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The Filter Cart provides a powerful way to query and access data for which you may be interested.  

A few points related to the filter cart are important to understand with the NDA Query/Filter implementation: 

First, the filter cart is populated asyncronously.  So, when you run a query, it may take a moment to populate but this will happen in the background so you can define other queries during this time.  

When you are adding your first filter, all data associated with your query will be added to the filter cart (whether it be a collection, a concept, a study, a data structure/elment or subjects). Not all data structures or collections will necessarily be displayed.  For example, if you select the NDA imaging structure image03, and further restrict that query to scan_type fMRI, only fMRI images will appear and only the image03 structure will be shown.  To see other data structures, select "Find All Subject Data" which will query all data for those subjects. When a secord or third filter is applied, an AND condition is used.  A subject must exist in all filters.  If the subject does not appear in any one filter, that subjects data will not be included in your filter cart. If that happens, clear your filter cart, and start over.  

It is best to package more data than you need and access those data using other tools, independent of the NDA (e.g. miNDAR snapshot), to limit the data selected.  If you have any questions on data access, are interested in using avaialble web services, or need help accessing data, please contact us for assistance.  

Frequently Asked Questions

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Magnetic Resonance Image Quality Metrics

mriqm

01

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Element NameData TypeSizeRequiredDescriptionValue RangeNotesAliases
subjectkeyGUIDRequiredThe NDAR Global Unique Identifier (GUID) for research subjectNDAR*
src_subject_idString20RequiredSubject ID how it's defined in lab/projectsubject_id
interview_dateDateRequiredDate on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYYRequired field
interview_ageIntegerRequiredAge in months at the time of the interview/test/sampling/imaging.0 :: 1260Age is rounded to chronological month. If the research participant is 15-days-old at time of interview, the appropriate value would be 0 months. If the participant is 16-days-old, the value would be 1 month.
sexString20RequiredSex of the subjectM;FM = Male; F = Femalegender
scan_typeString50RequiredType of ScanMR diffusion; fMRI; MR structural (MPRAGE); MR structural (T1); MR structural (PD); MR structural (FSPGR); MR structural (FISP); MR structural (T2); PET; ASL; microscopy; MR structural (PD, T2); MR structural (B0 map); MR structural (B1 map); single-shell DTI; multi-shell DTI; Field Map; X-Ray; static magnetic field B0; pCASL: ASL; MR: T2star; MR: FLAIR; Localizer scan; MR structural (FLASH); MR structural (MP2RAGE); MR structural (TSE); MR structural (T1, T2); 2D gradient echo
t1_cjvFloatConditionalcoefficient of joint variation (CJV): The cjv of GM and WM was proposed as objective function by [Ganzetti2016] for the optimization of INU correction algorithms. Higher values are related to the presence of heavy head motion and large INU artifacts. Lower values are better.
t1_cnrFloatConditionalcontrast-to-noise ratio (CNR): The cnr [Magnota2006], is an extension of the SNR calculation to evaluate how separated the tissue distributions of GM and WM are. Higher values indicate better quality.
t1_efcFloatConditionalThe EFC [Atkinson1997] uses the Shannon entropy of voxel intensities as an indication of ghosting and blurring induced by head motion. Lower values are better. The original equation is normalized by the maximum entropy, so that the EFC can be compared across images with different dimensions.
t1_fberFloatConditionalThe FBER [Shehzad2015], defined as the mean energy of image values within the head relative to outside the head [QAP-measures]. Higher values are better.
t1_fwhm_avgFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
t1_fwhm_xFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
t1_fwhm_yFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
t1_fwhm_zFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
t1_icvs_csfFloatConditionalThe ICV fractions of CSF, GM and WM. They should move within a normative range.
t1_icvs_gmFloatConditionalThe ICV fractions of CSF, GM and WM. They should move within a normative range.
t1_icvs_wmFloatConditionalThe ICV fractions of CSF, GM and WM. They should move within a normative range.
t1_inu_medFloatConditionalsummary statistics (max, min and median) of the INU field as extracted by the N4ITK algorithm [Tustison2010]. Values closer to 1.0 are better.
t1_inu_rangeFloatConditionalsummary statistics (max, min and median) of the INU field as extracted by the N4ITK algorithm [Tustison2010]. Values closer to 1.0 are better.
t1_qi_1FloatConditionalDetect artifacts in the image using the method described in [Mortamet2009]. The QI1 is the proportion of voxels with intensity corrupted by artifacts normalized by the number of voxels in the background. Lower values are better.
t1_qi_2FloatConditionalMortamet's quality index 2 (QI2) is a calculation of the goodness-of-fit of a chi-2 distribution on the air mask, once the artifactual intensities detected for computing the QI1 index have been removed [Mortamet2009].
t1_rpve_csfFloatConditionalthe rPVe of CSF, GM and WM. Lower values are better.
t1_rpve_gmFloatConditionalthe rPVe of CSF, GM and WM. Lower values are better.
t1_rpve_wmFloatConditionalthe rPVe of CSF, GM and WM. Lower values are better.
t1_size_xIntegerConditionalextent in number of voxels
t1_size_yIntegerConditionalextent in number of voxels
t1_size_zIntegerConditionalextent in number of voxels
t1_snr_csfFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
t1_snr_gmFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
t1_snr_totalFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
t1_snr_wmFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
t1_snrd_csfFloatConditionalDietrich's SNR (SNRd) as proposed by [Dietrich2007], using the air background as reference.
t1_snrd_gmFloatConditionalDietrich's SNR (SNRd) as proposed by [Dietrich2007], using the air background as reference.
t1_snrd_totalFloatConditionalDietrich's SNR (SNRd) as proposed by [Dietrich2007], using the air background as reference.
t1_snrd_wmFloatConditionalDietrich's SNR (SNRd) as proposed by [Dietrich2007], using the air background as reference.
t1_spacing_xFloatConditionalDistance in mm betwen centers of adjacent voxels in x direction
t1_spacing_yFloatConditionalDistance in mm betwen centers of adjacent voxels in y direction
t1_spacing_zFloatConditionalDistance in mm betwen centers of adjacent voxels in z direction
t1_summary_bg_kFloatConditionalbackground kurtosis
t1_summary_bg_madFloatConditionalbackground median absolute deviation
t1_summary_bg_meanFloatConditionalbackground mean
t1_summary_bg_medianFloatConditionalbackground median absolute deviation
t1_summary_bg_nFloatConditionalbackground count of voxels
t1_summary_bg_p05FloatConditionalbackground 5th percentile
t1_summary_bg_p95FloatConditionalbackground 95th percentile
t1_summary_bg_stdvFloatConditionalbackground standard deviation
t1_summary_csf_kFloatConditionalcerebrospinal fluid kurtosis
t1_summary_csf_madFloatConditionalcerebrospinal fluid median absolute deviation
t1_summary_csf_meanFloatConditionalcerebrospinal fluid mean
t1_summary_csf_medianFloatConditionalcerebrospinal fluid median absolute deviation
t1_summary_csf_nFloatConditionalcerebrospinal fluid count of voxels
t1_summary_csf_p05FloatConditionalcerebrospinal fluid 5th percentile
t1_summary_csf_p95FloatConditionalcerebrospinal fluid 95th percentile
t1_summary_csf_stdvFloatConditionalcerebrospinal fluid standard deviation
t1_summary_gm_kFloatConditionalgrey matter kurtosis
t1_summary_gm_madFloatConditionalgrey matter median absolute deviation
t1_summary_gm_meanFloatConditionalgrey matter mean
t1_summary_gm_medianFloatConditionalgrey matter median absolute deviation
t1_summary_gm_nFloatConditionalgrey matter count of voxels
t1_summary_gm_p05FloatConditionalgrey matter 5th percentile
t1_summary_gm_p95FloatConditionalgrey matter 95th percentile
t1_summary_gm_stdvFloatConditionalgrey matter standard deviation
t1_summary_wm_kFloatConditionalwhite matter kurtosis
t1_summary_wm_madFloatConditionalwhite matter median absolute deviation
t1_summary_wm_meanFloatConditionalwhite matter mean
t1_summary_wm_medianFloatConditionalwhite matter median absolute deviation
t1_summary_wm_nFloatConditionalwhite matter count of voxels
t1_summary_wm_p05FloatConditionalwhite matter 5th percentile
t1_summary_wm_p95FloatConditionalwhite matter 95th percentile
t1_summary_wm_stdvFloatConditionalwhite matter standard deviation
t1_tpm_overlap_csfFloatConditionalThe overlap of the TPMs estimated from the image and the corresponding maps from the ICBM nonlinear-asymmetric 2009c template.
t1_tpm_overlap_gmFloatConditionalThe overlap of the TPMs estimated from the image and the corresponding maps from the ICBM nonlinear-asymmetric 2009c template.
t1_tpm_overlap_wmFloatConditionalThe overlap of the TPMs estimated from the image and the corresponding maps from the ICBM nonlinear-asymmetric 2009c template.
t1_wm2maxFloatConditionalThe overlap of the TPMs estimated from the image and the corresponding maps from the ICBM nonlinear-asymmetric 2009c template.
t2_cjvFloatConditionalcoefficient of joint variation (CJV): The cjv of GM and WM was proposed as objective function by [Ganzetti2016] for the optimization of INU correction algorithms. Higher values are related to the presence of heavy head motion and large INU artifacts. Lower values are better.
t2_cnrFloatConditionalcontrast-to-noise ratio (CNR): The cnr [Magnota2006], is an extension of the SNR calculation to evaluate how separated the tissue distributions of GM and WM are. Higher values indicate better quality.
t2_efcFloatConditionalThe EFC [Atkinson1997] uses the Shannon entropy of voxel intensities as an indication of ghosting and blurring induced by head motion. Lower values are better. The original equation is normalized by the maximum entropy, so that the EFC can be compared across images with different dimensions.
t2_fberFloatConditionalThe FBER [Shehzad2015], defined as the mean energy of image values within the head relative to outside the head [QAP-measures]. Higher values are better.
t2_fwhm_avgFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
t2_fwhm_xFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
t2_fwhm_yFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
t2_fwhm_zFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
t2_icvs_csfFloatConditionalThe ICV fractions of CSF, GM and WM. They should move within a normative range.
t2_icvs_gmFloatConditionalThe ICV fractions of CSF, GM and WM. They should move within a normative range.
t2_icvs_wmFloatConditionalThe ICV fractions of CSF, GM and WM. They should move within a normative range.
t2_inu_medFloatConditionalsummary statistics (max, min and median) of the INU field as extracted by the N4ITK algorithm [Tustison2010]. Values closer to 1.0 are better.
t2_inu_rangeFloatConditionalsummary statistics (max, min and median) of the INU field as extracted by the N4ITK algorithm [Tustison2010]. Values closer to 1.0 are better.
t2_qi_1FloatConditionalDetect artifacts in the image using the method described in [Mortamet2009]. The QI1 is the proportion of voxels with intensity corrupted by artifacts normalized by the number of voxels in the background. Lower values are better.
t2_qi_2FloatConditionalMortamet's quality index 2 (QI2) is a calculation of the goodness-of-fit of a chi-2 distribution on the air mask, once the artifactual intensities detected for computing the QI1 index have been removed [Mortamet2009].
t2_rpve_csfFloatConditionalthe rPVe of CSF, GM and WM. Lower values are better.
t2_rpve_gmFloatConditionalthe rPVe of CSF, GM and WM. Lower values are better.
t2_rpve_wmFloatConditionalthe rPVe of CSF, GM and WM. Lower values are better.
t2_size_xIntegerConditionalextent in number of voxels
t2_size_yIntegerConditionalextent in number of voxels
t2_size_zIntegerConditionalextent in number of voxels
t2_snr_csfFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
t2_snr_gmFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
t2_snr_totalFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
t2_snr_wmFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
t2_snrd_csfFloatConditionalDietrich's SNR (SNRd) as proposed by [Dietrich2007], using the air background as reference.
t2_snrd_gmFloatConditionalDietrich's SNR (SNRd) as proposed by [Dietrich2007], using the air background as reference.
t2_snrd_totalFloatConditionalDietrich's SNR (SNRd) as proposed by [Dietrich2007], using the air background as reference.
t2_snrd_wmFloatConditionalDietrich's SNR (SNRd) as proposed by [Dietrich2007], using the air background as reference.
t2_spacing_xFloatConditionalDistance in mm betwen centers of adjacent voxels in x direction
t2_spacing_yFloatConditionalDistance in mm betwen centers of adjacent voxels in y direction
t2_spacing_zFloatConditionalDistance in mm betwen centers of adjacent voxels in z direction
t2_summary_bg_kFloatConditionalbackground kurtosis
t2_summary_bg_madFloatConditionalbackground median absolute deviation
t2_summary_bg_meanFloatConditionalbackground mean
t2_summary_bg_medianFloatConditionalbackground median absolute deviation
t2_summary_bg_nFloatConditionalbackground count of voxels
t2_summary_bg_p05FloatConditionalbackground 5th percentile
t2_summary_bg_p95FloatConditionalbackground 95th percentile
t2_summary_bg_stdvFloatConditionalbackground standard deviation
t2_summary_csf_kFloatConditionalcerebrospinal fluid kurtosis
t2_summary_csf_madFloatConditionalcerebrospinal fluid median absolute deviation
t2_summary_csf_meanFloatConditionalcerebrospinal fluid mean
t2_summary_csf_medianFloatConditionalcerebrospinal fluid median absolute deviation
t2_summary_csf_nFloatConditionalcerebrospinal fluid count of voxels
t2_summary_csf_p05FloatConditionalcerebrospinal fluid 5th percentile
t2_summary_csf_p95FloatConditionalcerebrospinal fluid 95th percentile
t2_summary_csf_stdvFloatConditionalcerebrospinal fluid standard deviation
t2_summary_gm_kFloatConditionalgrey matter kurtosis
t2_summary_gm_madFloatConditionalgrey matter median absolute deviation
t2_summary_gm_meanFloatConditionalgrey matter mean
t2_summary_gm_medianFloatConditionalgrey matter median absolute deviation
t2_summary_gm_nFloatConditionalgrey matter count of voxels
t2_summary_gm_p05FloatConditionalgrey matter 5th percentile
t2_summary_gm_p95FloatConditionalgrey matter 95th percentile
t2_summary_gm_stdvFloatConditionalgrey matter standard deviation
t2_summary_wm_kFloatConditionalwhite matter kurtosis
t2_summary_wm_madFloatConditionalwhite matter median absolute deviation
t2_summary_wm_meanFloatConditionalwhite matter mean
t2_summary_wm_medianFloatConditionalwhite matter median absolute deviation
t2_summary_wm_nFloatConditionalwhite matter count of voxels
t2_summary_wm_p05FloatConditionalwhite matter 5th percentile
t2_summary_wm_p95FloatConditionalwhite matter 95th percentile
t2_summary_wm_stdvFloatConditionalwhite matter standard deviation
t2_tpm_overlap_csfFloatConditionalThe overlap of the TPMs estimated from the image and the corresponding maps from the ICBM nonlinear-asymmetric 2009c template.
t2_tpm_overlap_gmFloatConditionalThe overlap of the TPMs estimated from the image and the corresponding maps from the ICBM nonlinear-asymmetric 2009c template.
t2_tpm_overlap_wmFloatConditionalThe overlap of the TPMs estimated from the image and the corresponding maps from the ICBM nonlinear-asymmetric 2009c template.
t2_wm2maxFloatConditionalThe white-matter to maximum intensity ratio is the median intensity within the WM mask over the 95% percentile of the full intensity distribution, that captures the existence of long tails due to hyper-intensity of the carotid vessels and fat. Values should be around the interval [0.6, 0.8].
bold_dummy_trsIntegerConditionalNumber of volumes (“dummy scans”) discarded by the scanner (as opposed to those discarded by the user post hoc) before saving the imaging file. For example, a sequence that automatically discards the first 4 volumes before saving would have this field as 4. A sequence that doesn’t discard dummy scans would have this set to 0.
bold_dvars_nstdFloatConditionalD referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels ([Power2012] dvars_nstd) indexes the rate of change of BOLD signal across the entire brain at each frame of data. DVARS is calculated with nipype after motion correction
bold_dvars_stdFloatConditionalD referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels ([Power2012] dvars_nstd) indexes the rate of change of BOLD signal across the entire brain at each frame of data. DVARS is calculated with nipype after motion correction
bold_dvars_vstdFloatConditionalD referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels ([Power2012] dvars_nstd) indexes the rate of change of BOLD signal across the entire brain at each frame of data. DVARS is calculated with nipype after motion correction
bold_efcFloatConditionalThe EFC [Atkinson1997] uses the Shannon entropy of voxel intensities as an indication of ghosting and blurring induced by head motion. Lower values are better. The original equation is normalized by the maximum entropy, so that the EFC can be compared across images with different dimensions.
bold_fberFloatConditionalThe FBER [Shehzad2015], defined as the mean energy of image values within the head relative to outside the head [QAP-measures]. Higher values are better.
bold_fd_meanFloatConditionalexpresses instantaneous head-motion. MRIQC reports the average FD, labeled as fd_mean. Rotational displacements are calculated as the displacement on the surface of a sphere of radius 50 mm. Along with the base framewise displacement, MRIQC reports the number of timepoints above FD threshold (fd_num), and the percent of FDs above the FD threshold w.r.t. the full timeseries (fd_perc). In both cases, the threshold is set at 0.20mm. [Power2012]:
bold_fd_numFloatConditionalexpresses instantaneous head-motion. MRIQC reports the average FD, labeled as fd_mean. Rotational displacements are calculated as the displacement on the surface of a sphere of radius 50 mm. Along with the base framewise displacement, MRIQC reports the number of timepoints above FD threshold (fd_num), and the percent of FDs above the FD threshold w.r.t. the full timeseries (fd_perc). In both cases, the threshold is set at 0.20mm. [Power2012]:
bold_fd_percFloatConditionalexpresses instantaneous head-motion. MRIQC reports the average FD, labeled as fd_mean. Rotational displacements are calculated as the displacement on the surface of a sphere of radius 50 mm. Along with the base framewise displacement, MRIQC reports the number of timepoints above FD threshold (fd_num), and the percent of FDs above the FD threshold w.r.t. the full timeseries (fd_perc). In both cases, the threshold is set at 0.20mm. [Power2012]:
bold_fwhm_avgFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
bold_fwhm_xFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
bold_fwhm_yFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
bold_fwhm_zFloatConditionalThe FWHM of the spatial distribution of the image intensity values in units of voxels [Forman1995]. Lower values are better. Uses the gaussian width estimator filter implemented in AFNI's 3dFWHMx
bold_gcorFloatConditionalGlobal Correlation (gcor) calculates an optimized summary of time-series correlation as in [Saad2013] using AFNI's @compute_gcor
bold_gsr_xFloatConditionalGhost to Signal Ratio (gsr(), labeled in the reports as gsr_x and gsr_y): along the two possible phase-encoding axes x, y
bold_gsr_yFloatConditionalGhost to Signal Ratio (gsr(), labeled in the reports as gsr_x and gsr_y): along the two possible phase-encoding axes x, y
bold_prov_set_fd_threshFloatConditionalProvisional setting of framewise displacement threshold
bold_size_tFloatConditionalnumber of time points
bold_size_xFloatConditionalextent in number of voxels
bold_size_yFloatConditionalextent in number of voxels
bold_size_zFloatConditionalextent in number of voxels
bold_snrFloatConditionalsignal-to-noise ratio (SNR): calculated within the tissue mask.
bold_spacing_trFloatConditionalLength of time between the begining of adjacent trs in s
bold_spacing_xFloatConditionalDistance in mm betwen centers of adjacent voxels in x direction
bold_spacing_yFloatConditionalDistance in mm betwen centers of adjacent voxels in y direction
bold_spacing_zFloatConditionalDistance in mm betwen centers of adjacent voxels in z direction
bold_summary_bg_kFloatConditionalbackground kurtosis
bold_summary_bg_madFloatConditionalbackground median absolute deviation
bold_summary_bg_meanFloatConditionalbackground mean
bold_summary_bg_medianFloatConditionalbackground median absolute deviation
bold_summary_bg_nFloatConditionalbackground count of voxels
bold_summary_bg_p05FloatConditionalbackground 5th percentile
bold_summary_bg_p95FloatConditionalbackground 95th percentile
bold_summary_bg_stdvFloatConditionalbackground standard deviation
bold_summary_fg_kFloatConditionalforeground kurtosis
bold_summary_fg_madFloatConditionalforeground median absolute deviation
bold_summary_fg_meanFloatConditionalforeground mean
bold_summary_fg_medianFloatConditionalforeground median absolute deviation
bold_summary_fg_nFloatConditionalforeground count of voxels
bold_summary_fg_p05FloatConditionalforeground 5th percentile
bold_summary_fg_p95FloatConditionalforeground 95th percentile
bold_summary_fg_stdvFloatConditionalforeground standard deviation
bold_tsnrFloatConditionalTemporal SNR (tSNR, tsnr) is a simplified interpretation of the tSNR definition [Kruger2001]. We report the median value of the tSNR map calculated like: where _S_t is the average BOLD signal (across time), and _t is the corresponding temporal standard-deviation map.
accelnumreferencelinesFloatRecommendedAcceleration number of reference lines
accelerationfactorpeFloatRecommendedAcceleration factor in the phase encode direction
acquisitionmatrixString50RecommendedDimensions of the acquired frequency /phase data before reconstruction. Multi-valued: frequency rowsfrequency columnsphase rowsphase columns. ID=DICOM:0018_1310
cogatlasidString50RecommendedURL for task in the cognitive atlas (http://www.cognitiveatlas.org)
cogpoidString50RecommendedURL for task in cogpowiki(http://wiki.cogpo.org)
coilcombinationmethodFloatRecommendedAlmost all fMRI studies using phased-array coils use root-sum-of-squares (rSOS) combination, but other methods exist. The image reconstruction is changed by the coil combination method (as for the matrix coil mode above), so anything non-standard should be reported
conversionsoftwareString50RecommendedSoftware used to convert dicom to nifti
conversionsoftwareversionString50RecommendedRelease version of the software used to convert dicom to nifti
cardiactriggerdelaytimesFloatRecommendedCardiac Trigger Delay Times
deviceserialnumberString200Recommendeddevice serial number/ID
echotimeFloatRecommendedTime in ms between the middle of the excitation pulse and the peak of the echo produced (kx=0). In the case of segmented k-space, the TE(eff) is the time between the middle of the excitation pulse to the peak of the echo that is used to cover the center of k-space (i.e.,-kx=0, ky=0). ID=DICOM:0018_0081
echotrainlengthFloatRecommendedNumber of lines in k-space acquired per excitation of the same volume regardless of the type of echo or the number of frames derived from them. ID:DICOM:0018_0091
effectiveechospacingFloatRecommendedThe "effective" sampling interval, specified in seconds, between lines in the phase-encoding direction, defined based on the size of the reconstructed image in the phase direction. It is frequently, but incorrectly, referred to as "dwell time" (see DwellTime parameter below for actual dwell time). It is required for unwarping distortions using field maps. Note that beyond just in-plane acceleration, a variety of other manipulations to the phase encoding need to be accounted for properly, including partial fourier, phase oversampling, phase resolution, phase field-of-view and interpolation.2 This parameter is REQUIRED if corresponding fieldmap data is present.
flipangleFloatRecommendedFlip angle for the acquisition, specified in degrees. Corresponds to: DICOM Tag 0018, 1314 Flip Angle.
hardcopydevicesoftwareversionString50RecommendedManufacturers designation of the software of the device that created this Hardcopy Image (the printer). Corresponds to DICOM Tag 0018, 101A Hardcopy Device Software Version
imagetypeString50RecommendedImage Type
imagingfrequencyFloatRecommendedImaging Frequency
inplanephaseencodingdirectionString50RecommendedThe axes of the in-plane phase encoding with respect to the frame. ID:DICOM:0018_1312
institutionaddressString50RecommendedMailing Address of the institution where the equipment is located.ID:DICOM:0008_0081
institutionnameString50RecommendedThe name of the institution in charge of the equipment that produced the composite instances. Corresponds to DICOM Tag 0008, 0080 InstitutionName.
instString50RecommendedText of the instructions given to participants before the scan. This is especially important in context of resting state fMRI and distinguishing between eyes open and eyes closed paradigms.instructions
inversiontimeFloatRecommendedThe inversion time (TI) for the acquisition, specified in seconds. Inversion time is the time after the middle of inverting RF pulse to middle of excitation pulse to detect the amount of longitudinal magnetization. Corresponds to DICOM Tag 0018, 0082 Inversion Time (please note that the DICOM term is in milliseconds not seconds).
mracquisitiontypeString50RecommendedIdentification of data encoding scheme. ID:DICOM:0018_0023
magneticfieldstrengthFloatRecommendedNominal field strength of MR magnet in Tesla. ID:DICOM:0018_0087
manufacturerString100RecommendedManufacturer
manufacturersmodelnameString50RecommendedManufacturer's model name of the equipment that produced the composite instances. Corresponds to DICOM Tag 0008, 1090 Manufacturers Model Name
matrixcoilmodeFloatRecommendedA method for reducing the number of independent channels by combining in analog the signals from multiple coil elements. There are typically different default modes when using un-accelerated or accelerated (e.g. GRAPPA, SENSE) imaging
multibandaccelerationfactorFloatRecommendedThe multiband factor, for multiband acquisitions.
numberofaveragesFloatRecommendedNumber of times a given pulse sequence is repeated before any parameter is changed. ID:DICOM:0018_0083
numberofphaseencodingstepsFloatRecommendedTotal number of lines in k-space in the "y" direction collected during acquisition. ID:DICOM:0018_0089
num_vols_disc_scannerFloatRecommendedNumber of volumes (“dummy scans”) discarded by the scanner (as opposed to those discarded by the user post hoc) before saving the imaging file. For example, a sequence that automatically discards the first 4 volumes before saving would have this field as 4. A sequence that doesn’t discard dummy scans would have this set to 0.
num_vols_disc_userFloatRecommendedNumber of volumes ("dummy scans") discarded by the user before including the file in the dataset. If possible, including all of the volumes is strongly recommended. Please note that the onsets recorded in the _event.tsv file should always refer to the beginning of the acquisition of the first volume in the corresponding imaging file - independent of the value of NumberOfVolumesDiscardedByUser field.
numbershotsFloatRecommendedThe number of RF excitations need to reconstruct a slice or volume. Please mind that this is not the same as Echo Train Length which denotes the number of lines of k-space collected after an excitation.
parallelacquisitiontechniqueString50RecommendedThe type of parallel imaging used (e.g. GRAPPA, SENSE). Corresponds to DICOM Tag 0018, 9078 Parallel Acquisition Technique.
par_red_fact_inplaneFloatRecommendedThe parallel imaging (e.g, GRAPPA) factor. Use the denominator of the fraction of k-space encoded for each slice. For example, 2 means half of k-space is encoded. Corresponds to DICOM Tag 0018, 9069 Parallel Reduction Factor In-plane.
partialfourierString50RecommendedThe fraction of partial Fourier information collected. Corresponds to DICOM Tag 0018, 9081 Partial Fourier.
patientpositionString50RecommendedDescription of imaging subject's position relative to the equipment. ID:DICOM:0018_5100
percentphasefieldofviewFloatRecommendedRatio of field of view dimension in phase direction to field of view dimension in frequency direction, expressed as a percent. ID:DICOM:0018_0094
percentsamplingFloatRecommendedFraction of acquisition matrix lines acquired, expressed as a percent. ID:DICOM:0018_0093
phaseencodingdirectionString50RecommendedPossible values: i, j, k, i-, j-, k-. The letters i, j, k correspond to the first, second and third axis of the data in the NIFTI file. The polarity of the phase encoding is assumed to go from zero index to maximum index unless - sign is present (then the order is reversed - starting from the highest index instead of zero). PhaseEncodingDirection is defined as the direction along which phase is was modulated which may result in visible distortions. Note that this is not the same as the DICOM term InPlanePhaseEncodingDirection which can have ROW or COL values. This parameter is REQUIRED if corresponding fieldmap data is present or when using multiple runs with different phase encoding directions (which can be later used for field inhomogeneity correction).
pixelbandwidthFloatRecommendedReciprocal of the total sampling period, in hertz per pixel. ID:DICOM:0018_0095
protocolnameString50RecommendedDescription of the conditions under which the Series was performed. ID:DICOM:0018_1030
pulsesequencedetailsString50RecommendedInformation beyond pulse sequence type that identifies the specific pulse sequence used (i.e. "Standard Siemens Sequence distributed with the VB17 software," "Siemens WIP ### version #.##," or "Sequence written by X using a version compiled on MM/DD/YYYY").
pulsesequencetypeString50RecommendedA general description of the pulse sequence used for the scan (i.e. MPRAGE, Gradient Echo EPI, Spin Echo EPI, Multiband gradient echo EPI).
receivecoilnameString50RecommendedInformation describing the receiver coil. Corresponds to DICOM Tag 0018, 1250 Receive Coil Name, although not all vendors populate that DICOM Tag, in which case this field can be derived from an appropriate private DICOM field
repetitiontimeFloatRecommendedThe time in seconds between the beginning of an acquisition of one volume and the beginning of acquisition of the volume following it (TR). Please note that this definition includes time between scans (when no data has been acquired) in case of sparse acquisition schemes. This value needs to be consistent with the pixdim[4] field (after accounting for units stored in xyzt_units field) in the NIfTI header. This field is mutually exclusive with VolumeTiming and is derived from DICOM Tag 0018, 0080 and converted to seconds.
scanoptionsString50RecommendedParameters of ScanningSequence. Corresponds to DICOM Tag 0018, 0022 Scan Options.
scanningsequenceString50RecommendedDescription of the type of data acquired. Corresponds to DICOM Tag 0018, 0020 Scanning Sequence.
sequencenameString50RecommendedManufacturers designation of the sequence name. Corresponds to DICOM Tag 0018, 0024 Sequence Name.
sequencevariantString50RecommendedVariant of the ScanningSequence. Corresponds to DICOM Tag 0018, 0021 Sequence Variant.
sliceencodingdirectionString50RecommendedPossible values: i, j, k, i-, j-, k- (the axis of the NIfTI data along which slices were acquired, and the direction in which SliceTiming is defined with respect to). i, j, k identifiers correspond to the first, second and third axis of the data in the NIfTI file. A - sign indicates that the contents of SliceTiming are defined in reverse order - that is, the first entry corresponds to the slice with the largest index, and the final entry corresponds to slice index zero. When present, the axis defined by SliceEncodingDirection needs to be consistent with the ‘slice_dim’ field in the NIfTI header. When absent, the entries in SliceTiming must be in the order of increasing slice index as defined by the NIfTI header.
softwareversionsString50RecommendedManufacturers designation of software version of the equipment that produced the composite instances. Corresponds to DICOM Tag 0018, 1020 Software Versions
taskdescriptionString50RecommendedLonger description of the task.
tasknameString50RecommendedName of the task. No two tasks should have the same name. Task label (task-) included in the file name is derived from this field by removing all non alphanumeric ([a-zA-Z0-9]) characters. For example task name faces n-back will corresponds to task label facesnback. A RECOMMENDED convention is to name resting state task using labels beginning with rest.
totalreadouttimeFloatRecommendedThis is actually the "effective" total readout time , defined as the readout duration, specified in seconds, that would have generated data with the given level of distortion. It is NOT the actual, physical duration of the readout train. If EffectiveEchoSpacing has been properly computed, it is just EffectiveEchoSpacing * (ReconMatrixPE - 1).3 . This parameter is REQUIRED if corresponding "field/distortion" maps acquired with opposing phase encoding directions are present (see 8.9.4).
totalscantimesecFloatRecommendedTotal time of the scan in seconds
transmitcoilnameString50RecommendedName of transmit coil used. ID:DICOM:0018_1251
variableflipangleflagString50RecommendedFlip angle variation applied during image acquisition. ID:DICOM:0018_1315
acq_idString50RecommendedAcquisition ID
run_idString50RecommendedRun ID
session_idString20Recommendedsession ID/screening ID
contrast_bolus_ingredientString50RecommendedActive ingredient of agent. Values MUST be one of: IODINE, GADOLINIUM, CARBON DIOXIDE, BARIUM, XENON Corresponds to DICOM Tag 0018,1048.
grad_set_typeString50RecommendedIt should be possible to infer the gradient coil from the scanner model. If not, e.g. because of a custom upgrade or use of a gradient insert set, then the specifications of the actual gradient coil should be reported independently
mr_transmit_coil_seqString50RecommendedThis is a relevant field if a non-standard transmit coil is used. Corresponds to DICOM Tag 0018, 9049 MR Transmit Coil Sequence
partial_fourier_dirString50RecommendedThe direction where only partial Fourier information was collected. Corresponds to DICOM Tag 0018, 9036 Partial Fourier Direction.
image_modalityString20RecommendedImage modalityMRI; PET; CT; SPECT; ultrasound; FA; X-Ray; spectroscopy; microscopyComputed Radiography; Computed Tomography; External Camera Photography; FA; General Microscopy; MRI; Magnetic Resonance; Magnetic Resonance Spectroscopy; Nuclear Medicine; Single Photon Emission Computed Tomography; Ultrasound
quest_instructString4,000RecommendedQuestionnaire InstructionsQuestionnaire Instructions
Data Structure

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