Pulseq-CEST: Towards multi-site multi-vendor compatibility and reproducibility of CEST experiments using an open-source sequence standard

Abstract

Purpose:

As the field of CEST is growing, various novel preparation periods using different parameters are being introduced. At the same time, large, multi-site clinical studies require clearly defined protocols, especially across different vendors. Here, we propose a CEST definition standard using the open Pulseq format for a shareable, simple and exact definition of CEST protocols.

Methods:

We present the benefits of such a standard in three ways: (I) an open database on GitHub, where fully defined, human-readable CEST protocols can be shared; (II) an open-source Bloch-McConnell simulation to test and optimize CEST preparation periods in silico and (III) a hybrid MR sequence that plays out the CEST preparation period and can be combined with any existing readout module.

Results:

The exact definition of the CEST preparation period, in combination with the flexible simulation leads to a good match between simulations and measurements. The standard allowed finding consensus on three APTw protocols that could be compared in healthy subjects and a tumor patient. In addition, we could show coherent multi-site results for a sophisticated CEST method, highlighting the benefits regarding protocol sharing and reproducibility.

Conclusion:

With Pulseq-CEST, we provide a straightforward approach to standardize, share, simulate and measure different CEST preparation schemes, which are inherently completely defined.

1. Introduction

Chemical exchange saturation transfer (CEST) MRI employs the exchange transfer of magnetization from solutes to water to increase the sensitivity of their detection through a saturation effect on the water signal^1–3. CEST employs a molecular amplification mechanism that accumulates its effect on the spin system during a saturation period (T_sat), consisting of one or more RF pulses with or without interpulse delays, followed by an imaging sequence. Detectable CEST effects in vivo have been reported for instance for proteins^4,5, glutamate,⁶ and different sugars^7–9. The choice of a specific saturation period is crucial for an optimal CEST experiment, as the maximum effect depends not only on the tissue and solute pool of interest, but also on the efficiency of the saturation imposed by the RF pulse scheme¹⁰ and of its transfer during T_sat^11,12 as well as on concomitant saturation effects, such as direct saturation and magnetization transfer contrast associated with the semi-solid pool^13,14. Moreover, differences in approaches for data analysis in terms of normalization or spectral regions considered can further affect the final image contrast calculated from Z-spectra¹⁵.

Thus, the saturation period has to be precisely defined by such parameters as RF pulse shape (both magnitude and phase), pulse duration (t_p), saturation duty cycle (DC_sat), total saturation time (T_sat), saturation field strength (B₁) and offset from the water resonance frequency (Δω). However, these parameters vary significantly in the current literature¹⁶, and are not always provided in sufficient detail. In addition, the literature uses different definitions or terminology to describe saturation “power”, e.g. the flip angle of the pulse¹⁷, pulse peak B₁ amplitude¹⁸, B₁ root mean square (B_1,rms) amplitude¹⁹ or continuous wave power equivalent (B_1cwpe)²⁰, potentially leading to confusion when implementing a comparable CEST MRI experiment. It is therefore not always possible to faithfully reproduce a CEST experiment without corresponding with the authors and even then, the method could still be prone to errors. Thus, a common, easy-to-use format for researchers to provide and share the precise saturation parameters is desirable, especially regarding the current focus on reproducibility in MR research²¹. Moreover, a growing number of deep learning-based evaluation approaches for large multi-site multi-vendor data sets make a proper definition of input data even more important²².

In terms of MR imaging, a vendor-independent, human-readable and sharable file format for sequences has been introduced with the Pulseq framework²³. In Pulseq, all sequence parameters are defined in a text-file (in the following termed as pulseq-file), which can be created with various popular programming applications such as MATLAB (The MathWorks, Inc., Natick, MA, USA) or python²⁴. This pulseq-file is then read and played out on the scanner via a vendor-specific interpreter sequence. While Pulseq is a great tool that enables a flexible implementation of complex sequence patterns²⁵, it is complicated to incorporate vendor provided image reconstruction functions, which are generally proprietary. However, having the source code of a full interpreter sequence at hand, a capsulated interpreter can be included into other existing sequences for imaging readout. For example, a 3D snapshot GRE^26,27 can be equipped with an encapsulated Pulseq interpreter that solely plays out a CEST preparation block defined in a pulseq-file. This makes the Pulseq file format a perfect candidate for sharing CEST preparation periods. The established MRI readout following the saturation period can be used with the familiar user interface and all possibilities of adjustments and image reconstruction. This procedure enables four major advantages:

1. The CEST preparation period definition in Pulseq is complete. It is defined in a human-readable text file, which is easy to interpret, and allows direct comparison of different protocols. Thus, exchanging and comparing such files allows total reproducibility.

2. The definition of the RF pulses can be done in MATLAB or python instead of implementing it in the sequence using a vendor-specific language (often C++), which, depending on the vendor, can be time consuming and can require the compilation of a new sequence library.

3. The CEST preparation period can be used directly in simulations in the same framework (e.g. MATLAB or python), eliminating possible sources of error from transferring simulation results to the sequence source code and vice versa.

4. The CEST preparation period can be used directly at the scanner with different state of the art readouts, bridging the gap from first publication to reproducible multi-site application not only for research, but also for clinical applications. In addition, novel developments and work-in-progress approaches can be compared much faster and more reliably than with existing approaches.

Using Pulseq for the CEST preparation part in the sequence theoretically enables a vendor-independent approach, provided that a Pulseq interpreter sequence is available for each vendor. In this work, we implemented such a hybrid Pulseq-CEST sequence for the Siemens IDEA (Integrated Development Environment for Applications, Siemens Healthineers, Erlangen, Germany) framework and tested it on three Siemens scanners at three sites, with two scanner models running very different software baselines. In addition, we present a fast and flexible open source simulation for the same pulseq-files that are played out on the MR scanner. Moreover, we provide a platform for researchers to share and test their saturation protocols in the Pulseq format. As a first illustration of these steps, we show applications at clinically available MR scanners including adiabatic spin-lock prepared imaging, well-defined and original-author-approved APT-weighted imaging, and a CEST MR Fingerprinting (MRF) protocol measured at three different MR sites in Europe and the USA.

2. Methods

2.1. Pulseq to standardize CEST Preparation Periods

Originally intended as a hardware independent MRI sequence prototyping framework, Pulseq allows for rapid and simple sequence definitions from within MATLAB, python and other software programming packages, which are usually open source^23,24,28. Within these programs, RF pulse, gradient, ADC and trigger events can be easily defined and are written to a pulseq-file, which is then read and played out by a native interpreter sequence on the scanner. Since Pulseq includes built-in functions to generate block, Gaussian, apodized sinc and arbitrary user-defined pulse shapes, theoretically every excitation or saturation CEST preparation scheme can be defined with only a few lines of code. Thus, defining saturation periods in Pulseq is a general and easy approach. Example codes to create different CEST preparation periods in MATLAB and python are provided at the projects’ website (https://pulseq-cest.github.io).

The pulseq-file thus contains the full definition of all sequence objects, which makes it a perfectly suitable candidate for sharing protocol parameters of preparation periods for generating different types of contrast before readout, such as a CEST contrast here. Moreover, these pulseq-files are human-readable and the aforementioned MATLAB and python packages include plot functions to compare preparation schemes directly. The benefit of such a direct comparison becomes obvious in Figure 1. Here, RF magnitude and phase of different CEST preparation periods used in this study are shown. For instance, Figures 1 A and B show the shape of an off-resonant adiabatic spin-lock pulse scheme previously used for in vivo DGEρ studies²⁹ and phantom measurements herein. While such a pulse shape would be rather complex to describe in a publication, it is completely defined in the pulseq-file. In Figures 1 C–H, three different saturation preparation protocols (APTw_3T_001³⁰, APTw_3T_002³¹ and APTw_3T_003³²), recently recommended for APT-weighted tumor applications, are shown³³. For comparison of the CEST preparation pulses, we use the following definitions for the B₁ pulse average (B_1,pa), B₁ average amplitude over pulse train (B_1,cwae) and B₁ average quadratic amplitude over pulse train (B_1,rms or B_1,cwpe)^34,35.

$$B_{1,pa}= \frac{1}{t_p}\underset{0}{\overset{t_p}{\int }}{B}_1\left(t\right)$$ (1)

$$B_{1,\mathit{\text{cwae}}}= \frac{1}{t_p+t_d}\underset{0}{\overset{t_p}{\int }}{B}_1\left(t\right)$$ (2)

$$B_{1,rms}= \sqrt{\frac{1}{t_p+t_d}\underset{0}{\overset{t_p}{\int }}{\left(B_1\left(t\right)\right)}^2}$$ (3)

where t_p is the pulse duration, and t_d the delay between preparation pulses. For the three different APTw protocols in Figure 1 C–H, B_1,rms was set to 2 μT. The different peak amplitudes of the saturation pulses due to the different shapes and duty cycles are directly observable. For instance, in Figures 1C and 1E, Sinc-Gauss pulses with the same shape are shown, but in 1E, the peak amplitude of the pulse is higher since this protocol uses a saturation duty cycle (DC_sat) of 0.5 compared to 0.9 for the protocol in C.

Figure 1:

(A,B) RF magnitude and phase of the HSExp spin-lock saturation at 0.6 ppm. Adiabatic tip-down (blue) and tip-up (yellow) pulses surround the locking pulse (red). The frequency modulation can be seen in the zoomed plot (black box) from the changing phase. The red dot marks the beginning of the readout period which can be e.g. an FID or a full 3D GRE; spoiler gradients are not shown for simplicity. RF magnitude (C,E,G) and phase (D,F,H) during three different APTw protocols: APTw_3T_001 (C,D), APTw_3T_002 (E,F) and APTw_3T_003 (G, H) all with B_1,rms = 2 μT and recovery time, T_rec = 3.5 s. Due to the different pulse shapes and DC_sat during the preparation periods, the peak amplitudes of the pulses differ. In C) and D), a zoomed graph for two RF pulses is shown in the black rectangles. The phase accumulation due to the off-resonance character of the pulses is taken into account as shown between the blue and red phase curve. The blue x marks the phase at the center of the RF pulse. (I, J) RF magnitude and phase of multiple repetitions during an amide-MRF spin-lock saturation at 3.5 ppm.

Figures 1 I and J show the RF magnitude and phase over multiple repetitions of the CEST-MRF schedule^36–38. Here, the amplitude of the spin-lock saturation pulses changes over the different repetitions.

Full details about the protocols can be found in the pulseq-files in the supporting information. In addition, the RF phase evolution during saturation pulses is available, a parameter which is rarely provided in CEST literature, although it can have an influence on the experiment as shown in more detail the supporting information (Supporting Information Figures S1 and S2).

2.2. Bloch McConnell Simulations

To be able to not only compare protocol parameters, but also simulate them, an application was written in C++ that loops through the pulseq-files, performs Bloch-McConnell simulations for the respective sequence events³⁷, and returns the current magnetization vector after preparation. The compiled code is callable as a mex-function for an easy integration into a MATLAB-based pipeline. The python implementation wraps the C++ code with SWIG (Simplified Wrapper and Interface Generator) and is simplified by a python parser. This setup ensures input and output compatibility between the MATLAB and python implementation. Where not specified explicitly, the mex-function was used for the simulations in this study. Due to the flexible design, it is possible to simulate an arbitrary number of CEST pools and an additional semi-solid MTC pool with either a Lorentzian or Super-Lorentzian line shape. In combination with the flexible Pulseq saturation period definition, any number of CEST pools can be simulated for any kind of preparation period in a relatively short amount of time, thanks to the native C++ implementation. For this study, the simulation program was compiled for a 64-bit Microsoft Windows 10 OS, using the Microsoft Visual C++ 2017 compiler. Simulations were performed on a PC with an Intel i7–7700K Kaby Lake CPU. The source code is available on the projects’ website (https://pulseq-cest.github.io/).

2.3. Pulseq Sequence Building Block

By adapting the source code of the original Pulseq sequence for the Siemens IDEA framework, we were able to play out the pulseq-files, containing the definition of the CEST preparation period, directly on the scanner, followed by different readout sequences. Adaptions were implemented to use the code as a so-called sequence building block (SBB), e.g.: (I) the FOV positioning options are removed, as this information is defined in the readout sequence; (II) during the Pulseq building block, no data is acquired as indicated by the ADC event; instead the ADC event is used internally as marker to interrupt the Pulseq sequence and switch to the readout sequence which is then played out (Figure 2). Note that the Pulseq block containing an ADC event is skipped entirely and therefore is not allowed to contain any other events. With this design of the Pulseq SBB, it can be implemented in every MR sequence where the source code is available. Since timing and SAR calculations are handled by the interpreter sequence the workload for such implementation is small. Only a few lines of code need to be implemented in the main sequence for initialization, preparation and running of the Pulseq SBB. For instance, to implement the SBB to an established, native EPI sequence^39,40, only 30 lines of C++ code were necessary, including code regarding communication with the UI.

Figure 2:

Schematic sketch of the Pulseq-CEST hybrid sequence playing out the preparation period of protocol APTw_3T_002. A) Format of the pulseq-file with channels for time delay, RF pulse, gradient, ADC and trigger events. The numbers link to entries in a lookup table where the actual event parameters are defined. Note that the RF pulses have the same amplitude and frequency offset, but a different ID due to different phase offsets. B) Example GRE readout sequence. C) Proposed combination of Pulseq events and the readout block using Pulseq as an SBB. RF events appear at blocks number 36, 38, and 40; spoiler gradients at block number 41 and delay events at blocks number 37 and 39. All Pulseq-CEST RF events are spatially non-selective. Blue crosses on the RF phase plots mark the RF phase at the moment of time when the peak RF magnitude is reached. For simplicity, GY and GZ gradient axes are not shown here. At every ADC event in the pulseq-file (block number 42), the GRE readout is played out.

The Pulseq interpreter sequence for Siemens IDEA contains vendor-specific code and can therefore only be obtained via the customer-to-customer partnership program (co-called C2P procedure). The interpreter sequence can be provided upon request to interested researchers. The current Pulseq C2P package supports multiple software and hardware platforms of Siemens, including Numaris4 vb15, vb17, vb19(a,b), vd11d, ve11(a,b,c,e,k,u), ve12u and NumarisX va11 and va20. For other vendors different “interpreter” approaches need to be developed or already exist, e.g. the TOPPE interpreter for GE sequences⁴¹.

2.4. Phantom Preparation

A phantom was prepared using multiple 6 ml tubes with either L-arginine or D-glucose. Five tubes were filled with 50 mmol/L L-arginine (Fluka Chemie, Switzerland) dissolved in phosphate buffered saline (PBS, according to Cold Spring Harbor Protocols⁴², but containing 2.7 mM KCl, 10 mM Na₂HPO₄, 1.8 mM KH₂PO₄ and 140 mM NaCl). The pH of the L-arginine tubes was adjusted between 4 and 6 using HCl (Sigma-Aldrich Laborchemikalien, Germany) and NaOH (Riedel-de Haën, Germany). Two additional tubes were filled with a D-glucose solution (Glucosteril 20%; Fresenius Kabi Deutschland, Germany) and diluted to 33 mmol/L or 66 mmol/L using PBS. To shorten T₁, Gd-DOTA (gadoterate meglumine (dotarem^® 500 mmol/L), Guerbet, Germany) was added to each tube in this phantom yielding a final concentration of approximately 0.054 mmol/L.

2.5. MRI Measurements

MRI measurements were performed on three clinical 3 T scanners, two 3 T Prismas and one 3 T Trio (Siemens Healthineers, Germany). For all Prisma measurements, the 64-channel head coil for signal reception and the body coil for transmission were used. All in vivo measurements were performed under approval by the local ethics committee. Each subject gave a written informed consent prior to the study. Where not specified explicitly, measurements were done on Siemens 3 T Prisma Scanner at MPI Tuebingen. Following the Pulseq SBB a 3D gradient echo readout^26,27 was used. A table with relevant imaging parameters for the readout sequence can be found in the supporting information (Supporting Information Table S1). In addition, all pulseq-files used in this study are provided in the supporting information.

For the phantom experiment, a spin-lock (SL) Z-spectrum acquisition was performed using HSExp pulses⁴³ with the pulse shape parameters recently optimized for 3T²⁹ (see Figures 1A and 1B, SLExp_3T_Phantom.seq in Supporting Information). For each saturation, a single SL pulse with a duration of 100 ms was played out after a recovery delay T_rec = 5 s. For this protocol, 39 evenly distributed offsets between ±6 ppm were acquired, together with an unsaturated S₀ scan. To enable realistic simulations, a WASABI⁴⁴, saturation recovery and T₂ magnetization preparation⁴⁵ sequence were applied to determine B₀, B₁, T₁ and T₂ maps. The phantom was scanned at a room temperature of about 25 °C.

The three different APTw protocols shown in Figures 1C–H (APTw_3T_001.seq, APTw_3T_002.seq, APTw_3T_003.seq in Supporting Information) were scanned for direct comparison in vivo in a healthy volunteer. In addition, a WASABI measurement was performed to correct the Z-spectra for B₀ field inhomogeneity. To generate MTR_asym maps with higher SNR in-vivo, all APTw protocols were acquired with repeated acquisitions (3 repetitions) at the offsets of interest and a dummy scan at the beginning (APTw_3T_001_AVG.seq, APTw_3T_002_AVG.seq, APTw_3T_003_AVG.seq in Supporting Information). This averaged protocol was additionally measured in a patient with a glioblastoma (WHO grade IV, IHD mutation and methylation of MGMT promoter) at the University Hospital Erlangen under approval of the local ethics committee.

Finally, we performed a whole-brain, in vivo CEST-MRF³⁶ measurement at three different sites. For this purpose, the Pulseq SBB was implemented into a 3D EPI sequence^39,40. All relevant readout parameters can be found in the supporting information (Supporting Information Table S1). The protocol consisted of the following measurements: (I) an amide-proton-transfer weighted MRF protocol with 31 scans, using spin-lock pulses with a B₁ varying between 0 – 4 μT at a constant offset of 3.5 ppm (see Figures 1I and 1J); (II) a semi-solid MTC weighted MRF protocol with 31 scans, using spin-lock pulses with B₁ varying between 0.2 – 4 μT at offsets varying between 6 – 14 ppm; (III) a WASABI measurement for B₀ and B₁ field inhomogeneity maps; (IV) a saturation recovery measurement for T₁ maps; (V) a T₂ preparation measurement for T₂ maps. All pulseq-files for this experiment can be found in the supporting information (MRF_Amide.seq, MRF_MT.seq, T1prep.seq, T2prep.seq, WASABI.seq). The protocol was applied at three different sites for one healthy volunteer each (two Siemens 3 T Prisma Scanners at MPI Tuebingen and MGH Boston and a Siemens 3 T Trio scanner at University Hospital Erlangen). For the Trio system, a 32-channel coil was used for reception, while a 64-channel coil was used at both Prisma systems.

2.6. Post-Processing

For the phantom experiment, MTR_asym maps were generated voxel-wise, with MTR_asym(Δω) = (S(−Δω)− S(Δω))/S₀, after a ΔB₀ correction using a linear interpolation between acquired offsets. Additionally, three regions of interest (ROIs) were drawn in the center slice in the tubes listed in Table 1. Z-spectra in these ROIs were simulated as described in section 2.2. All simulation parameters can be found in the supporting information (Supporting Information Tables S2–S4). Both simulated and measured Z-spectra were normalized by the measurement at −6 ppm.

Table 1:

ROI and phantom information

ROI No.	Solute	Concentration [mmol/l]	pH	No. Voxels
1	L-arginine	50	5.55	18
2	L-arginine	50	4.01	23
3	D-glucose	66	6.52	23

For the in-vivo APTw experiment, all measurements were motion corrected using elastix⁴⁶. The applied elastix parameter file can be found in the supporting information (Rigid_MMI.txt). MTR_asym maps were generated in the same way as for the phantom but with applying an additional principal component analysis denoising approach⁴⁷ using the Malinowski criterion and a spatial 2D in-plane Gaussian filter (σ = 0.6) to smooth the images. A white matter ROI was generated automatically by segmenting the image at −4 ppm using SPM⁴⁸

For the CEST-MRF experiment, all images were again motion corrected and registered to the T₁ measurement using elastix. T₁ maps were generated by fitting a mono-exponential function to the data of the saturation recovery measurement. The T₁ map was then used to generate a synthetic T₁-weighted image, which was subsequently used to generate gray and white matter segmentation masks using SPM. Dictionary generation and calculation of amide and semi-solid MTC concentration and exchange rate maps were performed according to Perlman et al.³⁸. A major advantage in using the Pulseq SBB in this context is that for both, measurement and dictionary generation, the same pulseq-file describing the saturation period definition is used. This reduces possible error sources from transferring the measurement parameters to the simulation or vice versa.

3. Results

3.1. Simulation and phantom measurement

The first experiment demonstrates how the same pulseq-file can be used for simulation and phantom measurements. Figure 3 shows the MTR_asym map (A) at 2 ppm, as well as the measured Z-spectra (Z(Δω) = S(Δω)/S(−6 ppm)) and simulated Z-spectra Z(Δω) = M_z(Δω)/M_z(−6 ppm) (B) and the MTR_asym (D) curve for the three different ROIs. The residuals between these measured and simulated normalized intensities are displayed in Figure 3C. The maximum residuals were 0.009, 0.008 and 0.004 for ROIs 1, 2, and 3 respectively. Simulation of the pulseq-file, using 500 samples per pulse (60000 pulse samples per Z-spectrum) took approx. 0.19 s for a single amide CEST pool Z-spectrum (ROI 1 and 2) and 0.77 s for four different D-glucose CEST pools (ROI 3).

Figure 3:

A) MTR_asym(Δω = 2ppm) in slice 6. B) Simulated and measured Z-spectra for ROIs 1,2 and 3, respectively. Error bars in the measured Z-spectra show the standard deviation of Z-spectra across voxels. C) Difference between the measured and simulated Z-spectra for each ROI. D) MTR_asym curves of simulated and measured Z-spectra for the 3 ROIs.

3.2. In vivo APT-weighted measurements

The second experiment demonstrates the value of well-defined pulseq-files for comparison of different author-approved APTw CEST implementations. The APTw (MTR_asym(3.5 ppm)) results for the volunteer and the tumor patient can be found in Figure 4. MTR_asym maps for the three different protocols are shown in Figures 4A–C and despite different saturation, they show similar contrast in the healthy volunteer. The contrast in healthy brain is very low, which is expected as the APTw-imaging parameters are designed to yield almost no contrast in healthy tissue^4,49–51. Corresponding Z-spectra, with Z(Δω) = S(Δω)/S(−1560 ppm), and MTR_asym spectra can be found in Figures 4H and 4I, respectively. Comparison with simulation can be found in Supporting Information Figure S3. While the intensity in the MTR_asym maps of the healthy volunteer is similar for all three protocols, a clear contrast can be seen in the tumor region (Figures 4D–F) of the glioblastoma patient. These protocols are all in use at clinical scanners, but were, to our knowledge, never compared side by side. Such a comparison can now be performed in different pathologies, to validate different protocols and their relation to previous work.

Figure 4:

A-C) MTR_asym maps at 3.5 ppm for the APTw_3T_001_AVG (A), APTw_3T_002_AVG (B) and APTw_3T_000_AVG3 (C) protocols in a healthy volunteer. D-F) MTR_asym maps at 3.5 ppm for the APTw_3T_001_AVG (D), APTw_3T_002_AVG (E) and APTw_3T_003_AVG (F) protocols in a glioblastoma patient. G) Contrast-enhanced MPRAGE image for this patient. Z-spectra (H) and MTR_asym curve (I) for a white matter ROI in the volunteer. Due to the rectangular saturation, residual artifacts in the ventricle regions can be observed in (C). Error bars in H show the standard deviation in the WM ROI.

3.3. In vivo CEST-MRF measurements

The final experiment is to demonstrate that with Pulseq-CEST, sophisticated CEST sequences can be shared between sites, for both simulation (and training of reconstruction networks in this CEST-MRF example), as well as for measurement of work-in-progress developments at different systems. The spin-lock-based CEST-MRF scheme with whole brain EPI readout was applied in three healthy volunteers and at three different MR sites. The resulting maps for the semisolid proton fraction (f_MT), the semisolid proton to water proton exchange rate (k_MT), the amide proton fraction (f_Amide), and the amide proton to water proton exchange rate (k_Amide) are shown in Figure 5. The mean values for gray and white matter across the entire brain are shown in table 2. Visual as well as quantitative comparison indicates reproducibility of effects across multiple sites.

Figure 5:

semi-solid MTC water fraction (1st column) and exchange rate (2nd column), amide water fraction (3rd column) and exchange rate (4th column) for measurements at a 3T Prisma in Tuebingen (1st row), 3T Prisma in Boston (2nd row) and 3T Trio in Erlangen (3rd row).

Table 2:

Mean values for quantitative MRF parameters and relative B₁ values of gray and white matter at the three different sites (Tuebingen - TUE; Boston - BOS; Erlangen - ERL).

	fMT GM	kMT GM [Hz]	fMT WM	kMT WM [Hz]	fAmide GM	kAmide GM [Hz]	fAmide WM	kAmide WM [Hz]	rel. B1 [GM]	rel. B1 [WM]
TUE Prisma	0.140 ± 0.033	47.3 ± 10.8	0.201 ± 0.017	31.7 ± 6.7	0.0046 ± 0.0010	54.6 ± 10.0	0.0052 ± 0.0009	60.7 ± 7.5	1.03 ± 0.10	1.05 ± 0.09
BOS Prisma	0.142 ± 0.038	47.7 ± 11.3	0.203 ± 0.019	32.0 ± 6.6	0.0049 ± 0.0010	58.3 ± 8.8	0.0055 ± 0.0008	63.2 ± 6.0	0.99 ± 0.09	1.02 ± 0.08
ERL Trio	0.142 ± 0.034	48.6 ± 11.2	0.197 ± 0.022	35.2 ± 8.6	0.0047 ± 0.0011	55.8 ± 10.3	0.0054 ± 0.0009	61.9 ± 7.3	1.01 ± 0.10	1.03 ± 0.09

4. Discussion

By adapting the source code of the Pulseq interpreter sequence to the SBB concept of the Siemens IDEA framework we were able to use it as a sequence building block in established MR sequences and subsequently run CEST experiments at different clinical 3T scanners. Hence, we combined the full flexibility of Pulseq and the sophisticated readout methods from native sequences to generate an easy-to-use and flexible method for reproducible CEST measurements. For instance, it was directly possible to perform CEST-MRF experiments at three different sites on scanners with distinctly different software versions using identical CEST preparation periods defined in Pulseq. Thus, a sophisticated and still work-in-progress protocol could be reliably shared between research sites using Pulseq-CEST, without being limited by hard-coded user interface interactions. The quantitative results of the applied CEST-MRF method are consistent across sites, although the maps generated from the Trio system appear noisier. We attribute this mainly to the used 32-channel receive coil, leading to lower SNR compared to the 64-channel coil used on the Prisma system. In addition, the actual amplitude of the RF pulses is not only determined by the defined pulse shape in the pulseq-file, but also by the reference voltage of the system. It is therefore possible, that different reference voltages lead to different results. Such MRF differences can now be evaluated and handled. While larger, further studies using this protocol should compare and discuss results with previous CEST-MRF studies^38,52, it is beyond the scope of the herein presented work, since Bloch-McConnell based quantification is extremely challenging in vivo. Results presented here represent a work-in-progress state of a sophisticated method undergoing an active development. The Pulseq-CEST standard allows for efficient and active exchange between the research sites even in such an early stage of development, accelerating generation and refinement of simulation databases and improving model training. The Pulseq SBB presented in this paper allows convenient though reliable multi-center collaboration to further investigate the method in detail in a larger cohort.

In addition, our software provides a Bloch-McConnell simulation tool for pulseq-files to simulate the exact same CEST preparation period that is played out by the interpreter on the scanner. The fast, native C++ implementation allows for pulsed CEST simulations, also with multiple isochromats, an application discussed in more detail in the supporting information (Supporting Information Figures S4 and S5). To ensure broad applicability, we provide implementations of the Bloch-McConnell simulation in both MATLAB and python, which are the most frequently used programming languages in research. Confirmation that both implementations yield identical results is provided in the supporting information (Supporting Information Table S5). We hope that with this work, we provide the first version of a valuable and needed tool for the CEST community, to exchange and test CEST preparation periods for the many different types of different CEST experiments. For instance, a researcher publishing data could share a pulseq-file containing all RF pulse, gradient and delay parameters via the supporting information and it could subsequently be used by other researchers. Additionally, all pulseq-files can be made available via the projects’ website (https://pulseq-cest.github.io/).

Moreover, the Pulseq sequence building block can be used to test all these CEST preparation blocks with a minimum workload, even with different readout sequences, for instance GRE, EPI or RARE, that have been optimized with regard to their imaging performance beforehand. While the SBB is so far only available for Siemens scanners, it already demonstrated its multi-platform capabilities by executing the same CEST preparation periods on scanners built upon different hardware components and running different software versions. Furthermore, it is generally possible to transfer the approach to GE and Bruker systems where Pulseq implementations have been demonstrated²³. For Bruker systems, we recently proposed an initial approach to combine CEST preparation periods from pulseq-files with native Bruker readouts automatically with MATLAB⁵³. By doing so, we were able to measure the above-mentioned APTw protocols on a Bruker 14.1 T scanner with ParaVision 6. This will be useful for comparison between pre-clinical and clinical trials, especially for pulsed CEST approaches.

A design of the interpreter software for other manufacturers (e.g. Philips, United or Canon) is needed for a universal application. However, even without actually applying pulseq-files on the scanner, the possibility to share, edit, display and simulate saturation periods can be very insightful and beneficial for the design of CEST experiments. Since the pulseq-file guarantees a completely defined CEST preparation period inherently, which we believe is needed to improve the reproducibility of data in the CEST community, this will be a valuable tool especially when designing multi-site clinical trials of technology such as for instance APTw MRI of brain tumors, which is being performed currently at many sites but often with different protocols.

The Pulseq interpreter sequence has been adapted and used as a sequence building block in native readout sequences herein, but it is also possible to use readouts implemented directly in Pulseq. The current version of the Pulseq interpreter allows for the images to be reconstructed directly on the scanner, which makes the development of novel imaging sequences even more convenient. As the presented approach is compatible to the “parent” Pulseq project, the previously published Pulseq-CEST preparation periods could be trivially integrated with these novel readout modules, implemented directly in Pulseq. This will allow full reproducibility as the complete sequence including preparation and readout can be published in the pulseq-file format in the same database.

While native Pulseq readout modules can be considered for measurements in the future, it is already possible to combine them with CEST preparation modules for more realistic simulations. In that case, the pseudo ADC event needs to be replaced by a full readout sequence. Various examples of possible readout sequences implemented with Pulseq can be found on the project’s website (https://pulseq.github.io/). By explicitly including the readout, it becomes possible to simulate the influence of the RF pulses during the readout with the provided simulation (Supporting Information Figure S6). It is also possible to employ a different Pulseq-compatible simulation software, such as JEMRIS⁵⁴, which supports Bloch-McConnell simulations, and provides a full MRI simulation framework. In addition, if the readout module is included in the pulseq-file it is possible to estimate the expected SAR value using sar4seq⁵⁵ prior to the scanning session. In general, the open format of Pulseq allows for a broad development of useful applications.

With regard to the growing number of applications of neural networks to CEST data reconstruction^{22,38,56–58}, often trained on simulated data, a match of the sequences used in silico and in vivo becomes crucial. In fact, the CEST-MRF reconstruction network used to infer the quantitative maps of Figure 5 was trained using simulated data employing exactly the same pulseq-file as used at the MR scanner. This is just one example showing that Pulseq-CEST could be valuable for many emerging machine and deep learning-based approaches.

Finally, the method is obviously not limited to CEST applications. In principle, any magnetization preparation sequence can be realized as exemplified in Supporting Information Figure S7 for the Water-Shift and B₁ (WASABI) field mapping approach and a T₁ saturation recovery sequence.

5. Conclusion

We presented a Pulseq-based sequence framework for CEST preparation pulse sequences and an accompanying simulation tool, the use of which in combination with available MRI sequences allows for straightforward sharing, implementing, testing, optimizing and running of CEST MRI studies. Since the pulseq-files inherently include a complete CEST parameter definition, this fosters faster comparison and facilitates reproducibility – not only between different MR sites, but also between real and simulated environments.

Source code for the Siemens IDEA interpreter sequence is available upon request. All code for creating and simulating pulseq-files is open source and can be obtained via the projects’ website (https://pulseq-cest.github.io/).

Data Availability Statement

All source code used for pulseq-file generation and simulation in this study is openly available at https://pulseq-cest.github.io/ where links to the corresponding GitHub repositories can be found.