Appendix XI: Quantitative MRI
Contents
Appendix XI: Quantitative MRI#
Quantitative MRI (qMRI) is a collection of methods aiming at generating parametric maps
that can characterize underlying tissue properties.
Unlike those of conventional MR images (for example, T1w
or T2w
),
intensity values of quantitative maps are not represented in an arbitrary range.
Instead, these maps are represented either in absolute physical units
(for example, seconds
for T1map
),
or within an application dependent range of arbitrary units
(for example, myelin water fraction MWFmap
in brain).
Organization of qMRI data in BIDS#
Unlike conventional MR images, quantitative maps are not immediate products of the image reconstruction step (from k-space data to structural images). Intensity values of qMRI maps are calculated by fitting a collection of parametrically linked images to a biophysical model or to an MRI signal representation. This processing is typically carried out in the image domain. There are two main ways to obtain a quantitative map:
Pre-generated qMRI maps: The qMRI maps are generated right after the reconstruction of required input images and made available to the user at the scanner console. The acquisition scenarios may include (a) vendor pipelines or (b) open-source pipelines deployed at the scanner site.
Post-generated qMRI maps: The qMRI maps are generated from a collection of input data after they are exported from the scanner site. This type of processing is commonly carried out using an open-source software such as hMRI toolbox, mrQ, PyQMRI, qmap, qMRLab, and QUIT.
Inputs are file collections#
The common concept of entity-linked file collections enables the description of a qMRI
application by creating logical groups of input files through suffix
and certain entities
representing acquisition parameters (echo
, flip
, inv
, mt
) or file parts (part
).
If a qMRI file collection is intended for creating structural quantitative maps (for example, T1map
),
files belonging to that collection are stored in the anat
subdirectory.
Below is an example file collection for MP2RAGE
:
Commonly, RF fieldmaps (B1+
and B1-
maps) are used for the correction of structural quantitative maps.
As these images do not convey substantial structural information,
respective file collections of RF fieldmaps are stored in the fmap
subdirectory.
Below is an example file collection for RF transmit field map TB1EPI
:
Please visit the file collections appendix to see the list of currently supported qMRI applications.
Quantitative maps are derivatives#
Regardless of how they are obtained (pre- or post-generated), qMRI maps are stored in the derivatives
directory.
For example a T1map
can be generated from an MP2RAGE
file collection using either options.
If the map is post-generated:
If the map is pre-generated, for example, by a Siemens scanner:
Note: Even though the process from which pre-generated qMRI maps are obtained (vendor pipelines) is not known, vendors generally allow exporting of the corresponding input data. It is RECOMMENDED to share them along with the vendor outputs, whenever possible for a qMRI method supported by BIDS.
Example datasets#
You can find example file collections and qMRI maps organized according to BIDS in the BIDS examples.
Metadata requirements for qMRI data#
The table of required entities for qMRI file collections are provided in the entity table. However, viability of a qMRI file collection is determined not only by the naming and organization of the input files, but also by which metadata fields are provided in accompanying json files.
Method-specific priority levels for qMRI file collections#
Anatomy imaging data#
File collection |
REQUIRED metadata |
OPTIONAL metadata |
---|---|---|
VFA |
|
|
IRT1 |
|
|
MP2RAGE* |
|
|
MESE |
|
|
MEGRE |
|
|
MTR |
|
|
MTS |
|
|
MPM |
|
|
* Please see MP2RAGE-specific notes for the calculation of NumberShots
and regarding the
organization of UNIT1
image.
Explanation of the table:
The metadata fields listed in the REQUIRED column are needed to perform a minimum viable qMRI processing for the corresponding
file collection
.Note that some of the metadata fields may be constant across different files in a file collection, yet still required as an input (for example,
NumberShots
inMP2RAGE
). Such metadata fields MUST be provided in the accompanying JSON files.The metadata fields listed in the OPTIONAL column can be used to form different flavors of an existing file collection suffix, dispensing with the need for introducing a new suffix. See deriving the intended qMRI application from an ambiguous file collection for details.
Field maps#
File collection |
REQUIRED metadata |
---|---|
TB1DAM |
|
TB1EPI |
|
TB1AFI |
|
TB1TFL |
|
TB1RFM |
|
TB1SRGE* |
|
RB1COR |
* Please see TB1SRGE-specific notes for the calculation of NumberShots
.
Metadata requirements for qMRI maps#
As qMRI maps are stored as derivatives, they are subjected to the metadata requirements of derived datasets.
An example dataset_description.json
for a qMRI map derivatives directory:
dataset_description.json
:
{
"Name": "qMRLab Outputs",
"BIDSVersion": "1.5.0",
"DatasetType": "derivative",
"GeneratedBy": [
{
"Name": "qMRLab",
"Version": "2.4.1",
"Container": {
"Type": "docker",
"Tag": "qmrlab/minimal:2.4.1"
}
},
{
"Name": "Manual",
"Description": "Generated example T1map outputs"
}
],
"SourceDatasets": [
{
"DOI": "DOI 10.17605/OSF.IO/K4BS5",
"URL": "https://osf.io/k4bs5/",
"Version": "1"
}
]
}
In addition to the metadata fields provided in the dataset_description.json
,
qMRI maps are RECOMMENDED to be accompanied by sidecar JSON files that contain further information about the quantified maps.
Although this may not be the generic case for common derivative outputs,
a proper interpretation of qMRI maps may critically depend on some metadata fields.
For example, without the information of MagneticFieldStrength
, white-matter T1 values in a T1map
become elusive.
All the acquisition parameters that are constant across the files in a file collection are RECOMMENDED to be added to the sidecar json of the qMRI maps.
Relevant acquisition parameters that vary across files in a qMRI file collection are RECOMMENDED to be added to the sidecar json of the qMRI map in array form.
The JSON file accompanying a qMRI map which is obtained by using open-source software is RECOMMENDED to include additional metadata fields listed in the following table:
Example:
sub-01_T1map.nii.gz
sub-01_T1map.json
sub-01_T1map.json:
{
<<Parameter injected by the software/pipeline>>
"BasedOn":["anat/sub-01_flip-1_VFA.nii.gz",
"anat/sub-01_flip-2_VFA.nii.gz",
"anat/sub-01_flip-3_VFA.nii.gz",
"anat/sub-01_flip-4_VFA.nii.gz",
"fmap/sub-01_TB1map.nii.gz"],
"EstimationPaper":"Deoni et. al.MRM, 2015",
"EstimationAlgorithm":"Linear",
"Units": "second",
<<Parameters that are constant across files in the (parent) file collection>>
"MagneticFieldStrength": "3",
"Manufacturer": "Siemens",
"ManufacturerModelName": "TrioTim",
"InstitutionName": "xxx",
"PulseSequenceType": "SPGR",
"PulseSequenceDetails": "Information beyond the sequence type that identifies
specific pulse sequence used (VB version, if not standard, Siemens WIP XXX
ersion ### sequence written by xx using a version compiled on mm/dd/yyyy/)",
"RepetitionTimeExcitation": "35",
"EchoTime": "2.86",
"SliceThickness": "5",
<<Relevant parameters that vary across the linking entity of the (parent) file collection>>
"FlipAngle": ["5","10","15","20"]
}
Deriving the intended qMRI application from an ambiguous file collection#
Certain file collection suffixes may refer to a generic data collection regime such as variable flip angle (VFA), rather than a more specific acquisition, for example, magnetization prepared two gradient echoes (MP2RAGE). Such generic acquisitions can serve as a basis to derive various qMRI applications by changes to the acquisition sequence (for example, readout) type or by varying additional scan parameters.
If such an inheritance relationship is applicable between an already existing file collection and a new qMRI application to be included in the specification, the inheritor qMRI method is listed in the table below instead of introducing a new file collection suffix. This approach aims at:
preventing the list of available suffixes from over-proliferation,
providing qMRI-focused BIDS applications with a set of meta-data driven rules to infer possible fitting options,
keeping an inheritance track of the qMRI methods described within the specification.
File-collection suffix |
If REQUIRED metadata == Value |
OPTIONAL metadata ( |
Derived application name (NOT a suffix) |
---|---|---|---|
VFA |
|
DESPOT1 |
|
VFA |
|
|
DESPOT2 |
MP2RAGE |
|
MP2RAGE-ME |
|
MPM |
|
MPM-ME |
In this table, (entity
/fixed
) denotes whether the OPTIONAL metadata that forms a new
flavor of qMRI application for the respective suffix varies across files of a file collection
(which calls for using a linking entity) or fixed. If former is the case, the entity is to be
added to the files in that file collection. Note that this addition MUST be allowed by the
priority levels given for that suffix in the entity table
. If latter (fixed
) is the case,
filenames will remain the same; however, the optional metadata (third column) may
define the flavor of the application (fourth column) along with the conditional value of a
required metadata field (second column).
A derived qMRI application becomes available if all the optional metadata fields listed for the respective file collection suffix are provided for the data. In addition, conditional rules based on the value of a given required metadata field can be set for the description of a derived qMRI application. Note that the value of this required metadata is fixed across constituent images of a file collection and defined in Method-specific priority levels for qMRI file collections.
For example, if the optional metadata field of PulseSequenceType
is SPGR
for a collection of anatomical images listed by the VFA
suffix, the data
qualifies for DESPOT1
T1 fitting. For the same suffix, if the PulseSequenceType
metadata field has the value of SSFP
, and the SpoilingRFPhaseIncrement
is
provided as a metadata field, then the dataset becomes eligible for DESPOT2
T2 fitting application.
Please note that optional metadata fields listed in the deriving the intended qMRI application from an ambiguous file collection table are included in the optional (third) column of the priority levels table for the consistency of this appendix.
Introducing a new qMRI file collection#
If a qMRI application cannot be interpreted as a subtype of an already existing suffix of a qMRI-related file collection, we RECOMMEND adhering to the following principles to introduce a new suffix:
All qMRI-relevant file collection suffixes are capitalized.
Unless the pulse sequence is exclusively associated with a specific qMRI application (for example,
MP2RAGE
), sequence names are not used as suffixes.File collection suffixes for qMRI applications attain a clear description of the qMRI method that they relate to in the file collections appendix.
Hyperlinks to example applications and reference method articles are encouraged whenever possible.
If it is possible to derive a qMRI application from an already existing file collection suffix by defining a set of logical conditions over the metadata fields, the tables of the deriving the intended qMRI application from an ambiguous file collection and the anatomy data priority levels sections are extended instead of introducing a new suffix.
Application-specific notes for qMRI file collections#
Anatomy imaging data#
General notes:
Some BIDS metadata field values are calculated based on the values of other metadata fields that are not listed as required fields. These fields include:
NumberShots
. The calculation of the values may depend on the type of the acquisition. These acquisitions include:MP2RAGE
andTB1SRGE
.
MP2RAGE
specific notes#
UNIT1
images#
Although the UNIT1
image is provided as an output by the acquisition sequence, it is used
as an input to offline calculation of a T1map
using a dictionary lookup approach. However,
complex
data is needed for an accurate calculation of the UNIT1
image, which is not commonly
provided by the stock sequence. Instead, the magnitude
and phase
images are exported. Please
see the relevant discussion at qMRLab issue #255.
Therefore, the UNIT1
image provided by the scanner is RECOMMENDED to be stored under the anat
raw dataset directory along with the MP2RAGE
file collection and to be used as the primary input
for quantifying a T1map
.
If an additional UNIT1
image is calculated offline, then the output is to be stored in the
derivatives
directory with necessary provenance information.
NumberShots
metadata field#
Note that the type of NumberShots
field can be either a number
or an array of numbers
.
If a single
number
is provided, this should correspond to the number ofSlicesPerSlab
orReconMatrixPE
. However, in this case,SlicePartialFourier
orPartialFourierPE
fraction is needed to calculate the number of partitionsbefore
andafter
of the k-space center to calculate a T1 map.If
before/after
calculation is performed during the BIDS conversion of theMP2RAGE
data, then the value ofNumberShots
metadata field can be given as a 1X2 array, with first entry corresponding tobefore
and the second to theafter
.
Formula:
If NumberShots is an array of numbers such that "NumberShots": [before, after]
,
the values of before
and after
are calculated as follows:
before = SlicesPerSlab*(SlicePartialFourier - 0.5)
after = SlicesPerSlab/2
See this reference implementation.
Other metadata fields#
The value of the RepetitionTimeExcitation
field is not commonly found in the DICOM files.
When accessible, the value of EchoSpacing
corresponds to this metadata.
When not accessible, 2 X EchoTime
can be used as a surrogate.
Further information about other MP2RAGE
qMRI protocol fields can be found in the
qMRLab documentation.
TB1SRGE
specific notes#
Calculation of before
and after
entries for NumberShots
metadata field of TB1SRGE
is more involved than that of MP2RAGE
.
The formula can be found in a
reference implementation,
which requires information about BaseResolution
(that is, image matrix size in PE direction),
partial Fourier fraction in the PE direction, number of reference lines for parallel imaging acceleration,
and the parallel imaging acceleration factor in PE direction.
Radiofrequency (RF) field mapping#
Some RF file collections call for the use of special notations that cannot be resolved by
by entities that can generalize to other applications.
Instead of introducing an entity that is exclusive to a single application,
method developers who commonly use these file collections for the MPM
application reached
the consensus on the use of acq
entity to distinguish individual files.
These suffixes include: TB1AFI
, TB1TFL
, TB1RFM
, and RB1COR
.
TB1EPI
specific notes#
The flip
and echo
entities MUST be used to distinguish images with this suffix.
The use of flip
follows the default convention. However, this suffix defines a
specific use case for the echo
entity:
|
|
---|---|
Lower |
Higher |
Spin Echo (SE) image |
Stimulated Echo (STE) image |
At each FlipAngle
, the TB1EPI
suffix lists two images acquired at two echo times.
The first echo is a spin echo (SE) formed by the pulses alpha-2alpha. However, the
second echo in this method is generated in a different fashion compared to a typical
MESE acquisition. The second echo is a stimulated echo (STE) that is formed by an
additional alpha pulse (that is, alpha-2alpha-alpha).
The FlipAngle
value corresponds to the nominal flip angle value of the STE pulse.
The nominal FA value of the SE pulse is twice this value.
Note that the following metadata fields MUST be defined in the accompanying JSON files:
Field name |
Definition |
---|---|
|
The effective readout length defined as |
|
Time interval between the SE and STE pulses |
To properly identify constituents of this particular method, values of the echo
entity MUST index the images as follows:
TB1AFI
specific notes#
This method calculates a B1+ map from two images acquired at two interleaved excitation repetition times (TR).
Note that there is no entity for the TR that can be used to label the files corresponding to the two
repetition times and the definition of repetition time depends on the modality
(functional
or anatomical
) in the specification.
Therefore, to properly identify constituents of this particular method,
values of the acq
entity SHOULD begin with either tr1
(lower TR) or tr2
(higher TR)
and MAY be followed by freeform entries:
First |
Second |
Use case |
---|---|---|
|
|
Single acquisition |
|
|
Acquisition |
|
|
Acquisition |
TB1TFL
and TB1RFM
specific notes#
These suffixes describe two outputs generated by Siemens tfl_b1_map
and rf_map
product sequences, respectively.
Both sequences output two images.
The first image appears like an anatomical image and the second output is a scaled flip angle map.
To properly identify files of this particular file collection,
values of the acq
entity SHOULD begin with either anat
or famp
and MAY be followed by freeform entries:
Anatomical (like) image |
Scaled flip angle map |
Use case |
---|---|---|
|
|
Single acquisition |
|
|
Acquisition |
|
|
Acquisition |
The example above applies to the TB1RFM
suffix as well.
RB1COR
specific notes#
This method generates a sensitivity map by combining two low resolution images collected by two transmit coils (the body and the head coil) upon subsequent scans with identical acquisition parameters.
To properly identify constituents of this particular method, values of the acq
entity SHOULD begin with either body
or head
and MAY be followed by freeform
entries:
Body coil |
Head coil |
Use case |
---|---|---|
|
|
Single acquisition |
|
|
|
|
|
|
|
|
|