hMRI – A toolbox for quantitative MRI in neuroscience and clinical research

Keywords: Quantitative MRI, In vivo histology, Microstructure, Multi-parameter mapping, Relaxometry, SPM toolbox

Table 1.

Review of studies related to the MPM acquisition protocol using predecessors of the hMRI-toolbox to make inference on myelin (My), iron (Fe), or the volume (Vol) measured by voxel-based morphometry (VBM). Note that all studies used Siemens MRI scanners. Abbreviations: R1=longitudinal relaxation rate, R2(⋆)= (effective) transverse relaxation rate, PD(∗)= (effective) proton density, MT= magnetisation transfer saturation, C = controls, P = patients, dMRI= diffusion MRI, fMRI= functional MRI, MEG= magnetoencephalography, EEG= electroencephalography.

2.1. The MPM protocol

The MPM multi-echo protocol was introduced in Weiskopf and Helms (2008) and Weiskopf et al. (2013) for estimating the longitudinal relaxation rate R1, the effective transverse relaxation rate R2⋆, the proton density PD and the magnetisation transfer MT and generalises a number of acquisition protocols. It typically involves acquiring six to eight images at different echo times (TE) for each of the PD-, T1-and MT-weighted acquisitions in an RF and gradient spoiled gradient echo sequence (referred to as T1w, PDw and MTw echoes, respectively). The hMRI-toolbox can flexibly deal with a large range of site- and study-specific acquisition schemes, from the full MPM protocol to subsets of it, including single contrast echo trains for mapping R2⋆ or variable flip angle data for mapping of R1 and PD using multi-echo or single-echo data, comparable to e.g. DESPOT1 (Deoni et al., 2005). Example MPM acquisition protocols can be found at http://hmri.info.

2.2. Overview theory of MPM signal model

We give here, and in Fig. 1, a short overview of the theory underlying the qMRI map creation process. For a more detailed outline of the theory and the estimation procedures applied in the hMRI-toolbox see Appendix A.

The signal from the multi-echo PDw, T1w and MTw acquisitions can be described by the Ernst equation (Ernst and Anderson, 1966; Helms et al., 2008a, b). The effective transverse relaxation rate R2⋆ can then be derived from the TE dependence of the signal. The unified description of the multi-echo data from all three contrasts into a single model, denoted as ESTATICS (Weiskopf et al., 2014), provides a more robust estimation of R2⋆ with a higher signal-to-noise ratio compared to separate estimations (Fig. 1a). Using approximations of the signal equations for small repetition time TR and small flip angles α, the longitudinal relaxation rate R1, the apparent signal amplitude A* map (proportional to the proton density PD) and the magnetisation transfer MT can be estimated. At this point (Fig. 1b), the generated maps are biased by B1 transmit fT (Fig. 1c) and receive fR (Fig. 1d) field inhomogeneities. The hMRI-toolbox provides correction methods for these bias fields based on specific B1 transmit and receive field measurements or image processing methods. While fT influences the local flip angle and hence all three (R1, PD, MT) maps are affected, the RF sensitivity bias field fR only influences the PD map (in the absence of subject motion).

The toolbox can also handle the situation where only a subset of data is available. For example, R2⋆, R1 and PD can still be estimated when no MTw acquisitions are acquired, R2⋆ alone can be estimated when neither MTw nor T1w acquisitions are available (single multi-echo PDw data). R1, PD and MT saturation maps can be generated from single echo PDw, T1w and MTw images, not requiring multi-echo acquisitions. The theory and map creation tools also encompass the creation of R1 maps from other variable flip angle approaches, such as DESPOT1 (Deoni et al., 2005) or R2⋆ maps from multi-echo data, such as certain susceptibility mapping/weighted imaging approaches.

3.1. Toolbox documentation and installation

The latest version of the toolbox can be downloaded from the hMRI-toolbox page (http://hmri.info) as a zip file (containing the last official release) or by cloning the git repository (https://github.com/hMRI-group/hMRI-toolbox) to keep up-to-date with the latest incremental developments.

Updated documentation is available as a Wiki (https://github.com/hMRI-group/hMRI-toolbox/wiki). It includes installation instructions, an example dataset, a tutorial and a detailed description of the implemented functionalities. Information on releases and versioning, development and contribution guidelines are also provided.

The toolbox has been developed and tested with MATLAB versions 8.0 (R2012b) to 9.3 (R2017b) and SPM12 from version r6906 onwards. Since the hMRI developments will be synchronised with SPM developments, it is recommended to use the most recent SPM release (http://www.fil.ion.ucl.ac.uk/spm/software/spm12/) to benefit from the latest developments.

The hMRI-toolbox is free but copyright software, distributed under the terms of the GNU General Public License as published by the Free Software Foundation (as given in file Copyright.txt). Further details on “copyleft” can be found at http://www.gnu.org/copyleft/.

In particular, the hMRI-toolbox is supplied as is. No formal support or maintenance is provided or implied. Since the toolbox was developed for academic research, it comes with no warranty and is not intended for clinical use.

3.2. MPM example dataset

An MPM example dataset from a healthy subject for demonstrating the hMRI-toolbox features was acquired on a 3T Prisma system (Siemens Healthcare, Erlangen, Germany) at the Wellcome Centre for Human Neuroimaging, London, UK. This dataset can be downloaded from http://hmri.info and is described in detail in Callaghan et al. (submitted).

3.3. Organisation of the toolbox

The hMRI-toolbox is organised into five main modules (Fig. 2): Configure toolbox, DICOM import, Auto-reorient, Create hMRI maps and Process hMRI maps. While the Configure Toolbox and Process hMRI maps modules can be run for a group of subjects, the DICOM Import, Auto-reorient and Create hMRI maps modules must be run for each subject and each session separately (if several datasets acquired per subject, e.g. in a longitudinal study). A brief description of each module is provided below and details can be found in the Appendices.

3.4. Configure toolbox module

The hMRI-toolbox provides the user with a set of default acquisition and processing parameters for most common acquisition protocols without any further customisation. However, customisation is possible and necessary to broaden the toolbox usability to a wider range of protocols, scanners and vendors. Therefore, the Configure toolbox module allows the user to select either standard or customised default parameters, to match the user's own site- or protocol-specific setup and to be used in the subsequent modules (see details in Appendix C.1).

3.5. DICOM import module

DICOM import is a tool to convert DICOM data into NIfTI files. During conversion, the whole DICOM header is stored as JSON-encoded metadata in a file along side the NIfTI images. This feature is implemented in SPM12 from release r7219 (November 2017), following the hMRI-toolbox implementation. Note that Philips-specific rescaling factor (Chenevert et al., 2014) is applied at conversion, starting from version v0.2.0 of the hMRI-toolbox and release r7487 of SPM12. The hMRI-toolbox also provides metadata handling functionalities to retrieve parameter values and store processing parameters in the JSON-encoded metadata file (see Table 3 for an example of processing information stored as metadata). Detailed description of the DICOM import module, the metadata handling tools and BIDS compliance aspects (Gorgolewski et al., 2016) is provided in Appendix B.

3.6. Auto-reorient module

The reorientation of the images towards a standard pose, setting the anterior commissure at the origin and both anterior and posterior commissure (AC/PC) in the same axial plane, as defined in MNI space (Mazziotta et al., 1995, 2001a,b), is a common step that increases the consistency in individual head positions prior to normalisation or segmentation. For example, SPM's segmentation (Ashburner and Friston, 2005) is sensitive to the initial orientation of the images. Therefore, Auto-reorient provides a simple tool for automatically and uniformly reorienting a set of images prior to any further processing including multi-parameter map calculation. Note, that the reorientation modifies the orientation information in each image header, but the reoriented images are not resliced. For more details, see Appendix C.2.

3.7. Create hMRI maps module

The Create hMRI maps module computes quantitative as well as semi-quantitative estimates of R2⋆, R1, PD and MT saturation from unprocessed multi-echo T1w, PDw and MTw spoiled gradient echo acquisitions. The map creation module corrects the qMRI estimates for spatial receive (Section 3.7.3) and transmit (Section 3.7.2) field inhomogeneities.

3.8. Process hMRI maps module

The Process hMRI maps module provides dedicated tools and tissue probability maps for the spatial processing of quantitative MRI maps based on the corresponding SPM framework. The spatial processing pipeline for hMRI data relies on three main operational steps (Fig. 4): (1) segmentation (Ashburner and Friston, 2005; Draganski et al., 2011; Lorio et al., 2016a), (2) non-linear spatial registration into common space (Ashburner (2007)) and (3) tissue-weighted smoothing (Draganski et al. (2011)), using three different sub-modules that are further detailed in the Appendix, Table 4. Furthermore, a fully integrated processing pipeline is provided as an additional sub-module to facilitate standard data processing without the need to combine the individual steps in this module. Details on the three sub-modules and the integrated pipeline are provided in Appendix C.5.

3.9. Statistical analysis

The standard SPM statistical analysis and modelling approaches such as mass-univariate General Linear Modelling can be applied to the spatially processed maps, see, e.g., Draganski et al. (2011) and Freund et al. (2013). Additionally, the multiple parameter maps lend themselves to multi-variate analyses approaches as well (Draganski et al., 2011).

Appendix A.1. R1 and PD estimation

For a single excitation and echo time TE, the signal intensity S is given by the Ernst equation for an ideal spoiled gradient echo acquisition (Ernst and Anderson, 1966; Helms et al., 2008a):

We denote the repetition time by TR and the flip angle by α. A is proportional to the equilibrium magnetisation and thus to the proton density PD (Helms et al., 2008a).

For fast estimation of the R1 and A parameters from (A.1) and a single echo of a T1w and a PDw acquisition, or the mean across multiple echoes for increased SNR, Helms et al. (2008a) introduced a linearisation of its exponential terms for short TR (i.e. R1⋅TR≪1):

leading to, after averaging over echos,

with

When S is the averaged signal over several echoes, the sum in Eq. (A.4) is calculated across all echoes included in the averaged signal. This introduces an R2⋆ bias which increases with the number of echoes and depends on the TE values included in the average. The R2⋆ bias in A⋆ can be evaluated using the R2⋆ estimate and corrected for. Alternatively, S can be the extrapolated signal at TE=0, which is free from R2⋆ weighting. Both the echo averaging with R2⋆ correction method and the TE=0 extrapolation method are implemented and available in the toolbox, and perform similarly (Balteau et al., 2018). The toolbox extrapolates to a TE of 0 ms by default.

Using the approximation sin(α)∼¯α and cos(α)∼¯1−α2/2 for small flip angles and neglecting the term α2⋅R1⋅TR, Eq. (A.1) simplifies to:

from which R1 and A⋆ are estimated using the signals from the PDw and T1w sequences:

where signals ST1 and SPD are either averaged over a number of echoes (Helms et al., 2008a), or extrapolated to TE=0, as explained above. In the first case, a ∑TEe−R2⋆⋅TE correction factor must be calculated to derive A (Balteau et al., 2018). A=A⋆ otherwise meaning that the R2⋆ bias remains.

Appendix A.2. Estimation of the magnetisation transfer

The MT (magnetisation transfer) saturation measure is derived in analogy to a dual-excitation spoiled gradient echo sequence where the second excitation is replaced by the MT pulse. Using a heuristic approximation for small read-out flip angles αMT, the closed form solution for the acquired signal S can be written (Helms and Hagberg, 2009; Helms et al., 2008b) as

where TRMT=TR1+TR2 is the total repetition time, and δ describes the MT saturation (more generally denoted MT in this paper).

From an additional MTw signal SMT we obtain, using again the linearisation of the exponential term for short TRMT (i.e. R1⋅TRMT≪1) and the small angle approximation for the trigonometric functions:

see Helms et al. (2008b). Note that the parameter maps R1, R2⋆, A and MT can alternatively be estimated from the ESTATICS model using analytic formulas and not relying on the linearisation outlined above (Tabelow et al., 2016).

Appendix A.3. The ESTATICS model and R2⋆ estimation

The ESTATICS model was introduced in Weiskopf et al. (2014) for efficiently estimating the effective transverse relaxation rate R2⋆ from an MPM acquisition protocol, where the exponential signal decay with the echo time is identical for T1w, PDw and MTw acquisitions. Specifically, the signal equations (A.1), (A.2), (A.3), (A.4), (A.5), (A.6), (A.7), (A.8) are re-written as

with indicator variables IPD, IT1, and IMT for the differently weighted acquisitions. The signal intensities, e.g. SPD, at TE=0 are given by

with the flip angle and repetition time of the PDw sequence and correspondingly for ST1 and SMT. From Eq. (A.10) R2⋆ can be estimated using all echoes.

Appendix A.4. Corrections for transmit and receive field inhomogeneities

Due to inhomogeneities of the B1 transmit field the local flip angle deviates from its nominal value (Helms et al., 2008a). Using a correction field fT, the effective local flip angle is given by

For details on the correction field fT estimation see Section 3.7.2 and C.3.

For the MTw acquisition we denote by αsat the off-resonance Gaussian shaped RF pulse used for MT weighting. The B1 transmit bias field fT(x) leads to a local correction of αsat, too, and thus to a residual bias field for the estimated δ even though δ is largely independent of the local transmit field bias unlike other MT metrics such as MTR (Helms et al., 2008b; Callaghan et al., 2014). In Helms (2015) the heuristic correction factor for δ was found to be

with B⋅αsat=0.4 for the typically used value αsat=220° in MPM sequences (Weiskopf et al., 2013).

Finally, the multiplicative RF sensitivity bias field fR (see Section 3.7.3 and Appendix C.4) for the correction of the signal amplitude maps A:

has to be measured or estimated from the data (for details see Section 3.7.3). The A⋆ maps can then be normalised to proton density PD maps by calibration. To this end, the mean PD value in white matter is assumed to be 69 percent units (p.u.) (Filo and Mezer, 2018).

Appendix A.5. Spoiling corrections

Further, imperfect spoiling requires an additional correction when estimating R1 (Preibisch and Deichmann, 2009), to account for deviation from Ernst equation (A.1). The correction is performed using the following expression

where Pa(fT) and Pb(fT) are quadratic functions in fT. Their coefficients depend on the specific sequence and can be determined by simulation (Preibisch and Deichmann, 2009). Further refinements of this correction include diffusion effects on signal spoiling (Yarnykh, 2007; Lutti and Weiskopf, 2013; Callaghan et al., 2015c).

Appendix B.1. DICOM to NIfTI conversion

The spm_dicom_convert script has been modified to achieve the above goals. An additional input argument has been set to deal with the new JSON metadata options. If JSON metadata storage is enabled, DICOM header information is stored as a JSON-formatted structure either as a separate JSON file or as extended NIfTI header (or both). If omitted or disabled, the DICOM to NIfTI conversion proceeds using exactly the same implementation as in the previously mentioned SPM12 distribution. The output NIfTI images, extended or not, have a valid NIfTI-1 format compatible with standard NIfTI readers (MRICron, SPM, FreeSurfer). Note that FSL up to version v5.0.9 does not support extended headers. For that reason, the extended header option, although suggested in (Gorgolewski et al., 2016) is currently not recommended and disabled by default. Instead, a separate JSON file is created.

Appendix B.2. DICOM to NIfTI conversion – SPM Batch.

The DICOM to NIfTI conversion with the above features is available through the hMRI-toolbox in the SPM Batch GUI (SPM > Tools > hMRI Tools > DICOM Import). The new JSON metadata format field provides the user with the following options:

• •

  None

• •

  Separate JSON file (default)

• •

  Extended nii header

• •

  Both

Appendix B.3. The metadata structure.

Metadata are simple MATLAB structures, hence quite flexible and modular. The structure of the metadata is divided into the following two main fields:

• •

  Acquisition parameters (hdr.acqpar): this branch contains the original DICOM header. It should be kept unchanged and is created when importing DICOM images to NIfTI. It is dropped for processed images relying on several input images.

• •

  History (hdr.history): is a nested structure containing

  • –

    procstep: a structure describing the current processing step and containing:

    • ∗

      descrip: a brief description of the processing applied (e.g. DICOM to NIfTI conversion, realign, create map)

    • ∗

      version: version number of the processing applied, for traceability

    • ∗

      procpar: processing parameters (relevant parameters used to process the data – e.g. for SPM processing, all the settings included in the MATLAB batch job except for the input images.

  • –

    input. An array listing the input images used for the processing. Each input (hdr.history.input(i)) is a structure containing:

    • ∗

      filename: the filename of the input image

    • ∗

      history: the history structure from the input image

  • –

    output: a structure containing

    • ∗

      imtype: image type of the current output (e.g. R1, FA, MT, ADC) including a summary description of the processing steps (see TableB 3).

    • ∗

      units: either physical units (e.g. sec, 1/sec), standardised units (e.g. percent units = p.u.) or arbitrary units (a.u.).

Appendix B.4. Metadata handling

The JSON transcription of the MATLAB structure is easily readable and searchable by opening the extended NIfTI or separate JSON file with a text editor. Furthermore, metadata set, get and search tools have been implemented. These tools have been added into the spm12/metadata directory. Typing help <function> in MATLAB gives detailed syntax. Below is a list of the most useful scripts and their summary descriptions. For a complete description of the implemented tools, please refer to the hMRI-toolbox Wiki pages.

get_metadata: returns the MATLAB structure described above.

set_metadata: to write (insert, modify, overwrite) metadata in JSON files and extended NIfTI headers. In general, metadata are initialised during the DICOM to NIfTI conversion and further modified as the processing progresses.

get_metadata_val: returns pairs of structure fields and values agreeing with search criteria.

find_field_name: recursively searches a MATLAB structure for a specific field name (can be applied to any MATLAB structure).

Appendix B.5. General usage of the metadata and BIDS compatibility

The DICOM import with metadata storage is available in the SPM distribution since release r7219 (November 2017) allowing for a wide use of such metadata. It provides most of the parameters required for map creation and processing. The hMRI-toolbox also provides metadata handling functionalities to retrieve parameter values and store processing parameters in the JSON-encoded metadata file. The format used for the metadata provides flexibility for using a wide range of protocols, and traceability of every parameter used to generate the results (see for example Table 3).

Outside the hMRI-toolbox, metadata and metadata handling tools implemented in the hMRI-toolbox can prove useful for many purposes. For quality control, metadata can help checking for protocol consistency by comparing essential parameters across sessions, subjects and sites. When processing data acquired with several protocols on several scanners from different vendors (multi-centre studies), metadata provide the information to automatically extract acquisition parameters to be used as regressors of no interest in a statistical analysis (e.g. MR scanner, field strength, head coil, …). Metadata also facilitate the sorting of the data between different data types (e.g. functional MRI, structural MRI, diffusion images, etc.) together with retrieving BIDS essential parameters (e.g. repetition time, echo time, flip angle, session and series numbers, etc.), therefore providing all the information required to make the data archiving and storage fully BIDS-compatible.

A BIDS extension proposal (BEP) is currently under discussion, that will define the parameters necessary to describe structural acquisitions with multiple contrasts (see https://bids.neuroimaging.io/for updates on BIDS specifications and extension proposals), and bring the metadata structure of such data in line with the BIDS recommendations (Gorgolewski et al., 2016). The extension will include all acquisition parameters required to fully describe and process data acquired with the MPM protocols, in a standard, platform- and vendor-independent way. With such BIDS extension, the hMRI-toolbox will be adapted and made fully BIDS-compliant, from the structure of the JSON-encoded metadata to the structure of the output data. The required adaptation will be achieved smoothly thanks to the metadata handling functionalities, without affecting the processing part of the hMRI-toolbox, and enabling the implementation of fully automated tools retrieving the BIDS-organised data, generating the maps and applying spatial processing.

Appendix C.1. Configure toolbox module

The Configure toolbox module must be run before any other module for the default parameters to take effect.

The default settings are defined and described in the config directory. The default files therein should never be modified. Template files for customised default parameters are stored in the config/local directory and can be modified and saved with meaningful names by the user. Note that most parameters should only be modified by advanced users who are aware of the underlying theory and implementation of the toolbox.

For brevity, a full description of each customisable setting is not provided here. A detailed description of each parameter may be found in the hmri_local_defaults.m help and examples are provided in the toolbox Wiki pages. Note that the default settings for B1 transmit bias correction (also including acquisition and processing parameters) can be customised separately within the Create hMRI maps module (Appendix C.3). Their customisation must follow the same guidelines as provided in this section.

While settings need to be defined either in the standard or customised default files, acquisition parameters can be retrieved from the input images if included in the metadata. When no metadata are available or not yet properly handled by the toolbox (e.g. customised sequences), acquisition parameters specified in the default files are used as a fallback solution. The use of metadata is strongly recommended (see Appendix B), to automatically retrieve acquisition parameters and to avoid incorrect processing.

Appendix C.2. Auto-reorient module

This module performs a rigid-body alignment of a subject's structural image into the MNI space, applying the estimated pose to a set of other images. The code makes use of the spm_affreg function and canonical images available in SPM12. The user must provide one structural image from the subject and a corresponding canonical image for co-registration, and a series of other images that need to be kept in alignment with the structural image (typically, all the images acquired during a given imaging session). The structural image should have good signal-to-noise ratio with a high white matter/grey matter contrast-to-noise to ensure a robust and reliable co-registration. The SPM canonical images can be selected from the SPM/canonical directory or any other user-defined template (e.g. atypical population-driven template), provided it is already oriented according to MNI. Since the registration is based on matching similar intensities, it is important that the contrast of the reference image be close to that of the canonical image. The following list is provided as guidelines:

If no specific output directory is selected, the original orientation of the images will be overwritten. If an output directory is selected then the images are copied there before being reoriented, therefore leaving the original data untouched. The reoriented images can be collected as dependencies for further processing in the batch interface, either all at once (Dependencies > Grouped) or as individual output images (Dependencies > Individual), which is more convenient to connect the Auto-reorient module to the next Create hMRI maps module.

Appendix C.3. B₁ transmit field corrections

Within the Create hMRI maps module, several methods for B1 transmit field correction are implemented. Depending on the choice of the specific method the GUI requires the user to provide adequate input files, which are described below. In order to proceed to the B1 map calculation, number of acquisition parameters must also be either retrieved from the metadata or provided to the toolbox via a standard or customised default B1 file (see below and in Appendix C.1).

• 1.

  3D_EPI

EPI spin-echo (SE)/stimulated-echo (STE) method (Lutti et al., 2009, 2012).

The sequence uses a set of pulses with nominal flip angles α, 2⋅α and α to create pairs of spin echo and stimulated echo images. All consecutive pairs of SE/STE images corresponding to the different nominal flip angle values α of the SE/STE RF pulse must be loaded as B1 input. B0 field mapping images must also be provided for correcting distortions in the EPI images. Both magnitude images and the pre-subtracted phase image must be selected, in that order, as B0 input.

• 2.

  3D_AFI

Actual Flip Angle Imaging (AFI) method (Yarnykh, 2007).

A pair of magnitude images acquired with two different repetition times (TR) must be loaded as B1 input. The hMRI-toolbox then calculates the B1 transmit bias field map as described in Yarnykh (2007).

• 3.

  tfl_b1_map

TFL B1 mapping (Chung et al., 2010).

For this method, the batch interface requests a pair of images (one anatomical image and one flip angle map, in that order) from a service sequence by Siemens (version available from VE11 on) based on a turbo flash (TFL) sequence with and without a pre-saturation pulse (Chung et al., 2010). The flip angle map used as input contains the measured flip angle multiplied by 10. After rescaling (p.u.) and smoothing, the output fT map is ready to be used for B1 transmit bias correction.

• 4.

  Rf_map

This requires a pair of images (one anatomical image and one pre-processed B1 map, in that order) from a service sequence by Siemens (Erlangen, Germany). These are based on the acquisition of a spin-echo/stimulated echo as used for method 3D_EPI above (Lutti et al., 2009, 2012). Rescaling and smoothing is applied to the input B1 map (a.u.) to generate a fT map (p.u.) suitable for B1 transmit bias correction.

• 5.

  Pre-processed B1

Any B1 transmit bias field map pre-calculated using one of the above methods or another method can be used as pre-processed B1 input. The user must select one anatomical reference (for co-registration) and one B1 map (in p.u.), in that order. The anatomical reference is typically a magnitude image from the B1 mapping dataset, and must be in the same space as the B1 transmit bias field map.

• 6.

  UNICORT

Data driven estimation of the B1 transmit bias field map using the UNICORT approach (Weiskopf et al., 2011).

If none of the above mentioned methods applies because no appropriate B1 transmit bias field mapping data were acquired, the UNICORT option is recommended to remove the transmit field bias in the R1 map and estimate the B1 transmit bias field map from the qMRI data. Optimal UNICORT processing parameters are RF transmit coil dependant. The default parameters have been optimised for Siemens 3T TIM Trio scanners and may need specific optimisation for other RF transmit coils. See the hMRI-toolbox Wiki for guidelines.

• 7.

  No_B1_correction

With this option, no correction for transmit inhomogeneities is applied, i.e. fT≡1.

For each B1 mapping protocol (1–4), one *_B1map (in p.u.) and one *_B1ref (for anatomical reference) file is created and saved in the Results/Supplementary directory (see Table 2). The output images are associated with JSON metadata including the type of B1 map processed and the setting used. For UNICORT, only the *_B1map in p.u. Is created (in the same space as the qMRI maps) with no anatomical reference. For pre-processed B1 transmit bias field maps provided by the user, the input files are not renamed to match the naming convention above. Default acquisition and processing parameters for the UNICORT, 3D_EPI and 3D_AFI methods can be customised at this point by providing the Create hMRI maps module with a customised configuration file (see Appendix C.1 and the hMRI Wiki pages for guidelines). Except for UNICORT, where the B1 transmit bias field is directly derived from the qMRI data, and the no_B1_correction option, where no anatomical reference is given, the hMRI-toolbox co-registers the anatomical reference image with the multi-echo spoiled gradient echo data before applying the B1 transmit bias correction.

Appendix C.4. Receive (RF) sensitivity bias correction

In this paragraph, the RF sensitivity bias correction methods based on measured RF sensitivity maps (Single or Per contrast options) as well as the data driven method (Unified Segmentation option: no input sensitivity map required) will be explained in detail.

Receive field sensitivity measurements. Since the receive field sensitivity of the body coil is assumed to be flat over the spatial extent of the head (Pruessmann et al., 1999), if the same anatomy is imaged with the head coil and the body coil sequentially, using the same acquisition parameters and assuming no motion, then the ratio of these two scans is proportional to the net head coil receive sensitivity field fR:

where SHC is the signal acquired with the head coil, fR is the receive sensitivity field of the head coil, SBC is the same signal acquired by the body coil, CBC is the receive sensitivity field of the body coil assumed to be approximately constant, and S0 is the signal specific to the underlying anatomy and acquisition parameters.

If receive field sensitivity measurements are available for each imaging contrast (one receive sensitivity field acquired either before or after each of the PDw, T1w and MTw contrasts), inter-scan variation of the sensitivity modulation can be accounted for and used to optimally correct for the combined inter-scan motion and sensitivity modulation effects in the quantitative maps (Papp et al. (2016) and Fig. 5a). Therefore, the implemented RF sensitivity correction method (Per contrast option) combines a correction for motion-related relative receive sensitivity variations with rigid-body realignment.

The low resolution measurements from the head and body coils (in this order) for each T1w, PDw and MTw acquisition are expected as inputs to the RF sensitivity bias correction. A receive field sensitivity map is calculated according to Eqs. (A.1), (C.1), (C.2).2 for each contrast, and applied to the corresponding contrast to correct for any coil sensitivity driven signal intensity modulation.

If RF sensitivity measurements are missing for some of the contrasts, or if only one single measurement is available, the same RF sensitivity bias correction can still be applied to all contrasts by choosing the corresponding (Single) option in the GUI. Only one pair of head coil and body coil images is required. Receiver field inhomogeneities will be corrected for although not accounting for inter-scan motion (Fig. 5b–d). Note that the measured RF sensitivity maps are modulated by the receive field of the body coil, which is not corrected for (Fig. 5e).

Data driven receive field estimation. If no receive field sensitivity map has been acquired, the Unified Segmentation option can be selected in the GUI. The receive field bias is then estimated from the calculated A map using SPM unified segmentation (US) approach (Ashburner and Friston, 2005). Accurate bias field estimation requires the use of a brain mask combining the white and grey matter probability maps. The latter are derived by segmentation of the calculated MT map (because of its higher contrast in the basal ganglia, see Helms et al. (2009)) or alternatively (if no MTw images are available), by segmentation of the calculated R1 map. The optimal US parameters depend on the RF receive coil and its sensitivity profile. Fine adjustment of the US parameters might be required for a specific RF receive coil.

Appendix C.5. Process hMRI maps module

This section describes the three main operational steps of the spatial processing pipeline for hMRI data: segmentation, diffeomorphic registration and tissue-weighted smoothing. Furthermore, it provides details on the fully integrated processing pipeline, which is proposed in this toolbox as a separate sub-module to facilitate standard spatial data processing without the need to combine the individual steps.

The processing tools and user interface can be used to process parameter maps generated from a series of subjects, assuming that each subject has the same small number of images (for example the four MT saturation, PD, R1, and R2⋆ maps). Therefore each module takes as input several series of images sorted per image type (for example the MT from all the subjects). The images in each series must follow the same subject-based ordering.

1. Acosta-Cabronero J., Callaghan M.F. ESMRMB 2017. 2017. Quantitative susceptibility mapping from variable flip angle measurements. [Google Scholar]
2. Acosta-Cabronero J., Milovic C., Mattern H., Tejos C., Speck O., Callaghan M.F. A robust multi-scale approach to quantitative susceptibility mapping. NeuroImage. 2018;183:7–24. doi: 10.1016/j.neuroimage.2018.07.065. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Alonso-Ortiz E., Levesque I.R., Pike G.B. Impact of magnetic susceptibility anisotropy at 3T and 7T on T2*-based myelin water fraction imaging. Neuroimage. 2018;182:370–378. doi: 10.1016/j.neuroimage.2017.09.040. [DOI] [PubMed] [Google Scholar]
4. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38(1):95–113. doi: 10.1016/j.neuroimage.2007.07.007. [DOI] [PubMed] [Google Scholar]
5. Ashburner J., Friston K.J. Voxel-based morphometry–the methods. Neuroimage. 2000;11(6 Pt 1):805–821. doi: 10.1006/nimg.2000.0582. [DOI] [PubMed] [Google Scholar]
6. Ashburner J., Friston K.J. Unified segmentation. Neuroimage. 2005;26(3):839–851. doi: 10.1016/j.neuroimage.2005.02.018. [DOI] [PubMed] [Google Scholar]
7. Assaf Y., Basser P.J. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27(1):48–58. doi: 10.1016/j.neuroimage.2005.03.042. [DOI] [PubMed] [Google Scholar]
8. Balteau E., Leutritz T., Weiskopf N., Reimer E., Lutti A., Callaghan M.F., Mohammadi S., Tabelow K. Proceedings of ISMRM 2018. 2018. Evaluating T2* bias impact and correction strategies in quantitative proton density mapping; p. 2694. [Google Scholar]
9. Battiston M., Cercignani M. Ch.10. CRC Press; 2018. MT: magentisation transfer; pp. 161–184. (Quantitative MRI of the Brain: Principles of Physical Measurement). [Google Scholar]
10. Baudrexel S., Reitz S.C., Hof S., Gracien R.-M., Fleischer V., Zimmermann H., Droby A., Klein J.C., Deichmann R. Quantitative T1 and proton density mapping with direct calculation of radiofrequency coil transmit and receive profiles from two-point variable flip angle data. NMR Biomed. 2016;29(3):349–360. doi: 10.1002/nbm.3460. [DOI] [PubMed] [Google Scholar]
11. Bernstein M.A., King K.F., Zhou X.J. Academic Press; 2004. Handbook of MRI Pulse Sequences. [Google Scholar]
12. Büchel C., Raedler T., Sommer M., Sach M., Weiller C., Koch M.A. White matter asymmetry in the human brain: a diffusion tensor MRI study. Cerebr. Cortex. 2004;14(9):945–951. doi: 10.1093/cercor/bhh055. [DOI] [PubMed] [Google Scholar]
13. Cabana J.-F., Gu Y., Boudreau M., Levesque I.R., Atchia Y., Sled J.G., Narayanan S., Arnold D.L., Pike G.B., Cohen-Adad J. Quantitative magnetization transfer imaging made easy with qMTLab: software for data simulation, analysis, and visualization. Concepts Magn. Reson. Part A Bridg. Educ. Res. 2015;44A(5):263–277. [Google Scholar]
14. Callaghan M.F., Dick F., Grabher P., Keller T., Freund P., Weiskopf N. Proceedings of ISMRM 2016. 2016. Optimisation of post-processing correction of transmit field inhomogeneity in R1 maps by relaxometry modelling. [Google Scholar]
15. Callaghan M.F., Freund P., Draganski B., Anderson E., Cappelletti M., Chowdhury R., Diedrichsen J., Fitzgerald T.H.B., Smittenaar P., Helms G., Lutti A., Weiskopf N. Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiol. Aging. 2014;35(8):1862–1872. doi: 10.1016/j.neurobiolaging.2014.02.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Callaghan M.F., Helms G., Lutti A., Mohammadi S., Weiskopf N. A general linear relaxometry model of R1 using imaging data. Magn. Reson. Med. 2015;73(3):1309–1314. doi: 10.1002/mrm.25210. [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Callaghan M.F., Josephs O., Herbst M., Zaitsev M., Todd N., Weiskopf N. An evaluation of prospective motion correction (PMC) for high resolution quantitative MRI. Front. Neurosci. 2015;9:97. doi: 10.3389/fnins.2015.00097. [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Callaghan, M.F., Lutti, A., Ashburner, J., Balteau, E., Corbin, N., Draganski, B., Helms, G., Kherif, F., Leutritz, T., Mohammadi, S., Phillips, C., Reimer, E., Ruthotto, L., Seif, M., Tabelow, K., Ziegler, G., Weiskopf, N., submitted. Example dataset for the hMRI toolbox. Data In Brief. [DOI] [PMC free article] [PubMed]
19. Callaghan M.F., Malik S.J., Weiskopf N. Proceedings of ISMRM 2015. 2015. Rapid calculation of correction parameters to compensate for imperfect RF spoiling in quantitative R1 mapping. [Google Scholar]
20. Callaghan M.F., Mohammadi S., Weiskopf N. Synthetic quantitative MRI through relaxometry modelling. NMR Biomed. 2016;29(12):1729–1738. doi: 10.1002/nbm.3658. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Canna A., Ponticorvo S., Russo A.G., Manara R., Di Salle F., Saponiero R., Callaghan M.F., Weiskopf N., Esposito F. A group-level comparison of volumetric and combined volumetric-surface normalization for whole brain analyses of myelin and iron maps. Magn. Reson. Imaging. Dec 2018;54:225–240. doi: 10.1016/j.mri.2018.08.021. [DOI] [PubMed] [Google Scholar]
22. Carey D., Krishnan S., Callaghan M.F., Sereno M.I., Dick F. Functional and quantitative MRI mapping of somatomotor representations of human supralaryngeal vocal tract. Cerebr. Cortex. 2017;27(1):265–278. doi: 10.1093/cercor/bhw393. [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Carey D., Caprini F., Allen M., Lutti A., Weiskopf N., Rees G., Callaghan M.F., Dick F. Quantitative MRI provides markers of intra-, inter-regional, and age-related differences in young adult cortical microstructure. Neuroimage. 2018;182:429–440. doi: 10.1016/j.neuroimage.2017.11.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Castella R., Arn L., Dupuis E., Callaghan M.F., Draganski B., Lutti A. Controlling motion artefact levels in MR images by suspending data acquisition during periods of head motion. Magn. Reson. Med. 2018;80(6):2415–2426. doi: 10.1002/mrm.27214. [DOI] [PubMed] [Google Scholar]
25. Cercignani M., Dowell N.G., Tofts P.S., editors. Quantitative MRI of the Brain: Principles of Physical Measurement. second ed. Series in Medical Physics and Biomedical Engineering. CRC Press; 2018. [Google Scholar]
26. Chenevert T.L., Malyarenko D.I., Newitt D., Li X., Jayatilake M., Tudorica A., Fedorov A., Kikinis R., Liu T.T., Muzi M., Oborski M.J., Laymon C.M., Li X., Thomas Y., Jayashree K.-C., Mountz J.M., Kinahan P.E., Rubin D.L., Fennessy F., Huang W., Hylton N., Ross B.D. Errors in quantitative image analysis due to platform-dependent image scaling. Transl. Oncol. 2014;7(1):65–71. doi: 10.1593/tlo.13811. [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Chung S., Kim D., Breton E., Axel L. Rapid B1+ mapping using a preconditioning RF pulse with TurboFLASH readout. Magn. Reson. Med. 2010;64(2):439–446. doi: 10.1002/mrm.22423. [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Dale A.M., Sereno M.I. Improved localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cognit. Neurosci. 1993;5(2):162–176. doi: 10.1162/jocn.1993.5.2.162. [DOI] [PubMed] [Google Scholar]
29. Davatzikos C., Bryan N. Using a deformable surface model to obtain a shape representation of the cortex. IEEE Trans. Med. Imag. 1996;15(6):785–795. doi: 10.1109/42.544496. [DOI] [PubMed] [Google Scholar]
30. Deichmann R., Schwarzbauer C., Turner R. Optimisation of the 3D MDEFT sequence for anatomical brain imaging: technical implications at 1.5 and 3 T. Neuroimage. 2004;21(2):757–767. doi: 10.1016/j.neuroimage.2003.09.062. [DOI] [PubMed] [Google Scholar]
31. Deoni S.C. High-resolution T1 mapping of the brain at 3T with driven equilibrium single pulse observation of T1 with high-speed incorporation of RF field inhomogeneities (DESPOT1-HIFI) J. Magn. Reson. Imag. 2007;26(4):1106–1111. doi: 10.1002/jmri.21130. [DOI] [PubMed] [Google Scholar]
32. Deoni S.C., Williams S.C., Jezzard P., Suckling J., Murphy D.G., Jones D.K. Standardized structural magnetic resonance imaging in multicentre studies using quantitative T1 and T2 imaging at 1.5 T. Neuroimage. 2008;40(2):662–671. doi: 10.1016/j.neuroimage.2007.11.052. [DOI] [PubMed] [Google Scholar]
33. Deoni S.C.L., Peters T.M., Rutt B.K. High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn. Reson. Med. 2005;53(1):237–241. doi: 10.1002/mrm.20314. [DOI] [PubMed] [Google Scholar]
34. Dick F., Tierney A.T., Lutti A., Josephs O., Sereno M.I., Weiskopf N. In vivo functional and myeloarchitectonic mapping of human primary auditory areas. J. Neurosci. 2012;32(46):16095–16105. doi: 10.1523/JNEUROSCI.1712-12.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Dick F.K., Lehet M.I., Callaghan M.F., Keller T.A., Sereno M.I., Holt L.L., Nov Extensive tonotopic mapping across auditory cortex is recapitulated by spectrally directed attention and systematically related to cortical myeloarchitecture. J. Neurosci. 2017;37(50):12187–12201. doi: 10.1523/JNEUROSCI.1436-17.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Does M.D. Inferring brain tissue composition and microstructure via MR relaxometry. NeuroImage. 2018;182:136–148. doi: 10.1016/j.neuroimage.2017.12.087. [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Donahue K.M., Burstein D., Manning W.J., Gray M.L. Studies of Gd-DTPA relaxivity and proton exchange rates in tissue. Magn. Reson. Med. 1994;32(1):66–76. doi: 10.1002/mrm.1910320110. [DOI] [PubMed] [Google Scholar]
38. Draganski B., Ashburner J., Hutton C., Kherif F., Frackowiak R., Helms G., Weiskopf N. Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ) Neuroimage. 2011;55(4):1423–1434. doi: 10.1016/j.neuroimage.2011.01.052. [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Drury H.A., Van Essen D.C., Anderson C.H., Lee C.W., Coogan T.A., Lewis J.W. Computerized mappings of the cerebral cortex: a multiresolution flattening method and a surface-based coordinate system. J. Cognit. Neurosci. 1996;8(1):1–28. doi: 10.1162/jocn.1996.8.1.1. [DOI] [PubMed] [Google Scholar]
40. Edwards L.J., Kirilina E., Mohammadi S., Weiskopf N. Microstructural imaging of human neocortex in vivo. NeuroImage. 2018;182:184–206. doi: 10.1016/j.neuroimage.2018.02.055. [DOI] [PubMed] [Google Scholar]
41. Ellerbrock I., Mohammadi S. Four in vivo g-ratio-weighted imaging methods: comparability and repeatability at the group level. Hum. Brain Mapp. 2018;39(1):24–41. doi: 10.1002/hbm.23858. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Ernst R.R., Anderson W.A. Application of Fourier transform spectroscopy to magnetic resonance. Rev. Sci. Instrum. 1966;37:93–102. [Google Scholar]
43. Fatouros P.P., Marmarou A. Use of magnetic resonance imaging for in vivo measurements of water content in human brain: method and normal values. J. Neurosurg. 1999;90(1):109–115. doi: 10.3171/jns.1999.90.1.0109. [DOI] [PubMed] [Google Scholar]
44. Fatouros P.P., Marmarou A., Kraft K.A., Inao S., Schwarz F.P. In vivo brain water determination by T1 measurements: effect of total water content, hydration fraction, and field strength. Magn. Reson. Med. 1991;17(2):402–413. doi: 10.1002/mrm.1910170212. [DOI] [PubMed] [Google Scholar]
45. Filo S., Mezer A.A. Ch. 4. CRC Press; 2018. PD: proton density of tissue water; pp. 55–72. (Quantitative MRI of the Brain: Principles of Physical Measurement). [Google Scholar]
46. Fischl B., Dale A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U.S.A. 2000;97(20):11050–11055. doi: 10.1073/pnas.200033797. [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Fischl B., Sereno M.I., Tootell R.B., Dale A.M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 1999;8(4):272–284. doi: 10.1002/(SICI)1097-0193(1999)8:4&#x0003c;272::AID-HBM10&#x0003e;3.0.CO;2-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Focke N.K., Helms G., Kaspar S., Diederich C., Tóth V., Dechent P., Mohr A., Paulus W. Multi-site voxel-based morphometry–not quite there yet. Neuroimage. 2011;56(3):1164–1170. doi: 10.1016/j.neuroimage.2011.02.029. [DOI] [PubMed] [Google Scholar]
49. Freund P., Weiskopf N., Ashburner J., Wolf K., Sutter R., Altmann D.R., Friston K., Thompson A., Curt A. MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: a prospective longitudinal study. Lancet Neurol. 2013;12(9):873–881. doi: 10.1016/S1474-4422(13)70146-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Fullerton G.D., Potter J.L., Dornbluth N.C. NMR relaxation of protons in tissues and other macromolecular water solutions. Magn. Reson. Imaging. 1982;1(4):209–226. doi: 10.1016/0730-725x(82)90172-2. [DOI] [PubMed] [Google Scholar]
51. Gelman N., Ewing J.R., Gorell J.M., Spickler E.M., Solomon E.G. Interregional variation of longitudinal relaxation rates in human brain at 3.0 T: relation to estimated iron and water contents. Magn. Reson. Med. 2001;45(1):71–79. doi: 10.1002/1522-2594(200101)45:1<71::aid-mrm1011>3.0.co;2-2. [DOI] [PubMed] [Google Scholar]
52. Gorgolewski K.J., Auer T., Calhoun V.D., Craddock R.C., Das S., Duff E.P., Flandin G., Ghosh S.S., Glatard T., Halchenko Y.O., Handwerker D.A., Hanke M., Keator D., Li X., Michael Z., Maumet C., Nichols B.N., Nichols T.E., Pellman J., Poline J.-B., Rokem A., Schaefer G., Sochat V., Triplett W., Turner J.A., Varoquaux G., Poldrack R.A. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data. 2016;3:160044. doi: 10.1038/sdata.2016.44. [DOI] [PMC free article] [PubMed] [Google Scholar]
53. Helbling S., Teki S., Callaghan M.F., Sedley W., Mohammadi S., Griffiths T.D., Weiskopf N., Barnes G.R. Structure predicts function: combining non-invasive electrophysiology with in-vivo histology. Neuroimage. 2015;108:377–385. doi: 10.1016/j.neuroimage.2014.12.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
54. Helms G. Proceedings of ISMRM. 2015. Correction for residual effects of B1+ inhomogeniety on MT saturation in FLASH-based multi-parameter mapping of the brain; p. 2015. [Google Scholar]
55. Helms G., Dathe H., Dechent P. Quantitative FLASH MRI at 3T using a rational approximation of the Ernst equation. Magn. Reson. Med. 2008;59:667–672. doi: 10.1002/mrm.21542. [DOI] [PubMed] [Google Scholar]
56. Helms G., Dathe H., Dechent P. Modeling the influence of TR and excitation flip angle on the magnetization transfer ratio (MTR) in human brain obtained from 3D spoiled gradient echo MRI. Magn. Reson. Med. 2010;64(1):177–185. doi: 10.1002/mrm.22379. [DOI] [PubMed] [Google Scholar]
57. Helms G., Dathe H., Kallenberg K., Dechent P. High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI. Magn. Reson. Med. 2008;60(6):1396–1407. doi: 10.1002/mrm.21732. [DOI] [PubMed] [Google Scholar]
58. Helms G., Dathe H., Weiskopf N., Dechent P. Identification of signal bias in the variable flip angle method by linear display of the algebraic ernst equation. Magn. Reson. Med. 2011;66(3):669–677. doi: 10.1002/mrm.22849. [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Helms G., Draganski B., Frackowiak R., Ashburner J., Weiskopf N. Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps. Neuroimage. 2009;47(1):194–198. doi: 10.1016/j.neuroimage.2009.03.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Helms G., Hagberg G.E. In vivo quantification of the bound pool T1 in human white matter using the binary spin–bath model of progressive magnetization transfer saturation. Phys. Med. Biol. 2009;54(23):N529–N540. doi: 10.1088/0031-9155/54/23/N01. [DOI] [PubMed] [Google Scholar]
61. Henkelman R.M., Stanisz G.J., Graham S.J. Magnetization transfer in MRI: a review. NMR Biomed. 2001;14(2):57–64. doi: 10.1002/nbm.683. [DOI] [PubMed] [Google Scholar]
62. Heule R., Ganter C., Bieri O., May Variable flip angle T1 mapping in the human brain with reduced s t2 ensitivity using fast radiofrequency-spoiled gradient echo imaging. Magn. Reson. Med. 2015;75(4):1413–1422. doi: 10.1002/mrm.25668. [DOI] [PubMed] [Google Scholar]
63. Hoult D.I. The principle of reciprocity in signal strength calculations - a mathematical guide. Concepts Magn. Reson. Part A Bridg. Educ. Res. 2000;12(4):173–187. [Google Scholar]
64. Kamman R.L., Go K.G., Brouwer W., Berendsen H.J. Nuclear magnetic resonance relaxation in experimental brain edema: effects of water concentration, protein concentration, and temperature. Magn. Reson. Med. 1988;6(3):265–274. doi: 10.1002/mrm.1910060304. [DOI] [PubMed] [Google Scholar]
65. Kaneoke Y., Furuse M., Inao S., Saso K., Yoshida K., Motegi Y., Mizuno M., Izawa A. Spin-lattice relaxation times of bound water–its determination and implications for tissue discrimination. Magn. Reson. Imaging. 1987;5(6):415–420. doi: 10.1016/0730-725x(87)90375-4. [DOI] [PubMed] [Google Scholar]
66. Klein A., Ghosh S.S., Avants B., Yeo B.T.T., Fischl B., Ardekani B., Gee J.C., Mann J.J., Parsey R.V. Evaluation of volume-based and surface-based brain image registration methods. Neuroimage. 2010;51(1):214–220. doi: 10.1016/j.neuroimage.2010.01.091. [DOI] [PMC free article] [PubMed] [Google Scholar]
67. Koenig S.H., Brown R.D., Ugolini R. A unified view of relaxation in protein solutions and tissue, including hydration and magnetization transfer. Magn. Reson. Med. 1993;29(1):77–83. doi: 10.1002/mrm.1910290114. [DOI] [PubMed] [Google Scholar]
68. Lambert C., Lutti A., Helms G., Frackowiak R., Ashburner J. Multiparametric brainstem segmentation using a modified multivariate mixture of Gaussians. Neuroimage: Clinica. 2013;2:684–694. doi: 10.1016/j.nicl.2013.04.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
69. Lee J., Nam Y., Choi J.Y., Kim E.Y., Oh S.-H., Kim D.-H. Mechanisms of T2* anisotropy and gradient echo myelin water imaging. NMR Biomed. 2016;30(4):e3513. doi: 10.1002/nbm.3513. [DOI] [PubMed] [Google Scholar]
70. Lee J.E., Chung M.K., Lazar M., DuBray M.B., Kim J., Bigler E.D., Lainhart J.E., Alexander A.L. A study of diffusion tensor imaging by tissue-specific, smoothing-compensated voxel-based analysis. Neuroimage. 2009;44(3):870–883. doi: 10.1016/j.neuroimage.2008.09.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
71. Lee Y., Callaghan M.F., Acosta-Cabronero J., Lutti A., Nagy Z. Establishing intra- and inter-vendor reproducibility of T1 relaxation time measurements with 3T MRI. Magn. Reson. Med. 2018;81(1):454–465. doi: 10.1002/mrm.27421. [DOI] [PubMed] [Google Scholar]
72. Lee Y., Callaghan M.F., Nagy Z. Analysis of the precision of variable flip angle T1 mapping with emphasis on the noise propagated from RF transmit field maps. Front. Neurosci. 2017;11:106. doi: 10.3389/fnins.2017.00106. [DOI] [PMC free article] [PubMed] [Google Scholar]
73. Liberman G., Louzoun Y., Ben Bashat D. T1 mapping using variable flip angle SPGR data with flip angle correction. J. Magn. Reson. Imag. 2013;40(1):171–180. doi: 10.1002/jmri.24373. [DOI] [PubMed] [Google Scholar]
74. Lorio S., Fresard S., Adaszewski S., Kherif F., Chowdhury R., Frackowiak R., Ashburner J., Helms G., Weiskopf N., Lutti A., Draganski B. New tissue priors for improved automated classification of subcortical brain structures on MRI. Neuroimage. 2016;130:157–166. doi: 10.1016/j.neuroimage.2016.01.062. [DOI] [PMC free article] [PubMed] [Google Scholar]
75. Lorio S., Kherif F., Ruef A., Melie-Garcia L., Frackowiak R., Ashburner J., Helms G., Lutti A., Draganski B. Neurobiological origin of spurious brain morphological changes: a quantitative MRI study. Hum. Brain Mapp. 2016;37(5):1801–1815. doi: 10.1002/hbm.23137. [DOI] [PMC free article] [PubMed] [Google Scholar]
76. Lorio S., Lutti A., Kherif F., Ruef A., Dukart J., Chowdhury R., Frackowiak R., Ashburner J., Helms G., Weiskopf N., Draganski B. Disentangling in vivo the effects of iron content and atrophy on the ageing human brain. Neuroimage. 2014;103:280–289. doi: 10.1016/j.neuroimage.2014.09.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
77. Lorio S., Tierney T.M., McDowell A., Arthurs O.J., Lutti A., Weiskopf N., Carmichael D.W. Flexible proton density (PD) mapping using multi-contrast variable flip angle (VFA) data. Neuroimage. 2018;186:464–475. doi: 10.1016/j.neuroimage.2018.11.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
78. Lutti A., Dick F., Sereno M.I., Weiskopf N. Using high-resolution quantitative mapping of R1 as an index of cortical myelination. Neuroimage. 2014;93:176–188. doi: 10.1016/j.neuroimage.2013.06.005. [DOI] [PubMed] [Google Scholar]
79. Lutti A., Hutton C., Finsterbusch J., Helms G., Weiskopf N. Optimization and validation of methods for mapping of the radiofrequency transmit field at 3T. Magn. Reson. Med. 2010;64(1):229–238. doi: 10.1002/mrm.22421. [DOI] [PMC free article] [PubMed] [Google Scholar]
80. Lutti A., Hutton C., Weiskopf N. Proceedings of ISMRM 2009. 2009. Optimization of 3D EPI technique for radio frequency (B1) field mapping at 3T; p. 2797. [Google Scholar]
81. Lutti A., Stadler J., Josephs O., Windischberger C., Speck O., Bernarding J., Hutton C., Weiskopf N. Robust and fast whole brain mapping of the RF transmit field B1 at 7T. PLoS One. 2012;7(3):1–7. doi: 10.1371/journal.pone.0032379. [DOI] [PMC free article] [PubMed] [Google Scholar]
82. Lutti A., Weiskopf N. Proceedings of ISMRM 2013. 2013. Optimizing the accuracy of T1 mapping accounting for RF non-linearities and spoiling characteristics in FLASH imaging. [Google Scholar]
83. Marques J.P., Khabipova D., Gruetter R. Studying cyto and myeloarchitecture of the human cortex at ultra-high field with quantitative imaging: R1, R2* and magnetic susceptibility. Neuroimage. 2017;147:152–163. doi: 10.1016/j.neuroimage.2016.12.009. [DOI] [PubMed] [Google Scholar]
84. Mazziotta J., Toga A., Evans A., Fox P., Lancaster J., Zilles K., Woods R., Paus T., Simpson G., Pike B. A four-dimensional probabilistic atlas of the human brain. J. Am. Med. Inf. Assoc. 2001;8(5):401–430. doi: 10.1136/jamia.2001.0080401. [DOI] [PMC free article] [PubMed] [Google Scholar]
85. Mazziotta J., Toga A., Evans A., Fox P., Lancaster J., Zilles K., Woods R., Paus T., Simpson G., Pike B. A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM) Philos. Trans. R. Soc. B Biol. Sci. 2001;356(1412):1293–1322. doi: 10.1098/rstb.2001.0915. [DOI] [PMC free article] [PubMed] [Google Scholar]
86. Mazziotta J.C., Toga A.W., Evans A., Fox P., Lancaster J. A probabilistic atlas of the human brain: theory and rationale for its development: the international consortium for brain mapping (ICBM) Neuroimage. 1995;2(2, Part A):89–101. doi: 10.1006/nimg.1995.1012. [DOI] [PubMed] [Google Scholar]
87. Mezer A., Rokem A., Berman S., Hastie T., Wandell B.A. Evaluating quantitative proton-density-mapping methods. Hum. Brain Mapp. 2016;37(10):3623–3635. doi: 10.1002/hbm.23264. [DOI] [PMC free article] [PubMed] [Google Scholar]
88. Mohammadi S., Callaghan M.F. In: Cercignani, editor. vol. 2018. 2018. pp. 303–324. (Quantitative MRI of the Brain: Principles of Physical Measurement, Ch. Image Analysis). [Google Scholar]
89. Mohammadi S., Carey D., Dick F., Diedrichsen J., Sereno M.I., Reisert M., Callaghan M.F., Weiskopf N. Whole-brain in-vivo measurements of the axonal g-ratio in a group of 37 healthy volunteers. Front. Neurosci. 2015;9:441. doi: 10.3389/fnins.2015.00441. [DOI] [PMC free article] [PubMed] [Google Scholar]
90. Mohammadi S., Keller S.S., Glauche V., Kugel H., Jansen A., Hutton C., Flöel A., Deppe M. The influence of spatial registration on detection of cerebral asymmetries using voxel-based statistics of fractional anisotropy images and tbss. PLoS One. 2012;7(6) doi: 10.1371/journal.pone.0036851. [DOI] [PMC free article] [PubMed] [Google Scholar]
91. Mohammadi S., Tabelow K., Ruthotto L., Feiweier T., Polzehl J., Weiskopf N. High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing. Front. Neurosci. 2014;8:427. doi: 10.3389/fnins.2014.00427. [DOI] [PMC free article] [PubMed] [Google Scholar]
92. Mugler J.P., Brookeman J.R. Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE) Magn. Reson. Med. 1990;15(1):152–157. doi: 10.1002/mrm.1910150117. [DOI] [PubMed] [Google Scholar]
93. Oh S.-H., Kim Y.-B., Cho Z.-H., Lee J. Origin of B0 orientation dependent R2* (=1/T2*) in white matter. Neuroimage. 2013;73:71–79. doi: 10.1016/j.neuroimage.2013.01.051. [DOI] [PMC free article] [PubMed] [Google Scholar]
94. Papp D., Callaghan M.F., Meyer H., Buckley C., Weiskopf N. Correction of inter-scan motion artifacts in quantitative R1 mapping by accounting for receive coil sensitivity effects. Magn. Reson. Med. 2016;76(5):1478–1485. doi: 10.1002/mrm.26058. [DOI] [PMC free article] [PubMed] [Google Scholar]
95. Pohmann R., Scheffler K. A theoretical and experimental comparison of different techniques for B1mapping at very high fields. NMR Biomed. 2013;26(3):265–275. doi: 10.1002/nbm.2844. [DOI] [PubMed] [Google Scholar]
96. Polzehl J., Tabelow K. Low SNR in diffusion MRI models. J. Am. Stat. Assoc. 2016;111(516):1480–1490. [Google Scholar]
97. Preibisch C., Deichmann R. Influence of RF spoiling on the stability and accuracy of T1 mapping based on spoiled FLASH with varying flip angles. Magn. Reson. Med. 2009;61(1):125–135. doi: 10.1002/mrm.21776. [DOI] [PubMed] [Google Scholar]
98. Pruessmann K.P., Weiger M., Scheidegger M.B., Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 1999;42(5):952–962. [PubMed] [Google Scholar]
99. Reisert M., Mader I., Umarova R., Maier S., Tebartz van Elst L., Kiselev V.G. Fiber density estimation from single q-shell diffusion imaging by tensor divergence. Neuroimage. 2013;77:166–176. doi: 10.1016/j.neuroimage.2013.03.032. [DOI] [PubMed] [Google Scholar]
100. Reuter M., Tisdall M.D., Qureshi A., Buckner R.L., van der Kouwe A.J., Fischl B. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage. 2015;107:107–115. doi: 10.1016/j.neuroimage.2014.12.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
101. Ridgway G.R., Henley S.M.D., Rohrer J.D., Scahill R.I., Warren J.D., Fox N.C. Ten simple rules for reporting voxel-based morphometry studies. Neuroimage. 2008;40(4):1429–1435. doi: 10.1016/j.neuroimage.2008.01.003. [DOI] [PubMed] [Google Scholar]
102. Rosen A.F., Roalf D.R., Ruparel K., Blake J., Seelaus K., Villa L.P., Ciric R., Cook P.A., Davatzikos C., Elliott M.A., de La Garza A.G., Gennatas E.D., Quarmley M., Schmitt J.E., Shinohara R.T., Tisdall M.D., Craddock R.C., Gur R.E., Gur R.C., Satterthwaite T.D. Quantitative assessment of structural image quality. Neuroimage. 2018;169:407–418. doi: 10.1016/j.neuroimage.2017.12.059. [DOI] [PMC free article] [PubMed] [Google Scholar]
103. Ruthotto L., Mohammadi S., Heck C., Modersitzki J., Weiskopf N. Hyperelastic susceptibility artifact correction of DTI in SPM. In: Meinzer H.-P., Deserno T.M., Handels H., Tolxdorff T., editors. Bildverarbeitung für die Medizin 2013. Springer Berlin Heidelberg; Berlin, Heidelberg: 2013. pp. 344–349. [Google Scholar]
104. Schabel M.C., Morrell G.R. Uncertainty in T1 mapping using the variable flip angle method with two flip angles. Phys. Med. Biol. 2008;54(1):N1–N8. doi: 10.1088/0031-9155/54/1/N01. [DOI] [PubMed] [Google Scholar]
105. Seif M., Leutritz T., Samson R.S., Curt A., Wheeler-Kingshott C.A.G., Freund P., Weiskopf N. Proceedings of ISMRM 2018. 2018. A multi-center study on fast full-brain quantitative multi-parameter mapping of R1, MT, and R2*: scan-rescan repeatability and inter-site reproducibility; p. 1199. [Google Scholar]
106. Sereno M.I., Lutti A., Weiskopf N., Dick F. Mapping the human cortical surface by combining quantitative T1 with retinotopy. Cerebr. Cortex. 2013;23(9):2261–2268. doi: 10.1093/cercor/bhs213. [DOI] [PMC free article] [PubMed] [Google Scholar]
107. Shuter B., Wang S.C., Roche J., Briggs G., Pope J.M. Relaxivity of Gd-EOB-DTPA in the normal and biliary obstructed Guinea pig. J. Magn. Reson. Imag. 1998;8(4):853–861. doi: 10.1002/jmri.1880080415. [DOI] [PubMed] [Google Scholar]
108. Smith S.M., Jenkinson M., Johansen-Berg H., Rueckert D., Nichols T.E., Mackay C.E., Watkins K.E., Ciccarelli O., Cader M.Z., Matthews P.M., Behrens T.E.J. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31(4):1487–1505. doi: 10.1016/j.neuroimage.2006.02.024. [DOI] [PubMed] [Google Scholar]
109. Soares J.M., Marques P., Alves V., Sousa N. A hitchhiker's guide to diffusion tensor imaging. Front. Neurosci. 2013;7:31. doi: 10.3389/fnins.2013.00031. [DOI] [PMC free article] [PubMed] [Google Scholar]
110. Sotiropoulos S.N., Jbabdi S., Xu J., Andersson J.L., Moeller S., Auerbach E.J., Glasser M.F., Hernandez M., Sapiro G., Jenkinson M., Feinberg D.A., Yacoub E., Lenglet C., Van Essen D.C., Ugurbil K., Behrens T.E.J., WU-Minn HCP Consortium Advances in diffusion MRI acquisition and processing in the human connectome project. Neuroimage. 2013;80:125–143. doi: 10.1016/j.neuroimage.2013.05.057. [DOI] [PMC free article] [PubMed] [Google Scholar]
111. Steiger T.K., Weiskopf N., Bunzeck N. Iron level and myelin content in the ventral striatum predict memory performance in the aging brain. J. Neurosci. 2016;36(12):3552–3558. doi: 10.1523/JNEUROSCI.3617-15.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
112. Stikov N., Campbell J.S.W., Stroh T., Lavelée M., Frey S., Novek J., Nuara S., Ho M.-K., Bedell B.J., Dougherty R.F., Leppert I.R., Boudreau M., Narayanan S., Duval T., Cohen-Adad J., Picard P.-A., Gasecka A., Côté D., Pike G.B. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage. 2015;118:397–405. doi: 10.1016/j.neuroimage.2015.05.023. [DOI] [PubMed] [Google Scholar]
113. Stikov N., Perry L.M., Mezer A., Rykhlevskaia E., Wandell B.A., Pauly J.M., Dougherty R.F. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. Neuroimage. 2011;54(2):1112–1121. doi: 10.1016/j.neuroimage.2010.08.068. [DOI] [PMC free article] [PubMed] [Google Scholar]
114. Stüber C., Morawski M., Schäfer A., Labadie C., Wähnert M., Leuze C., Streicher M., Barapatre N., Reimann K., Geyer S., Spemann D., Turner R. Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. Neuroimage. 2014;93(Pt 1):95–106. doi: 10.1016/j.neuroimage.2014.02.026. [DOI] [PubMed] [Google Scholar]
115. Tabelow K., D'Alonzo C., Polzehl J., F.Callaghan M., Ruthotto L., Weiskopf N., Mohammadi S. Proceedings of HBM 2016. 2016. How to achieve very high resolution quantitative MRI at 3T. [Google Scholar]
116. Tabelow K., D'Alonzo C., Ruthotto L., Callaghan M.F., Weiskopf N., Polzehl J., Mohammadi S. Proceedings of ISMRM 2017. 2017. Removing the estimation bias due to the noise floor in multi-parameter maps. [Google Scholar]
117. Tabelow K., Mohammadi S., Weiskopf N., Polzehl J. POAS4SPM: a toolbox for SPM to denoise diffusion MRI data. Neuroinformatics. 2015;13(1):19–29. doi: 10.1007/s12021-014-9228-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
118. Thompson P., Toga A.W. A surface-based technique for warping three-dimensional images of the brain. IEEE Trans. Med. Imag. 1996;15(4):402–417. doi: 10.1109/42.511745. [DOI] [PubMed] [Google Scholar]
119. Trampel R., Bazin P.-L., Pine K., Weiskopf N. In-vivo magnetic resonance imaging (MRI) of laminae in the human cortex. Neuroimage. 2017 doi: 10.1016/j.neuroimage.2017.09.037. [DOI] [PubMed] [Google Scholar]
120. Volz S., Nöth U., Jurcoane A., Ziemann U., Hattingen E., Deichmann R. Quantitative proton density mapping: correcting the receiver sensitivity bias via pseudo proton densities. Neuroimage. 2012;63(1):540–552. doi: 10.1016/j.neuroimage.2012.06.076. [DOI] [PubMed] [Google Scholar]
121. Volz S., Nöth U., Rotarska-Jagiela A., Deichmann R. A fast B1-mapping method for the correction and normalization of magnetization transfer ratio maps at 3T. Neuroimage. 2010;49(4):3015–3026. doi: 10.1016/j.neuroimage.2009.11.054. [DOI] [PubMed] [Google Scholar]
122. Wang H.Z., Riederer S.J., Lee J.N. Optimizing the precision in T1 relaxation estimation using limited flip angles. Magn. Reson. Med. 1987;5(5):399–416. doi: 10.1002/mrm.1910050502. [DOI] [PubMed] [Google Scholar]
123. Weiskopf N., Callaghan M.F., Josephs O., Lutti A., Mohammadi S. Estimating the apparent transverse relaxation time (R2(*)) from images with different contrasts (ESTATICS) reduces motion artifacts. Front. Neurosci. 2014;8:278. doi: 10.3389/fnins.2014.00278. [DOI] [PMC free article] [PubMed] [Google Scholar]
124. Weiskopf N., Helms G. Proceedings of ISMRM 2008. 2008. Multi-parameter mapping of the human brain at 1mm resolution in less than 20 minutes; p. 2241. [Google Scholar]
125. Weiskopf N., Lutti A., Helms G., Novak M., Ashburner J., Hutton C. Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT) Neuroimage. 2011;54(3):2116–2124. doi: 10.1016/j.neuroimage.2010.10.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
126. Weiskopf N., Mohammadi S., Lutti A., Callaghan M.F. Advances in MRI-based computational neuroanatomy. Curr. Opin. Neurol. 2015;28(4):313–322. doi: 10.1097/WCO.0000000000000222. [DOI] [PubMed] [Google Scholar]
127. Weiskopf N., Suckling J., Williams G., Correia M.M., Inkster B., Tait R., Ooi C., Bullmore E.T., Lutti A. Quantitative multi-parameter mapping of R1, PD(*), MT, and R2(*) at 3T: a multi-center validation. Front. Neurosci. 2013;7:95. doi: 10.3389/fnins.2013.00095. [DOI] [PMC free article] [PubMed] [Google Scholar]
128. Wharton S., Bowtell R. Fiber orientation-dependent white matter contrast in gradient echo MRI. Proc. Natl. Acad. Sci. U.S.A. 2012;109(45):18559–18564. doi: 10.1073/pnas.1211075109. [DOI] [PMC free article] [PubMed] [Google Scholar]
129. Whitaker K.J., Vértes P.E., Romero-Garcia R., Váša F., Moutoussis M., Prabhu G., Weiskopf N., Callaghan M.F., Wagstyl K., Rittman T. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc. Natl. Acad. Sci. U.S.A. 2016;113(32):9105–9110. doi: 10.1073/pnas.1601745113. [DOI] [PMC free article] [PubMed] [Google Scholar]
130. Wood T. QUIT: QUantitative imaging tools. J. Open Source Softw. 2018;3(26):656. [Google Scholar]
131. Yablonskiy D.A. Quantitation of intrinsic magnetic susceptibility-related effects in a tissue matrix. phantom study. Magn. Reson. Med. 1998;39(3):417–428. doi: 10.1002/mrm.1910390312. [DOI] [PubMed] [Google Scholar]
132. Yarnykh V.L. Actual flip-angle imaging in the pulsed steady state: a method for rapid three-dimensional mapping of the transmitted radiofrequency field. Magn. Reson. Med. 2007;57(1):192–200. doi: 10.1002/mrm.21120. [DOI] [PubMed] [Google Scholar]
133. Yarnykh V.L. Optimal radiofrequency and gradient spoiling for improved accuracy of T1 and B1 measurements using fast steady-state techniques. Magn. Reson. Med. 2010;63(6):1610–1626. doi: 10.1002/mrm.22394. [DOI] [PubMed] [Google Scholar]
134. Yendiki A., Panneck P., Srinivasan P., Stevens A., Zöllei L., Augustinack J., Wang R., Salat D., Ehrlich S., Behrens T., Jbabdi S., Gollub R., Fischl B. Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front. Neuroinf. 2011;5:23. doi: 10.3389/fninf.2011.00023. [DOI] [PMC free article] [PubMed] [Google Scholar]
135. Ziegler G., Grabher P., Thompson A., Altmann D., Hupp M., Ashburner J., Friston K., Weiskopf N., Curt A., Freund P. Progressive neurodegeneration following spinal cord injury. Neurology. 2018;90(14):e1257–e1266. doi: 10.1212/WNL.0000000000005258. [DOI] [PMC free article] [PubMed] [Google Scholar]