ -2.75907000000000013e-01, 2.73990000000000011e-01, 7.46086999999999945e-01 ] DWMRI_gradient_18 [ 9.45671000000000039e-01, 9.04170999999999947e-01, 6.65085999999999955e-01 ] DWMRI_gradient_19 [ 9.99209999999999959e-02, 4.64341999999999977e-01, 5.05291000000000046e-01 ] DWMRI_gradient_20 [ 2.23110000000000003e-01, 1.82130999999999987e-01, 0.00000000000000000e+00 ] DWMRI_gradient_21 [ -8.86965000000000003e-01, -8.78866000000000036e-01, -9.02787000000000006e-01 ] DWMRI_gradient_22 [ -6.36889000000000038e-01, -3.76056000000000001e-01, -1.35655999999999999e-01 ] DWMRI_gradient_23 [ -5.13440000000000007e-01, -9.19289000000000023e-01, -6.90002000000000004e-01 ] DWMRI_gradient_24 [ 1.21288000000000007e-01, 2.24300000000000020e-03, 7.27187999999999946e-01 ] DWMRI_gradient_25 [ 4.86420000000000019e-01, 7.04329999999999956e-02, 4.52668000000000015e-01 ] DWMRI_gradient_26 [ 6.05491000000000001e-01, 2.41391999999999995e-01, 5.12356999999999951e-01 ] DWMRI_gradient_27 [ 7.39936000000000038e-01, -9.35429999999999984e-01, 9.44517999999999969e-01 ] DWMRI_gradient_28 [ 1.49946999999999997e-01, 1.10030000000000006e-02, -3.72134000000000020e-01 ] DWMRI_gradient_29 [ -6.18109000000000020e-01, -6.56355000000000022e-01, -4.85057000000000016e-01 ] DWMRI_gradient_30 [ -1.00839999999999999e-01, 4.87715000000000010e-01, 1.10823000000000005e-01 ] useDiffusionProtocol: No bValueAcceptablePercentageTolerance: 5.00000000000000010e-03 gradientToleranceForSameness_degree: 1.00000000000000000e+00 diffusionReplacedDWIFileNameSuffix: _DiffusionReplaced.nrrd reportFileNameSuffix: _QCReport.txt reportFileMode: append bQuitOnCheckFailure: false ================================ Slice Check Protocol ================================ bCheck: Yes bSubregionalCheck: No subregionalCheckRelaxationFactor: 1.10000000000000009e+00 checkTimes: 0 headSkipSlicePercentage: 1.00000000000000006e-01 tailSkipSlicePercentage: 1.00000000000000006e-01 correlationDeviationThresholdbaseline: 3.00000000000000000e+00 correlationDeviationThresholdgradient: 3.50000000000000000e+00 outputDWIFileNameSuffix: reportFileNameSuffix: _QCReport.txt reportFileMode: append excludedDWINrrdFileNameSuffix: bQuitOnCheckFailure: false ================================ Interlace Check Protocol ================================ bCheck: Yes correlationThresholdBaseline: 9.49999999999999956e-01 correlationDeviationBaseline: 2.50000000000000000e+00 correlationThresholdGradient: 8.67800000000000016e-01 correlationDeviationGradient: 3.00000000000000000e+00 translationThreshold: 1.00000000000000000e+00 rotationThreshold: 5.00000000000000000e-01 outputDWIFileNameSuffix: reportFileNameSuffix: _QCReport.txt reportFileMode: append excludedDWINrrdFileNameSuffix: bQuitOnCheckFailure: false ================================ Baseline Average Protocol ================================ bAverage: Yes averageMethod: baselines optimized Average interpolation method: Linear stopThreshold: 2.00000000000000004e-02 outputDWIFileNameSuffix: reportFileNameSuffix: _QCReport.txt reportFileMode: append ================================ Eddy-Motion Correction Protocol ================================ bCorrect: Yes numberOfIterations: 1000 numberOfSamples: 100000 translationScale: 1.00000000000000000e+03 maxStepLength: 2.00000002980232239e-01 minStepLength: 9.99999974737875164e-05 relaxFactor: 5.00000000000000000e-01 outputDWIFileNameSuffix: reportFileNameSuffix: _QCReport.txt Eddy motion correction interpolation method: Linear reportFileMode: append ================================ Gradient Check Protocol ================================ bCheck: Yes gradientTranslationThreshold: 1.00000000000000000e+00 gradientRotationThreshold: 5.00000000000000000e-01 outputDWIFileNameSuffix: reportFileNameSuffix: _QCReport.txt reportFileMode: append excludedDWINrrdFileNameSuffix: bQuitOnCheckFailure: false ================================ DTI Computing Protocol ================================ bCompute: Yes dtiestimCommand: /home/jirka/grid/veda/nudz/dtiestim dtiprocessCommand: /home/jirka/grid/veda/nudz/dtiprocess method: wls tensorSuffix: _DTI.nrrd baselineThreshold: 50 bfa: Yes faSuffix: _FA.nrrd bmd: Yes mdSuffix: _MD.nrrd bcoloredfa: Yes coloredfaSuffix: _colorFA.nrrd bbaseline: Yes baselineSuffix: _Baseline.nrrd bfrobeniusnorm: Yes frobeniusnormSuffix: _frobeniusnorm.nrrd bidwi: Yes idwiSuffix: _IDWI.nrrd reportFileMode: append Loading in CIntensityMotionCheck: Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu.nrrd ... Done ===================== ImageCheck ... Image size Check: OK Image origin Check: FAILED Image Spacing Check: FAILED Image space Check: OK result of ImageCheck = 1ImageCheck DONE ===================== DiffusionCheck ... protocol->GetDiffusionProtocol().bValueAcceptablePercentageTolerance_5.00000000000000010e-03 DWMRI_bValue check: OK Image measurement frame (scanner space) 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 Protocol measurement frame (scanner space) 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 Protocol measurement frame (normalized) 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 Protocol measurement frame (normalized inverse) 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 Image measurement frame (normalized) 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 Image measurement frame (normalized inverse) 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 0.00000000000000000e+00 1.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 0 Gradient from protocol (scanner space) 0.00000000000000000e+00 -1.84067000000000008e-01 2.19216999999999995e-01 Gradient from image (scanner space) 0.00000000000000000e+00 -1.84067000000000008e-01 2.19216999999999995e-01 Gradient from image (anatomical space) 0.00000000000000000e+00 -1.84067000000000008e-01 2.19216999999999995e-01 Gradient from protocol (anatomical space) 0.00000000000000000e+00 -1.84067000000000008e-01 2.19216999999999995e-01 Dot product of 2 vectors: 8.19367535779999961e-02 Product of vector magnitudes: 8.19367535779999823e-02 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 1 Gradient from protocol (scanner space) 4.23597000000000001e-01 -3.90456000000000025e-01 -1.95107000000000003e-01 Gradient from image (scanner space) 4.23597000000000001e-01 -3.90456000000000025e-01 -1.95107000000000003e-01 Gradient from image (anatomical space) 4.23597000000000001e-01 -3.90456000000000025e-01 -1.95107000000000003e-01 Gradient from protocol (anatomical space) 4.23597000000000001e-01 -3.90456000000000025e-01 -1.95107000000000003e-01 Dot product of 2 vectors: 3.69957047794000071e-01 Product of vector magnitudes: 3.69957047794000071e-01 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 2 Gradient from protocol (scanner space) -8.52404000000000051e-01 -7.21265000000000045e-01 -3.86577999999999977e-01 Gradient from image (scanner space) -8.52404000000000051e-01 -7.21265000000000045e-01 -3.86577999999999977e-01 Gradient from image (anatomical space) -8.52404000000000051e-01 -7.21265000000000045e-01 -3.86577999999999977e-01 Gradient from protocol (anatomical space) -8.52404000000000051e-01 -7.21265000000000045e-01 -3.86577999999999977e-01 Dot product of 2 vectors: 1.39625832952499995e+00 Product of vector magnitudes: 1.39625832952500017e+00 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999999889e-01 Angle between vectors (raw): 8.53773646251593871e-07 Minimum angle between vectors: 8.53773646251593871e-07 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 3 Gradient from protocol (scanner space) -7.16921999999999948e-01 -6.55004000000000031e-01 -3.24166000000000010e-01 Gradient from image (scanner space) -7.16921999999999948e-01 -6.55004000000000031e-01 -3.24166000000000010e-01 Gradient from image (anatomical space) -7.16921999999999948e-01 -6.55004000000000031e-01 -3.24166000000000010e-01 Gradient from protocol (anatomical space) -7.16921999999999948e-01 -6.55004000000000031e-01 -3.24166000000000010e-01 Dot product of 2 vectors: 1.04809098965600000e+00 Product of vector magnitudes: 1.04809098965600000e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 4 Gradient from protocol (scanner space) -3.40532999999999975e-01 -6.61125999999999991e-01 -9.80709999999999971e-01 Gradient from image (scanner space) -3.40532999999999975e-01 -6.61125999999999991e-01 -9.80709999999999971e-01 Gradient from image (anatomical space) -3.40532999999999975e-01 -6.61125999999999991e-01 -9.80709999999999971e-01 Gradient from protocol (anatomical space) -3.40532999999999975e-01 -6.61125999999999991e-01 -9.80709999999999971e-01 Dot product of 2 vectors: 1.51484241606500003e+00 Product of vector magnitudes: 1.51484241606500003e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 5 Gradient from protocol (scanner space) -8.63373999999999975e-01 -1.30969999999999993e-02 -2.96100000000000009e-03 Gradient from image (scanner space) -8.63373999999999975e-01 -1.30974999999999998e-02 -2.96106000000000017e-03 Gradient from image (anatomical space) -8.63373999999999975e-01 -1.30974999999999998e-02 -2.96106000000000017e-03 Gradient from protocol (anatomical space) -8.63373999999999975e-01 -1.30969999999999993e-02 -2.96100000000000009e-03 Dot product of 2 vectors: 7.45594969532159979e-01 Product of vector magnitudes: 7.45594969532286767e-01 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999829914e-01 Angle between vectors (raw): 3.34173592702913076e-05 Minimum angle between vectors: 3.34173592702913076e-05 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 6 Gradient from protocol (scanner space) 6.41083999999999987e-01 3.11724999999999974e-01 2.21010000000000012e-01 Gradient from image (scanner space) 6.41083999999999987e-01 3.11724999999999974e-01 2.21010000000000012e-01 Gradient from image (anatomical space) 6.41083999999999987e-01 3.11724999999999974e-01 2.21010000000000012e-01 Gradient from protocol (anatomical space) 6.41083999999999987e-01 3.11724999999999974e-01 2.21010000000000012e-01 Dot product of 2 vectors: 5.57006590781000011e-01 Product of vector magnitudes: 5.57006590781000011e-01 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 7 Gradient from protocol (scanner space) 1.81147000000000002e-01 6.48745000000000016e-01 3.24938999999999978e-01 Gradient from image (scanner space) 1.81147000000000002e-01 6.48745000000000016e-01 3.24938999999999978e-01 Gradient from image (anatomical space) 1.81147000000000002e-01 6.48745000000000016e-01 3.24938999999999978e-01 Gradient from protocol (anatomical space) 1.81147000000000002e-01 6.48745000000000016e-01 3.24938999999999978e-01 Dot product of 2 vectors: 5.59269664355000029e-01 Product of vector magnitudes: 5.59269664355000029e-01 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 8 Gradient from protocol (scanner space) 2.09740000000000010e-01 4.19049000000000005e-01 7.47805999999999971e-01 Gradient from image (scanner space) 2.09740000000000010e-01 4.19049000000000005e-01 7.47805999999999971e-01 Gradient from image (anatomical space) 2.09740000000000010e-01 4.19049000000000005e-01 7.47805999999999971e-01 Gradient from protocol (anatomical space) 2.09740000000000010e-01 4.19049000000000005e-01 7.47805999999999971e-01 Dot product of 2 vectors: 7.78806745637000031e-01 Product of vector magnitudes: 7.78806745637000142e-01 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999999889e-01 Angle between vectors (raw): 8.53773646251593871e-07 Minimum angle between vectors: 8.53773646251593871e-07 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 9 Gradient from protocol (scanner space) 7.41017000000000037e-01 8.57037000000000049e-01 8.44011999999999984e-01 Gradient from image (scanner space) 7.41017000000000037e-01 8.57037000000000049e-01 8.44011999999999984e-01 Gradient from image (anatomical space) 7.41017000000000037e-01 8.57037000000000049e-01 8.44011999999999984e-01 Gradient from protocol (anatomical space) 7.41017000000000037e-01 8.57037000000000049e-01 8.44011999999999984e-01 Dot product of 2 vectors: 1.99597486980200012e+00 Product of vector magnitudes: 1.99597486980200012e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 10 Gradient from protocol (scanner space) 9.77009000000000016e-01 0.00000000000000000e+00 4.23570999999999975e-01 Gradient from image (scanner space) 9.77009000000000016e-01 0.00000000000000000e+00 4.23570999999999975e-01 Gradient from image (anatomical space) 9.77009000000000016e-01 0.00000000000000000e+00 4.23570999999999975e-01 Gradient from protocol (anatomical space) 9.77009000000000016e-01 0.00000000000000000e+00 4.23570999999999975e-01 Dot product of 2 vectors: 1.13395897812200008e+00 Product of vector magnitudes: 1.13395897812200008e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 11 Gradient from protocol (scanner space) 4.23719999999999986e-01 7.44370000000000032e-02 6.64768000000000026e-01 Gradient from image (scanner space) 4.23719999999999986e-01 7.44374000000000008e-02 6.64768000000000026e-01 Gradient from image (anatomical space) 4.23719999999999986e-01 7.44374000000000008e-02 6.64768000000000026e-01 Gradient from protocol (anatomical space) 4.23719999999999986e-01 7.44370000000000032e-02 6.64768000000000026e-01 Dot product of 2 vectors: 6.26996028967800045e-01 Product of vector magnitudes: 6.26996028967879315e-01 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999873546e-01 Angle between vectors (raw): 2.88140700189933150e-05 Minimum angle between vectors: 2.88140700189933150e-05 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 12 Gradient from protocol (scanner space) 9.05823000000000045e-01 5.04981000000000013e-01 4.64926000000000006e-01 Gradient from image (scanner space) 9.05823000000000045e-01 5.04981000000000013e-01 4.64926000000000006e-01 Gradient from image (anatomical space) 9.05823000000000045e-01 5.04981000000000013e-01 4.64926000000000006e-01 Gradient from protocol (anatomical space) 9.05823000000000045e-01 5.04981000000000013e-01 4.64926000000000006e-01 Dot product of 2 vectors: 1.29167730316600027e+00 Product of vector magnitudes: 1.29167730316600049e+00 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999999778e-01 Angle between vectors (raw): 1.20741826972573334e-06 Minimum angle between vectors: 1.20741826972573334e-06 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 13 Gradient from protocol (scanner space) 7.39210000000000006e-02 9.95999999999999941e-02 7.45828000000000046e-01 Gradient from image (scanner space) 7.39212999999999953e-02 9.96000999999999970e-02 7.45828000000000046e-01 Gradient from image (anatomical space) 7.39212999999999953e-02 9.96000999999999970e-02 7.45828000000000046e-01 Gradient from protocol (anatomical space) 7.39210000000000006e-02 9.95999999999999941e-02 7.45828000000000046e-01 Dot product of 2 vectors: 5.71643911961300155e-01 Product of vector magnitudes: 5.71643911961349227e-01 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999914180e-01 Angle between vectors (raw): 2.37373641412244944e-05 Minimum angle between vectors: 2.37373641412244944e-05 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 14 Gradient from protocol (scanner space) 9.45998000000000006e-01 5.96015999999999990e-01 5.71234000000000020e-01 Gradient from image (scanner space) 9.45998000000000006e-01 5.96015999999999990e-01 5.71234000000000020e-01 Gradient from image (anatomical space) 9.45998000000000006e-01 5.96015999999999990e-01 5.71234000000000020e-01 Gradient from protocol (anatomical space) 9.45998000000000006e-01 5.96015999999999990e-01 5.71234000000000020e-01 Dot product of 2 vectors: 1.57645557101599998e+00 Product of vector magnitudes: 1.57645557101600020e+00 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999999889e-01 Angle between vectors (raw): 8.53773646251593871e-07 Minimum angle between vectors: 8.53773646251593871e-07 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 15 Gradient from protocol (scanner space) 1.82339000000000001e-01 2.22882999999999998e-01 7.95745000000000036e-01 Gradient from image (scanner space) 1.82339000000000001e-01 2.22882999999999998e-01 7.95745000000000036e-01 Gradient from image (anatomical space) 1.82339000000000001e-01 2.22882999999999998e-01 7.95745000000000036e-01 Gradient from protocol (anatomical space) 1.82339000000000001e-01 2.22882999999999998e-01 7.95745000000000036e-01 Dot product of 2 vectors: 7.16134447635000115e-01 Product of vector magnitudes: 7.16134447635000115e-01 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 16 Gradient from protocol (scanner space) 9.70423000000000036e-01 5.71402999999999994e-01 5.96088999999999980e-01 Gradient from image (scanner space) 9.70423000000000036e-01 5.71402999999999994e-01 5.96088999999999980e-01 Gradient from image (anatomical space) 9.70423000000000036e-01 5.71402999999999994e-01 5.96088999999999980e-01 Gradient from protocol (anatomical space) 9.70423000000000036e-01 5.71402999999999994e-01 5.96088999999999980e-01 Dot product of 2 vectors: 1.62354428325899991e+00 Product of vector magnitudes: 1.62354428325899991e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 17 Gradient from protocol (scanner space) -2.75907000000000013e-01 2.73990000000000011e-01 7.46086999999999945e-01 Gradient from image (scanner space) -2.75907000000000013e-01 2.73990000000000011e-01 7.46086999999999945e-01 Gradient from image (anatomical space) -2.75907000000000013e-01 2.73990000000000011e-01 7.46086999999999945e-01 Gradient from protocol (anatomical space) -2.75907000000000013e-01 2.73990000000000011e-01 7.46086999999999945e-01 Dot product of 2 vectors: 7.07841004317999900e-01 Product of vector magnitudes: 7.07841004317999900e-01 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 18 Gradient from protocol (scanner space) 9.45671000000000039e-01 9.04170999999999947e-01 6.65085999999999955e-01 Gradient from image (scanner space) 9.45671000000000039e-01 9.04170999999999947e-01 6.65085999999999955e-01 Gradient from image (anatomical space) 9.45671000000000039e-01 9.04170999999999947e-01 6.65085999999999955e-01 Gradient from protocol (anatomical space) 9.45671000000000039e-01 9.04170999999999947e-01 6.65085999999999955e-01 Dot product of 2 vectors: 2.15415822487800002e+00 Product of vector magnitudes: 2.15415822487800002e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 19 Gradient from protocol (scanner space) 9.99209999999999959e-02 4.64341999999999977e-01 5.05291000000000046e-01 Gradient from image (scanner space) 9.99209999999999959e-02 4.64341999999999977e-01 5.05291000000000046e-01 Gradient from image (anatomical space) 9.99209999999999959e-02 4.64341999999999977e-01 5.05291000000000046e-01 Gradient from protocol (anatomical space) 9.99209999999999959e-02 4.64341999999999977e-01 5.05291000000000046e-01 Dot product of 2 vectors: 4.80916693886000002e-01 Product of vector magnitudes: 4.80916693885999946e-01 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 20 Gradient from protocol (scanner space) 2.23110000000000003e-01 1.82130999999999987e-01 0.00000000000000000e+00 Gradient from image (scanner space) 2.23110000000000003e-01 1.82130999999999987e-01 0.00000000000000000e+00 Gradient from image (anatomical space) 2.23110000000000003e-01 1.82130999999999987e-01 0.00000000000000000e+00 Gradient from protocol (anatomical space) 2.23110000000000003e-01 1.82130999999999987e-01 0.00000000000000000e+00 Dot product of 2 vectors: 8.29497732610000038e-02 Product of vector magnitudes: 8.29497732609999899e-02 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 21 Gradient from protocol (scanner space) -8.86965000000000003e-01 -8.78866000000000036e-01 -9.02787000000000006e-01 Gradient from image (scanner space) -8.86965000000000003e-01 -8.78866000000000036e-01 -9.02787000000000006e-01 Gradient from image (anatomical space) -8.86965000000000003e-01 -8.78866000000000036e-01 -9.02787000000000006e-01 Gradient from protocol (anatomical space) -8.86965000000000003e-01 -8.78866000000000036e-01 -9.02787000000000006e-01 Dot product of 2 vectors: 2.37413672455000002e+00 Product of vector magnitudes: 2.37413672455000002e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 22 Gradient from protocol (scanner space) -6.36889000000000038e-01 -3.76056000000000001e-01 -1.35655999999999999e-01 Gradient from image (scanner space) -6.36889000000000038e-01 -3.76056000000000001e-01 -1.35655999999999999e-01 Gradient from image (anatomical space) -6.36889000000000038e-01 -3.76056000000000001e-01 -1.35655999999999999e-01 Gradient from protocol (anatomical space) -6.36889000000000038e-01 -3.76056000000000001e-01 -1.35655999999999999e-01 Dot product of 2 vectors: 5.65448263793000061e-01 Product of vector magnitudes: 5.65448263793000061e-01 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 23 Gradient from protocol (scanner space) -5.13440000000000007e-01 -9.19289000000000023e-01 -6.90002000000000004e-01 Gradient from image (scanner space) -5.13440000000000007e-01 -9.19289000000000023e-01 -6.90002000000000004e-01 Gradient from image (anatomical space) -5.13440000000000007e-01 -9.19289000000000023e-01 -6.90002000000000004e-01 Gradient from protocol (anatomical space) -5.13440000000000007e-01 -9.19289000000000023e-01 -6.90002000000000004e-01 Dot product of 2 vectors: 1.58481565912500000e+00 Product of vector magnitudes: 1.58481565912499978e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 24 Gradient from protocol (scanner space) 1.21288000000000007e-01 2.24300000000000020e-03 7.27187999999999946e-01 Gradient from image (scanner space) 1.21288000000000007e-01 2.24258000000000009e-03 7.27187999999999946e-01 Gradient from image (anatomical space) 1.21288000000000007e-01 2.24258000000000009e-03 7.27187999999999946e-01 Gradient from protocol (anatomical space) 1.21288000000000007e-01 2.24300000000000020e-03 7.27187999999999946e-01 Dot product of 2 vectors: 5.43518196394939923e-01 Product of vector magnitudes: 5.43518196395028075e-01 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999837796e-01 Angle between vectors (raw): 3.26338154439275691e-05 Minimum angle between vectors: 3.26338154439275691e-05 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 25 Gradient from protocol (scanner space) 4.86420000000000019e-01 7.04329999999999956e-02 4.52668000000000015e-01 Gradient from image (scanner space) 4.86420000000000019e-01 7.04329999999999956e-02 4.52668000000000015e-01 Gradient from image (anatomical space) 4.86420000000000019e-01 7.04329999999999956e-02 4.52668000000000015e-01 Gradient from protocol (anatomical space) 4.86420000000000019e-01 7.04329999999999956e-02 4.52668000000000015e-01 Dot product of 2 vectors: 4.46473542113000055e-01 Product of vector magnitudes: 4.46473542113000110e-01 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999999889e-01 Angle between vectors (raw): 8.53773646251593871e-07 Minimum angle between vectors: 8.53773646251593871e-07 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 26 Gradient from protocol (scanner space) 6.05491000000000001e-01 2.41391999999999995e-01 5.12356999999999951e-01 Gradient from image (scanner space) 6.05491000000000001e-01 2.41391999999999995e-01 5.12356999999999951e-01 Gradient from image (anatomical space) 6.05491000000000001e-01 2.41391999999999995e-01 5.12356999999999951e-01 Gradient from protocol (anatomical space) 6.05491000000000001e-01 2.41391999999999995e-01 5.12356999999999951e-01 Dot product of 2 vectors: 6.87399144193999945e-01 Product of vector magnitudes: 6.87399144194000056e-01 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999999889e-01 Angle between vectors (raw): 8.53773646251593871e-07 Minimum angle between vectors: 8.53773646251593871e-07 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 27 Gradient from protocol (scanner space) 7.39936000000000038e-01 -9.35429999999999984e-01 9.44517999999999969e-01 Gradient from image (scanner space) 7.39936000000000038e-01 -9.35429999999999984e-01 9.44517999999999969e-01 Gradient from image (anatomical space) 7.39936000000000038e-01 -9.35429999999999984e-01 9.44517999999999969e-01 Gradient from protocol (anatomical space) 7.39936000000000038e-01 -9.35429999999999984e-01 9.44517999999999969e-01 Dot product of 2 vectors: 2.31464882132000005e+00 Product of vector magnitudes: 2.31464882132000005e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 28 Gradient from protocol (scanner space) 1.49946999999999997e-01 1.10030000000000006e-02 -3.72134000000000020e-01 Gradient from image (scanner space) 1.49946999999999997e-01 1.10028000000000001e-02 -3.72134000000000020e-01 Gradient from image (anatomical space) 1.49946999999999997e-01 1.10028000000000001e-02 -3.72134000000000020e-01 Gradient from protocol (anatomical space) 1.49946999999999997e-01 1.10030000000000006e-02 -3.72134000000000020e-01 Dot product of 2 vectors: 1.61088880573400012e-01 Product of vector magnitudes: 1.61088880573419996e-01 Dot product of 2 vectors / Product of vector magnitudes: 9.99999999999875988e-01 Angle between vectors (raw): 2.85344385506479940e-05 Minimum angle between vectors: 2.85344385506479940e-05 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 29 Gradient from protocol (scanner space) -6.18109000000000020e-01 -6.56355000000000022e-01 -4.85057000000000016e-01 Gradient from image (scanner space) -6.18109000000000020e-01 -6.56355000000000022e-01 -4.85057000000000016e-01 Gradient from image (anatomical space) -6.18109000000000020e-01 -6.56355000000000022e-01 -4.85057000000000016e-01 Gradient from protocol (anatomical space) -6.18109000000000020e-01 -6.56355000000000022e-01 -4.85057000000000016e-01 Dot product of 2 vectors: 1.04814091515500007e+00 Product of vector magnitudes: 1.04814091515499985e+00 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 protocol->GetDiffusionProtocol().gradientToleranceForSameness_degree1.00000000000000000e+00 =================================================== Gradient 30 Gradient from protocol (scanner space) -1.00839999999999999e-01 4.87715000000000010e-01 1.10823000000000005e-01 Gradient from image (scanner space) -1.00839999999999999e-01 4.87715000000000010e-01 1.10823000000000005e-01 Gradient from image (anatomical space) -1.00839999999999999e-01 4.87715000000000010e-01 1.10823000000000005e-01 Gradient from protocol (anatomical space) -1.00839999999999999e-01 4.87715000000000010e-01 1.10823000000000005e-01 Dot product of 2 vectors: 2.60316364154000002e-01 Product of vector magnitudes: 2.60316364153999946e-01 Dot product of 2 vectors / Product of vector magnitudes: 1.00000000000000000e+00 Angle between vectors (raw): 0.00000000000000000e+00 Minimum angle between vectors: 0.00000000000000000e+00 Diffusion gradient Check: OK DiffusionCheck DONE ===================== Denoising LMMSE... Denoising LMMSE check NOT set. Denoising LMMSE DONE ===================== SliceWiseCheck ... Slice correlation calculating ................................ DONE slice checking ... count: 1... count: 2... count: 3... count: 4... DONE SliceWiseCheck DONE ===================== InterlaceWiseCheck ... Interlace calculating Interlace correlation for gradient 0: 9.64191815421690079e-01 Interlace correlation for gradient 1: 9.64550653907242705e-01 Interlace correlation for gradient 2: 9.62952777496318690e-01 Interlace correlation for gradient 3: 9.61411891307468180e-01 Interlace correlation for gradient 4: 9.62594302784613332e-01 Interlace correlation for gradient 5: 9.62686302687720596e-01 Interlace correlation for gradient 6: 9.62820281283670143e-01 Interlace correlation for gradient 7: 9.62248059338842943e-01 Interlace correlation for gradient 8: 9.61543252325948616e-01 Interlace correlation for gradient 9: 9.64996237006657154e-01 Interlace correlation for gradient 10: 9.63734303171186579e-01 Interlace correlation for gradient 11: 9.62892728188347813e-01 Interlace correlation for gradient 12: 9.63224249724663695e-01 Interlace correlation for gradient 13: 9.63841763303101784e-01 Interlace correlation for gradient 14: 9.62310717119924330e-01 Interlace correlation for gradient 15: 9.64041643179990526e-01 Interlace correlation for gradient 16: 9.65098292699180127e-01 Interlace correlation for gradient 17: 9.62260074293815326e-01 Interlace correlation for gradient 18: 9.62259242475783205e-01 Interlace correlation for gradient 19: 9.61887817939869882e-01 Interlace correlation for gradient 20: 9.62949673791255178e-01 Interlace correlation for gradient 21: 9.63907104030949258e-01 Interlace correlation for gradient 22: 9.63132545601239509e-01 Interlace correlation for gradient 23: 9.63190231469157498e-01 Interlace correlation for gradient 24: 9.64307852513403452e-01 Interlace correlation for gradient 25: 9.63512935521793179e-01 DONE Interlace checking ... DONE MEHDI INTELACE INSIDE 26 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 2 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 3 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 4 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 5 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 6 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 7 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 9 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 10 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 11 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 12 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 13 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 14 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 15 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 16 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 17 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 18 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 19 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 22 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 23 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 24 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 25 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 26 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 27 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 28 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 29 -1.00000000000000000e+00 (m_Original_ForcedConformance_Mapping[i].index_original)[0] 30 -1.00000000000000000e+00 qcResult->GetInterlaceWiseCheckResult() After31 InterlaceWise_qcResult Size:26 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 InterlaceWise_qcResult:1 No gradient excluded No excluded gradient file created. InterlaceWiseCheck DONE Mapping Original and Comforce image: length of Map: 26 ===================== BaselineAverage ... No baseline included. BaselineAverage DONE ===================== EddyCurrentHeadMotionCorrect ... EddyCurrentHeadMotionCorrectIowa ... Eddy-current and head motion correction using IOWA tool. protocol->GetEddyMotionCorrectionProtocol().numberOfIterations 1000 Register Volume .......................... EddyCurrentHeadMotionCorrect DONE ===================== GradientCheck ... Gradient calculating Register Gradient 1 to Baseline or first Image ... Register Gradient 2 to Baseline or first Image ... Register Gradient 3 to Baseline or first Image ... Register Gradient 4 to Baseline or first Image ... Register Gradient 5 to Baseline or first Image ... Register Gradient 6 to Baseline or first Image ... Register Gradient 7 to Baseline or first Image ... Register Gradient 8 to Baseline or first Image ... Register Gradient 9 to Baseline or first Image ... Register Gradient 10 to Baseline or first Image ... Register Gradient 11 to Baseline or first Image ... Register Gradient 12 to Baseline or first Image ... Register Gradient 13 to Baseline or first Image ... Register Gradient 14 to Baseline or first Image ... Register Gradient 15 to Baseline or first Image ... Register Gradient 16 to Baseline or first Image ... Register Gradient 17 to Baseline or first Image ... Register Gradient 18 to Baseline or first Image ... Register Gradient 19 to Baseline or first Image ... Register Gradient 20 to Baseline or first Image ... Register Gradient 21 to Baseline or first Image ... Register Gradient 22 to Baseline or first Image ... Register Gradient 23 to Baseline or first Image ... Register Gradient 24 to Baseline or first Image ... Register Gradient 25 to Baseline or first Image ... DONE Gradient checking ... DONE DWIGradientResultsContainer26 No gradient excluded No excluded gradient file created. GradientCheck DONE Baselines_indices: 2 QCIndex: 0 Included Gradients: 3 QCIndex: 1 Included Gradients: 4 QCIndex: 2 Included Gradients: 5 QCIndex: 3 Included Gradients: 6 QCIndex: 4 Included Gradients: 7 QCIndex: 5 Included Gradients: 9 QCIndex: 6 Included Gradients: 10 QCIndex: 7 Included Gradients: 11 QCIndex: 8 Included Gradients: 12 QCIndex: 9 Included Gradients: 13 QCIndex: 10 Included Gradients: 14 QCIndex: 11 Included Gradients: 15 QCIndex: 12 Included Gradients: 16 QCIndex: 13 Included Gradients: 17 QCIndex: 14 Included Gradients: 18 QCIndex: 15 Included Gradients: 19 QCIndex: 16 Included Gradients: 22 QCIndex: 17 Included Gradients: 23 QCIndex: 18 Included Gradients: 24 QCIndex: 19 Included Gradients: 25 QCIndex: 20 Included Gradients: 26 QCIndex: 21 Included Gradients: 27 QCIndex: 22 Included Gradients: 28 QCIndex: 23 Included Gradients: 29 QCIndex: 24 Included Gradients: 30 QCIndex: 25 ===================== Denoising Joint LMMSE... HACK: SKIPPING JointDenoising( m_DwiForcedConformanceImage );/opt/Francois/temp/LabTools/NAMICExternalProjects-build/DTIPrep/src/IntensityMotionCheck.cxx 4585 Denoising Joint LMMSE DONE ===================== Save QC'ed DWI ... DONE ===================== Brain Mask protocol->GetBrainMaskProtocol().reportFileMode 1 Brain mask check NOT set. Brain Mask DONE ===================== Dominant directional artifact detector... Brain mask process has been failed or is not set in protocol. Dominant directional artifact detector DONE ===================== DTIComputing ... =============== Starting dtiestim command =============== dtiestim command: --dwi_image Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed.nrrd --tensor_output Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed_DTI.nrrd -m wls -t 50 --idwi Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed_IDWI.nrrd --B0 Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed_Baseline.nrrd ret 1 =============== Starting dtiprocess command =============== dtiprocess command: /home/jirka/grid/veda/nudz/dtiprocess --dti_image Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed_DTI.nrrd --scalar_float -f Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed_DTI_FA.nrrd -m Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed_DTI_MD.nrrd --color_fa_output Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed_DTI_colorFA.nrrd --frobenius-norm-output Fantom_20160219_001_051_cmrr_mbep2d_diff_bezpatu_QCed_DTI_frobeniusnorm.nrrd DTIComputing DONE Left DWI Diffusion: baselineLeftNumber: 0 bValueLeftNumber: 25 gradientDirLeftNumber: 26 0 [ -0.85075226000391002, -0.72205817904209302, -0.38872986468967702 ] 1 1 [ -0.71645188899814805, -0.65621624050897098, -0.32275056328263502 ] 1 2 [ -0.33614872232893001, -0.66256817021929704, -0.98124913877867004 ] 1 3 [ -0.86336240622336402, -0.013622938863508001, -0.0038402233656909999 ] 1 4 [ 0.63861240940532504, 0.31676904095834701, 0.22099356557342101 ] 1 5 [ 0.17598000012246601, 0.651878517430862, 0.321488883826545 ] 1 6 [ 0.72908193665572896, 0.87124583106255005, 0.83984826087839204 ] 1 7 [ 0.97712331502810701, 0.0050767518662119997, 0.42327677935480001 ] 1 8 [ 0.42291072611970398, 0.076615960729237001, 0.66503576673529397 ] 1 9 [ 0.90422759981395495, 0.50779552389812499, 0.46496608135403 ] 1 10 [ 0.073241303518383996, 0.10203357421454801, 0.74556609719704403 ] 1 11 [ 0.93687939996208103, 0.61300503277633001, 0.56827580516293397 ] 1 12 [ 0.178966131385388, 0.2283698964109, 0.79495456591255798 ] 1 13 [ 0.969037925111771, 0.57377461738896995, 0.59606415040179495 ] 1 14 [ -0.27781537042839699, 0.27201466216624398, 0.74610163371910398 ] 1 15 [ 0.94475374405163304, 0.905794161826044, 0.66418034025426897 ] 1 16 [ 0.098936171779720999, 0.46524078348166598, 0.50465764750466202 ] 1 17 [ -0.63617030566055799, -0.37704650116166999, -0.136277444760661 ] 1 18 [ -0.51242327024288403, -0.91854463781095197, -0.69174691874076 ] 1 19 [ 0.121172878235783, -0.00097700401255200009, 0.72720999339686798 ] 1 20 [ 0.48611018485070001, 0.069908952952212, 0.45308185639522602 ] 1 21 [ 0.60563142364498701, 0.24264286921834899, 0.511599609954145 ] 1 22 [ 0.741260480459496, -0.93581707280915805, 0.94309497277202003 ] 1 23 [ 0.14993160950298601, 0.01100885120796, -0.37214002208814501 ] 1 24 [ -0.61740422941870399, -0.65766814557844799, -0.48417511598831797 ] 1 25 [ -0.100145979881351, 0.48832244887267001, 0.10875813898096499 ] 1 ================================ QC result summary: ================================ PASS: Gradient direction #is not less than 6! PASS: Left Baseline images and the left b-value are ok! PASS: Bad gradient directions #passed in the tolerance! qcResult->GetSliceWiseCheckResult().size() 84 qcResult->GetInterlaceWiseCheckResult().size() 31 qcResult->GetGradientWiseCheckResult().size() 31 FAILED execution of ./DTIPrep in /opt/Francois/temp/LabTools/NAMICExternalProjects-build/DTIPrep/src/main.cxx at 1473 with code 1