Basic echo-wise reports

Reports for: echo 1.

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-1_desc-stdev_bold.svg

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-1_desc-background_bold.svg

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-1_desc-zoomed_bold.svg

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-1_desc-carpet_bold.svg

Reports for: echo 2.

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-2_desc-stdev_bold.svg

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-2_desc-background_bold.svg

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-2_desc-zoomed_bold.svg

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-2_desc-carpet_bold.svg

Reports for: echo 3.

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-3_desc-stdev_bold.svg

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-3_desc-background_bold.svg

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-3_desc-zoomed_bold.svg

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-3_desc-carpet_bold.svg

Reports for: echo 4.

Standard deviation of signal through time

The voxel-wise standard deviation of the signal (variability along time).

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-4_desc-stdev_bold.svg

View of the background of the voxel-wise average of the BOLD timeseries

This panel shows a mosaic enhancing the background around the head. Artifacts usually unveil themselves in the air surrounding the head, where no signal sources are present.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-4_desc-background_bold.svg

Voxel-wise average of BOLD time-series, zoomed-in covering just the brain

This panel shows a mosaic of the brain. This mosaic is the most suitable to screen head-motion intensity inhomogeneities, global/local noise, signal leakage (for example, from the eyeballs and across the phase-encoding axis), etc.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-4_desc-zoomed_bold.svg

Carpetplot and nuisance signals

The so-called «carpetplot» may assist in assessing head-motion derived artifacts and respiation effects.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-4_desc-carpet_bold.svg

Extended echo-wise reports

Reports for: echo 1.

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-1_desc-mean_bold.svg

Reports for: echo 2.

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-2_desc-mean_bold.svg

Reports for: echo 3.

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-3_desc-mean_bold.svg

Reports for: echo 4.

Voxel-wise average of BOLD time-series

The average signal calculated across the last axis (time).

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_echo-4_desc-mean_bold.svg

Extended reports shared across echos

Brain extraction performance

Brain mask as internally extracted by MRIQC. Defects on the brainmask could indicate problematic aspects of the image quality-wise.

Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_desc-brainmask_bold.svg

Spatial normalization of the anatomical image

This panel shows a quick-and-dirty nonlinear registration into the MNI152NLin2009cAsym template accessed with TemplateFlow.

Problem loading figure sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_desc-norm_bold.svg. If the link below works, please try reloading the report in your browser.
Get figure file: sub-001/figures/sub-001_ses-pilot014_task-rest_dir-AP_desc-norm_bold.svg

About

Errors

Reproducibility and provenance information

Thanks for using MRIQC. The following information may assist in reconstructing the provenance of the corresponding derivatives.

Execution environmentsingularity
Input filename/sub-001/ses-pilot014/func/sub-001_ses-pilot014_task-rest_dir-AP_echo-1_bold.nii.gz
VersionsMRIQC24.1.0.dev0+g3fe90466.d20240417
VersionsNiPype1.8.6
VersionsTemplateFlow24.2.0
md5sum71369ea2c14aafca20d86560bbd6f293

AcquisitionDuration1230.47
AcquisitionMatrixPE96
AcquisitionTime19:18:59.380000
B0FieldSource['pilot014pepolarbold', 'pilot014phasediff']
BandwidthPerPixelPhaseEncode37.879
BaseResolution96
DerivedVendorReportedEchoSpacing0.000549997
DwellTime2.3e-06
EchoNumber1
EchoTime0.0126
EchoTrainLength35
EffectiveEchoSpacing0.000274998
FlipAngle64
ImageOrientationPatientDICOM[0.99738, 0.0550401, 0.0469541, -0.0395358, 0.958188, -0.283395]
ImageType['ORIGINAL', 'PRIMARY', 'FMRI', 'NONE']
ImageTypeText['ORIGINAL', 'PRIMARY', 'M', 'MB', 'TE1', 'DIS2D']
InPlanePhaseEncodingDirectionDICOMCOL
PercentPhaseFOV100
PhaseEncodingDirectionj-
PixelBandwidth2264
ProtocolNamefunc-bold_task-rest_dir-AP__cmrr_me4_sms4
PulseSequenceNameepfid2d4_96
ReceiveCoilActiveElementsHC1-7
ReconMatrixPE96
RefLinesPE22
RepetitionTime1.6
SequenceVariantSK\SS
SeriesDescriptionfunc-bold_task-rest_dir-AP__cmrr_me4_sms4
SeriesNumber14
ShimSetting[8248, -5629, -6031, 686, 7, -236, 46, 5]
SliceThickness2.2
SliceTiming[0, 0.845, 0.1075, 0.95, 0.2125, 1.055, 0.3175, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.845, 0.1075, 0.95, 0.2125, 1.055, 0.3175, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.845, 0.1075, 0.95, 0.2125, 1.055, 0.3175, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.845, 0.1075, 0.95, 0.2125, 1.055, 0.3175, 1.16, 0.4225, 1.265, 0.5275, 1.37, 0.6325, 1.475, 0.7375]
SpacingBetweenSlices2.2
TotalReadoutTime0.0261249
TxRefAmp246.09
WipMemBlocke3b2d54d-1e87-435b-85be-38accfa2de26||Sequence: R017 nxva30a/master r/d34922cc2+; Jun 27 2022 13:20:16 by eja

aor[0.0006083689839572192, 0.0007590641711229947, 0.0008212700534759358, 0.0009070721925133689]
aqi[0.005920030561497326, 0.006579752259358288, 0.010506597794117648, 0.018440951604278073]
dummy_trs2
dvarsnstd28.78446274634537
dvarsstd1.0715898980321292
dvarsvstd1.0021669944042837
efc0.5791
fber659.1575
fdmean0.14530317058975342
fdnum87
fdperc11.631016042780749
fwhmavg3.6853105280247616
fwhmx3.3358082731825207
fwhmy3.750859760559599
fwhmz3.9692635503321654
gcor[0.0119513, 0.0112516, 0.00902737, 0.00675135]
gsrx0.012939290143549442
gsry0.11499381810426712
sizet748
sizex96
sizey96
sizez60
snr2.515955250941988
spacingtr1.600000023841858
spacingx2.197920083999634
spacingy2.197920083999634
spacingz2.200000047683716
summarybgk10.3615
summarybgmad19.8863
summarybgmean122.418
summarybgmedian30.0
summarybgn418800.0
summarybgp051.0
summarybgp95611.0
summarybgstdv217.9414
summaryfgk1.4578
summaryfgmad274.112
summaryfgmean807.876
summaryfgmedian771.0
summaryfgn134160.0
summaryfgp05378.0
summaryfgp951349.0
summaryfgstdv306.4431
tsnr48.62636221945286
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