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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016_task-qct_dir-RL_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-016/func/sub-001_ses-016_task-qct_dir-RL_echo-1_part-mag_bold.nii.gz
VersionsMRIQC24.1.0.dev0+g3fe90466.d20240417
VersionsNiPype1.8.6
VersionsTemplateFlow24.2.0
md5sum5771b9a23e42c52d992ae7ab91e037d0

AcquisitionDuration188.475
AcquisitionMatrixPE76
AcquisitionTime21:54:32.217500
B0FieldSource['016pepolarbold', '016phasediff']
BandwidthPerPixelPhaseEncode40.486
BaseResolution102
DerivedVendorReportedEchoSpacing0.000649997
DwellTime2.5e-06
EchoNumber1
EchoTime0.0126
EchoTrainLength28
EffectiveEchoSpacing0.000324999
FlipAngle64
ImageOrientationPatientDICOM[0.997393, -0.04706, 0.0546977, 0.0634498, 0.932986, -0.354276]
ImageType['ORIGINAL', 'PRIMARY', 'FMRI', 'NONE']
ImageTypeText['ORIGINAL', 'PRIMARY', 'M', 'MB', 'TE1', 'DIS2D']
InPlanePhaseEncodingDirectionDICOMROW
PercentPhaseFOV74.5098
PhaseEchoTime10.0126
PhaseEchoTime20.0322
PhaseEncodingDirectioni
PixelBandwidth1961
ProtocolNamefunc-bold_task-qct_dir-RL__cmrr_me4_sms4
PulseSequenceNameepfid2d4_76
ReceiveCoilActiveElementsHC1-7
ReconMatrixPE76
RefLinesPE18
RepetitionTime1.6
SequenceVariantSK\SS\OSP
SeriesDescriptionfunc-bold_task-qct_dir-RL__cmrr_me4_sms4
SeriesNumber13
ShimSetting[8234, -5624, -6010, 648, -19, -233, 38, 26]
SliceThickness2.2
SliceTiming[0, 0.8425, 0.105, 0.9475, 0.21, 1.0525, 0.315, 1.1575, 0.42, 1.2625, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.21, 1.0525, 0.315, 1.1575, 0.42, 1.2625, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.21, 1.0525, 0.315, 1.1575, 0.42, 1.2625, 0.5275, 1.37, 0.6325, 1.475, 0.7375, 0, 0.8425, 0.105, 0.9475, 0.21, 1.0525, 0.315, 1.1575, 0.42, 1.2625, 0.5275, 1.37, 0.6325, 1.475, 0.7375]
SpacingBetweenSlices2.2
TotalReadoutTime0.0243749
TxRefAmp244.928
WipMemBlock3d1a2417-7b8c-43c1-ad21-3a046cf01307||Sequence: R017 nxva30a/master r/d34922cc2+; Jun 27 2022 13:20:16 by eja

aor[0.0009195918367346938, 0.0009496938775510205, 0.0010409183673469387, 0.0012276530612244899]
aqi[0.0056104493877551015, 0.006916353163265306, 0.011212951326530616, 0.01850505306122449]
dummy_trs1
dvarsnstd41.3391997741237
dvarsstd1.0645381956701028
dvarsvstd1.0300518945360826
efc0.6512
fber134.2489
fdmean0.23027634523118337
fdnum56
fdperc57.142857142857146
fwhmavg3.6693109780199156
fwhmx3.553272243592381
fwhmy3.6361198641411
fwhmz3.818540826326266
gcor[0.00427551, 0.00502057, 0.00505444, 0.00393896]
gsrx0.14552442729473114
gsry0.014733555726706982
sizet98
sizex76
sizey102
sizez60
snr2.5762799231369624
spacingtr1.600000023841858
spacingx2.196079969406128
spacingy2.196079969406128
spacingz2.200000047683716
summarybgk5.9114
summarybgmad63.8124
summarybgmean169.3877
summarybgmedian69.0
summarybgn331587.0
summarybgp050.0
summarybgp95722.0
summarybgstdv239.1998
summaryfgk0.7168
summaryfgmad301.8609
summaryfgmean829.5287
summaryfgmedian800.0
summaryfgn133533.0
summaryfgp05386.0
summaryfgp951371.0
summaryfgstdv310.5241
tsnr37.19717612955719
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