CEST_EVAL_branch code adapted for internal research use from M. Zaiss et al. at https://github.com/cest-sources/CEST_EVAL

The analysis works conventionally on 2-D CEST data. Current updates support 3D CEST data but need to modify the run section step by step.

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• unzip all files in the package
• load the whole folder into the matlab path
• run sections step by step

Analysis pipeline

For a complete CEST evaluation, the general pipeline is the following: 1. process the WASABI series to obtain simultaneous B0- and B1-maps if needed. 2.run B1-correction using multiple B0-corrected Mz series acquired at different B1 values. 3. The corrected Z-spectra are stored in the variable Z_corrExt. 4. performing pixel-wise Multi-Lorentzian fitting of the corrected Z-spectra. This allows us to evaluate separate CEST peaks (amide, amine, etc.) and calculate CEST contrast images using different metrics.

Create a P structure containing parameters for the fitting functions. Parameters are extracted automatically from Simens dicom metadata files, for Philips rec file seperately run batch's Philip section as well; for other vendor run ini section for .ini file loaded or manual input should be needed.

Define an image ROI mask (consisting of 1s and NaNs) called Segment that selects pixels for all following analyses. Create it manually with the make_Segment UI tool, or load a predefined one.

Computing Z-spectra

The Z-spectrum for each pixel is obtained by normalizing the Mz_stack image by M0_stack. This is the most basic operation to visualize and quantify CEST effects.

Z_uncorr = NORM_ZSTACK(Mz_stack, M0_stack, P, Segment);

B0-correction using internal map

(1) Calculate an internal dB0 map. This is given by the x-axis offset of the minimum of the interpolated Z-spectrum from the nominal 0 ppm.

dB0_stack_int = MINFIND_SPLINE_3D(Mz_stack, Segment, P);

(2) Then do pixel-wise B0 correction. This simply centers each pixel’s Z-spectrum by shifting it by the calculated x-offset dB0 map):

Mz_CORR = B0_CORRECTION(Mz_stack, dB0_stack_int, P, Segment);

(3) Finally, compute B0-corrected Z-spectra using NORM_ZSTACK.

Multi-Lorentzian fitting of Z-spectra

To model CEST effects of interest we fit a sum of Lorentzian line shapes to each pixel’s Z-spectrum. The fit is performed pixel-wise by calling FIT_3D with fitting parameters specified in the Pstructure (default is 5-pool model: water, amine, amide, NOE, MT). The function get_FIT_LABREF then calculates Zlab and Zref (used for CEST contrasts).

For pH-weighted Ultravist-CEST, use the function:

[Zlab, Zref, P, popt] = lorentzianfit_main(Z_corrExt, P, Segment, 'invivo');

The ‘invivo’ argument specifies a 6-pool Lorentzian model that includes Ultravist peaks (water, amine, amide, NOE, 5.6ppm, 4.2ppm). For phantom scans, set the argument to ‘ultravist’. This fits a 3-pool model corresponding to Ultravist peaks only (Chen et al., 2014).

Zlab is the full n-pool fit (the 'label' image). Zref is a structure containing reference images for each pool i (i.e. for each pixel, the sum of all Lorentzians excluding pool i).

CEST contrasts calculation

The CEST effect can be quantified using different metrics:

• MTR_asym (not implemented): Magnetization Transfer Ratio asymmetry. It is simply computed as the difference between each Z-value and the value at the symmetrically opposite spectral location.
• MTR_LD: Magnetization Transfer Ratio, where the asymmetry is calculated as linear difference between Zref and Zlab at each frequency offset. This measure is confounded by water saturation spillover effects (‘dilution’ of the Z-spectrum), especially at higher B1 strengths (Zaiss et al., 2014).
• MTR_Rex: Magnetization Transfer Ratio, where the asymmetry is calculated as the difference of the reciprocal terms. Does spillover- and MT-correction.
• AREX (Apparent Exchange-Dependent Relaxation): MTR_Rex normalized by T1 map. Relaxation-compensated measure.

Exporting CEST images

CEST contrast images can be written to DICOM format. However, DICOM stores data in uint16 type. This means that CEST contrast values (encoded as double-precision floating point, typically smaller than 1) will be automatically re-scaled to unsigned integers. To be able to recover the original values, together with the DICOM file we also save a .mat file ('[filename]_ScalingFactorMap') with the pixel-wise scaling factors. This way, if you load into MATLAB a DICOM CEST image (e.g. after ROI analysis in PMOD) you can convert the pixel values back to the original scale and then do statistics and further analyses on them.

Additional acquisitions that are used for field inhomogeneity corrections and for T1 mapping, by Morit Zaiss, Magn Reson Med. 2017 Feb;77(2):571-580.:

• Mz image series at different B1 strengths: e.g. with B1 = 0.5-3.0 muT. At least two such series are needed for B1-inhomogeneity correction with the 'Z-B1-correction' method (see Windschuh et al., 2015). Note that B1 correction also requires a B1 map obtained with a WASABI sequence.
• WASABI: image series for simultaneous B0- ("WAter Shift") and B1- ("BI") mapping. These maps are used for field inhomogeneity correction (see Schuenke et al., 2016).
• T1 mapping sequence: a series of scans with different inversion recovery times for T1 mapping. A T1 map is needed for calculating the relaxation-compensated CEST contrast, AREX (see 'Contrasts' below).