This example aims to provide an overview of Bayesian model-based analysis for CEST  using the QuantiCEST widget  available as part of Quantiphyse . Here, we work with a preclinical ischaemic stroke dataset using continuous wave CEST , however the following analysis pipeline should be applicable to both pulsed and continuous wave sequences acquired over a full Z-spectrum.
Before we do any data modelling, this is a quick orientation guide to Quantiphyse if you’ve not used it before. You can skip this section if you already know how the program works.
Loading some CEST Data¶
If you are taking part in an organized practical workshop, the data required may be available in your home
directory, in the
course_data/CEST_PRECLINICAL folder. If not, an encrypted zipfile containing the data can be
downloaded below - you will be given the password by the course organizers:
Files can be loaded in NIFTI or DICOM format either by dragging and dropping in to the view pane, or by clicking
Load Data. When loading a file you should indicate if it is data or an ROI by clicking the
appropriate button when the load dialog appears. Load the following data file:
The data should appear in the viewing window.
If your single slice CEST NIFTI file is in 3D format rather than 4D, you may need to select
when loading data and
Treat as 2D multi-volume.
The left part of the window normally contains three orthogonal views of your data. In this case the data is a 2D slice so Quantiphyse has maximised the relevant viewing window. If you double click on the view it returns to the standard of three orthogonal views - this can be used with 3D data to look at just one of the slice windows at a time.
- Left mouse click to select a point of focus using the crosshairs
- Left mouse click and drag to pan the view
- Right mouse click and drag to zoom
- Mouse wheel to move through the slices
- Double click to ‘maximise’ a view, or to return to the triple view from the maximised view.
The right hand side of the window contains ‘widgets’ - tools for analysing and processing data. Three are visible at startup:
Volumesprovides an overview of the data sets you have loaded
Data statisticsdisplays summary statistics for data set
Voxel analysisdisplays timeseries and overlay data at the point of focus
Select a widget by clicking on its tab, just to the right of the image viewer.
More widgets can be found in the
Widgets menu at the top of the window. The tutorial
will tell you when you need to open a new widget.
For a slightly more detailed introduction, see the Getting Started section of the User Guide.
For clinical data, we recommend brain extraction is performed as a preliminary step using FSL’s BET tool , with the
–m option set to create a binary mask. You can also do this from within Quantiphyse using the FSL integration
plugin. It is strongly recommended to include a brain ROI as this will decrease processing time considerably.
In this case we have preclinical data for which BET is not optimised, so we have prepared the brain mask in advance in the following file:
Load this data set via the
File menu, and his time select
ROI as the data type. Once loaded, it will show up in the
dropdown under the viewing pane, and will also be visible as a red shaded region on the CEST data:
When viewing the output of modelling, it may be clearer if the ROI is displayed as an outline rather than a shaded
region. To do this, select
Contour from the
View options below the ROI selector:
If you accidentally load an ROI data set as
Data, you can set it to be an ROI using the
(visible by default). Just click on the data set in the list and click the
Toggle ROI button.
If you prefer you can skip this step - motion correction does not improve this data significantly.
Motion correction can be implemented using FSL’s MCFLIRT tool within Quantiphyse, or beforehand using FSL. To run
within Quantiphyse, select
To run motion correction on the data, you need to:
- Set the registration mode to
- Ensure the method is set to
- Select the reference volume as
- For CEST data, you probably want the motion correction reference to be an unsaturated image, so we have set
Index of reference volumeto 0 to select the first image in the CEST sequence.
- Set the output name to
The resulting setup should look like this:
Run to run the motion correction. The output in this case is not much different to the input as there
was not much motion in this data, however if you switch between
CEST_moco in the
selector (below the image view) you may be able to see slight differences.
Voxel Analysis widget which is visible by default to the right of the viewing window. By
clicking on different voxels in the image the Z-spectra can be displayed:
Bayesian Model-based Analysis¶
To do CEST model analysis, select the QuantiCEST tool from the menu:
Widgets -> CEST -> QuantiCEST. The widget
should look something like this:
Data and sequence section¶
To begin with, make sure the
CEST data set is selected as the CEST data, and the
ROI is selected as the ROI.
The B0 field strength can be selected as 3T for clinical and 9.4T for preclinical studies. This selection
varies the pool defaults. If you choose
Custom as the field strength as well as specifying
the value you will need to adjust the pool defaults (see below).
In this case the acquisition parameters do not need altering, however in general you will need to specify the B1 field strength, saturation method and saturation time for your specific setup.
Next we will specify the frequency offsets of your acquisition - this is a set of frequences whose length
must match the number of volumes in the CEST data. You can enter them manually, or if they are stored in
a text file (e.g. with one value per row) you can click the
Load button and choose the file.
For this tutorial we have provided the frequency offsets in the
Frequency_offsets.txt, so click
Load, select this file and verify that the values are as follows:
In general, a minimum of three pools should be included in model-based analysis. We provide some of the most common
pools to include, along with literature values for frequency offset, exchange rate, and T1 and T2 values for the
field strengths of 3T and 9.4T. The data for the pools we have selected can be displayed by clicking the
You can also use this dialog box to change the values, for example if you are using a custom field strength. The
Add button can also be used if you want to use a pool that isn’t one of the ones provided.
In the analysis section we have the option of allowing the T1/T2 values to vary. We will enable this, but provide T1 and T2 maps to guide the modelling. These maps are stored in the following files:
Load both of these files into Quantiphyse using
File->Load Data as before. Now select the
T1 map and
checkboxes, and select the appropriate data sets from the dropdown menus. The result should look like this:
By default, CESTR* maps will be output, with the added option to output individual parameter maps, as well as fitted curves. As shown above, we have set both of these options, so that fitted data can be properly interrogated.
Running model-based analysis¶
Run button is used to start the analysis. The output data will be loaded into Quantiphyse but if you would
also like to save it in a file, you can select the
Save copy of output data checkbox and choose a folder
to save it in.
Visualising Processed Data¶
If you re-select the
Voxel analysis widget which we used at the start to look at the CEST signal in the
input data, you can see the model prediction overlaid onto the data. By clicking on different voxels you
can get an idea of how well the model has fitted your data.
For each non-water pool included in the model there will be a corresponding CESTR* map output (here amide and a macromolecular pool), and these values will be summarised for each voxel underneath the timeseries data.
Here we are most interested in the behaviour of the Amide pool; cest_rstar_Amide. In this preclinical example,
there is an ischemic region on the right hand side of the brain. If we select
cest_rstar_Amide from the
overlay selector (below the viewing window), a reduced CESTR* is just about visible.
We can extract quantitative metrics for this using regions of interest (ROIs). Before doing this it can
help to apply some smoothing to the data. From the menu select
Widgets->Processing->Smoothing and set
the options to smooth
cest_rstar_Amide with a smoothing kernel size of 0.4mm:
The output of this smoothing appears as follows:
The ischaemic region is a little more visible in this section (to the left of the image, i.e. the right side of the brain).
Extracting quantitative Metrics¶
We have prepared an ROI for the ischaemic region in the file:
Load this file using
File->Load Data, selecting it as an ROI.
Now open the
Data Statistics widget which is visible by default above the
Voxel Analysis widget. We
can now select statistics on
cest_rstar_Amide within this ROI (click on
Summary statistics to view):
Note that it is possible to display statistics from more than one data set, however here we are just going to look at the CESTR* for the Amide pool.
To compare with the non-ischemic portion, we will now draw a contralateral ROI. To do this, open the
Widgets->ROIs->ROI Builder and select the
Ischemic_mask ROI for editing:
The default label of 1 has been used to label the ischemic core, so type
ischemic in the
Label description box.
Now enter a new label number (e.g. 2) and change the default name from
Region 2 to
To manually draw a contralateral ROI, use either the pen tool to draw freehand around a region on the opposite
side of the brain, or use one of the other tools to select a suitable region - for example you could draw it
as an ellipse using the tool. After drawing a region, click
Add to add it to the ROI. It should appear
in a different colour as it is a different label. Here is an example (the new contralateral region is yellow):
Now go back to the
Data Statistics widget where we can compare the CESTR* in the two regions we have defined.
As expected, CESTR* of the amide pool is lower for the ischemic tissue than for healthy tissue.
The minimum outputs from running model-based analysis are the model-fitted z-spectra, and CESTR* maps for non-water pools, as defined in your model setup. If the Parameter Maps option is highlighted then for each pool, including water, there will be additional maps of proton concentration and exchange rate (from which CESTR* is calculated), as well as frequency offset (ppm). For water, the offset map represents the correction for any field inhomogeneities.
Allow uncertainty in T1/T2 values is set then fitted maps of T1 and T2 will be available for each pool.
Naming conventions follow the order the pools are defined in the QuantiCEST setup panel.
Viewing data without the water baseline¶
Rather than doing a full model-based analysis as described in section Bayesian model-based analysis, QuantiCEST also
has the option simply remove the water baseline from the raw data, allowing you to directly view or quantify the
smaller non-water peaks in the acquired CEST volume. Baseline removal is done using the Lorentzian Difference
Analysis (LDA) option in QuantiCEST - this is available by selecting the alternative tab in the box containing
LDA works by fitting a subset of the raw CEST data (within ±1ppm, and beyond ±30ppm) to a water pool,
and then subtracting this model fit from the data. This leaves behind the smaller non-water
peaks in the data, called a Lorentzian Difference spectrum. QuantiCEST outputs this as
This can be viewed in the
Voxel Analysis widget alongside the data signal and the model-based fit:
Running QuantiCEST from the command line¶
Here we have covered basic model-based analysis of CEST data using the interactive GUI. If you have multiple data sets it may be desirable to automate this analysis so that the same processing steps can be run on several data sets from the command line, without interactive use.
Although this is beyond the scope of this tutorial, it can be set up relatively simply. The batch processing options for the analysis you have set up can be displayed by clicing on the following button at the top of the QuantiCEST widget . For more information see documentation for Batch processing.
|||Chappell et al., Quantitative Bayesian model‐based analysis of amide proton transfer MRI, Magnetic Resonance in Medicine, 70(2), (2013).|
|||Croal et al., QuantiCEST: Bayesian model-based analysis of CEST MRI. 27th Annual Meeting of International Society for Magnetic Resonance in Medicine, #2851 (2018).|
|||Ray et al., Investigation into the origin of the APT MRI signal in ischemic stroke. Proc. Int. Soc. Magn. Reson. Med. 25 (2017).|
|||S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, 2002.|