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Bruker MRI Sequences:
Continuous-wave CEST (cwCEST)
These sequences are applied to collect continuous wave CEST with RARE and RAREst MRI readout on Bruker scanner (PV6). cestRARE sequence is suggested for phantoms and 3D images, while cwRAREst can acquire CEST rapidly on tissues, but it is not work well on phantoms.
| cwCEST with RARE (ParaVision 6.0.1) |
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cwCEST with short echo RARE (RAREst) (ParaVision 6.0.1) |
Please kindly cite the following papers when using these programs:
a. Lin Chen, Haifeng Zeng, Xiang Xu, Nirbhay N Yadav, Shuhui Cai, Nicolaas A Puts, Peter B Barker, Tong Li, Robert G Weiss, Peter CM van Zijl, Jiadi Xu*, “Investigation of the contribution of total creatine to the CEST Z‐spectrum of brain using a knockout mouse model”, NMR in Biomedicine, 30, e3834(2017).
b. Lin Chen, Peter B Barker, Robert G Weiss, Peter CM van Zijl, Jiadi Xu*, “Creatine and phosphocreatine mapping of mouse skeletal muscle by a polynomial and Lorentzian line‐shape fitting CEST method”, Magnetic Resonance in Medicine, 81, 69-78(2019).
c. Lin Chen, Zhiliang Wei, Shuhui Cai, Yuguo Li, Guanshu Liu, Hanzhang Lu, Robert G Weiss, Peter CM van Zijl, Jiadi Xu*, “High‐resolution creatine mapping of mouse brain at 11.7 T using non‐steady‐state chemical exchange saturation transfer”, NMR in Biomedicine , 32 (11), e4168(2019).
Ultra-short echo time MRI CEST (UTE-CEST)
This sequence is used to collect steady-state CEST with UTE MRI readout. The UTE MRI is robust to motions and is suitable for body images such as liver, kidney and heart. It also works on brain to suppress the fluctuation in Z-spectra introduced by physiological motions. The UTE image can be reconstructed by Bruker ParaVision software. We also offer reconstruct method to further improve the stability by removing the K-lines with motions.
| UTE-CEST (ParaVision 6.0.1) |
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| UTE-CEST Example Protocols (ParaVision 6.0.1) |
Please kindly cite the following papers when using these programs:
a. Lin Chen, Zhiliang Wei, Kannie WY Chan, Shuhui Cai, Guanshu Liu, Hanzhang Lu, Philip C Wong, Peter CM van Zijl, Tong Li*, Jiadi Xu*, “Protein aggregation linked to Alzheimer's disease revealed by saturation transfer MRI”, Neuroimage, 188, 380-390(2019).
Application examples:
a. Yang Zhou, Peter CM van Zijl, Xiang Xu, Jiadi Xu, Yuguo Li, Lin Chen, Nirbhay N Yadav “Magnetic resonance imaging of glycogen using its magnetic coupling with water”, Proceedings of the National Academy of Sciences, 117, 3144-3149(2020).
Steady Pulsed Imaging and Labeling (SPIL) Perfusion Mapping
The Steady-state Pulsed Imaging and Labeling (SPIL) scheme is introduced to acquire whole-brain high-resolution perfusion images noninvasively. SPIL has several unique advantages for performing multi-slice perfusion imaging in addition to the typical advantages of PASL methods (i.e., no MT background interference, low SAR, and straightforward implementation): (i) SPIL is robust with respect to experimental errors: Arterial blood is saturated by a train of RF pulses, which is insensitive to the pulse flip angle; (ii) SPIL extends the duration of the PASL bolus to the total experimental time; multi-slice images are acquired during the exchange times. Therefore, it achieves high acquisition efficiency comparing to conventional PASL methods. (iii) SPIL perfusion images do not require correction for each slice’s post-label acquisition delay.
| SPIL Arterial Spin Labeling Sequence (ParaVision 6.0.1) |
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| SPIL Example Protocols (ParaVision 6.0.1) |
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| Matlab Codes for SPIL Processing |
Instruction:
1. This sequence only works with big volume coil (>70mm) combined with receiving coils. The excitation profile of the volume coil should cover mouse or rat heart to achieve enough labeling efficiency on blood.
2. The excitation profile of the inversion pulse is critical. Sech80 pulse is suggested. Copy the attached Sech80.inv into the directory /opt/PV6.0.1/exp/stan/nmr/lists/wave
3. The sequence is a multi-slice method and the slice number should be higher than five.
Please kindly cite the following papers when using these programs:
a. Jiadi Xu*, Qin Qin, Dan Wu, Jun Hua, Xiaolei Song, Michael T McMahon, Frances J Northington, Jiangyang Zhang, Peter CM van Zijl, James J Pekar, “ Steady pulsed imaging and labeling scheme for noninvasive perfusion imaging”, Magnetic Resonance in Medicine, 75, 238-248(2016).
On-resonance variable delay multi-pulse (onVDMP) with multiple saturation lengths
This sequence works similarly to the T1rho experiments and can collect images with different onVDMP saturation lengths. One application of this sequence is the Dynamic Glucose Enhancement (DGE) study. Two different labeling lengths could be applied to monitor the glucose uptake in the brain parenchyma and CSF, respectively.
| onVDMP with multiple saturation lengths (ParaVision 6.0.1) |
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| Post-processing Matlab Code |
Instruction:
1. Copy the attached bibp.inv into the directory /opt/PV6.0.1/exp/stan/nmr/lists/wave
2. The total labeling time is calculated by timing the TElist and the Bibp pulse width, and is determined by the tissue T2. For parenchyma, it is about 36 ms, while it is about 300 ms for CSF at 11.7T. The two values will be 60 ms and 1.4s at 3T. With long saturation length (>500ms), it needs to be caution with the heating of the coil.
3. This sequence support multi-slice acquisition, e.g.several image slices will be collected sequentially after the onVDMP saturation .
Please kindly cite the following papers when using these programs:
a. Jiadi Xu+*, Kannie WY Chan+, Xiang Xu, Nirbhay Yadav, Guanshu Liu, Peter CM van Zijl, “On‐resonance variable delay multipulse scheme for imaging of fast‐exchanging protons and semisolid macromolecules”, Magnetic Resonance in Medicine, 77, 730-739(2017).
b. Jianpan Huang, Peter CM van Zijl, Xiongqi Han, Celia M Dong, Gerald WY Cheng, Kai-Hei Tse, Linda Knutsson, Lin Chen, Joseph HC Lai, Ed X Wu, Jiadi Xu*, Kannie WY Chan*, “ Altered d-glucose in brain parenchyma and cerebrospinal fluid of early Alzheimer’s disease detected by dynamic glucose-enhanced MRI ”, Science Advances, 6, eaba3884 (2020).
c. Lin Chen, Zhiliang Wei, Kannie Chan, Yuguo Li, Kapil Suchal, Sheng Bi, Jianpan Huang, Xiang Xu, Philip Wong, Hanzhang Lu, Peter van Zijl, Tong Li*, Jiadi Xu* “D-Glucose uptake and clearance in the tauopathy Alzheimer’s disease mouse brain detected by on-resonance variable delay multiple pulse MRI ”, Journal of Cerebral Blood Flow & Metabolism, (2020) https://doi.org/10.1177/0271678X20941264.
Post-Processing Softwares:
Polynomial and Lorentzian Line‐shape Fitting (PLOF) Method
This Matlab code is used to extract and quantify CEST signal with distinguishable peaks such as amide, guanidinium and PCr peaks.
| PLOF Matlab Code |
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Please kindly cite the following papers when using these programs:
a. Lin Chen, Haifeng Zeng, Xiang Xu, Nirbhay N Yadav, Shuhui Cai, Nicolaas A Puts, Peter B Barker, Tong Li, Robert G Weiss, Peter CM van Zijl, Jiadi Xu*, “Investigation of the contribution of total creatine to the CEST Z‐spectrum of brain using a knockout mouse model”, NMR in Biomedicine, 30, e3834(2017).
b. Lin Chen, Peter B Barker, Robert G Weiss, Peter CM van Zijl, Jiadi Xu*, “Creatine and phosphocreatine mapping of mouse skeletal muscle by a polynomial and Lorentzian line‐shape fitting CEST method”, Magnetic Resonance in Medicine, 81, 69-78(2019).
c. Ran Sui+, Lin Chen+, Kannie W. Y. Chan, Yuguo Li, Jianpan Huang, Xiang Xu, Peter C. M. van Zijl, Jiadi Xu* “Whole-brain amide CEST imaging at 3T with a steady-state radial MRI acquisition ”, Magnetic Resonance in Medicine, (2021) http://doi.org/10.1002/mrm.28770.
Application examples:
a. Lin Chen, Michael Schär, Kannie WY Chan, Jianpan Huang, Zhiliang Wei, Hanzhang Lu, Qin Qin, Robert G Weiss, Peter CM van Zijl, Jiadi Xu*, “ In vivo imaging of phosphocreatine with artificial neural networks”, Nature Communications, 11, 1072 (2020).
b. Lin Chen*, Suyi Cao, Raymond C. Koehler, Peter CM van Zijl, Jiadi Xu, “High-sensitivity CEST Mapping Using the Spatiotemporal Correlation Enhanced Method”, Magnetic Resonance in Medicine, (2020) https://doi.org/10.1002/mrm.28380.
Artificial Neural Networks CEST (ANNCEST) Quantification
This Matlab code is used to apply ANN method for the CEST quantification as well as the B0/B1 mapping from Z-spectra.
| ANN Matlab Code |
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Please kindly cite the following papers when using these programs:
a. Lin Chen, Michael Schär, Kannie WY Chan, Jianpan Huang, Zhiliang Wei, Hanzhang Lu, Qin Qin, Robert G Weiss, Peter CM van Zijl, Jiadi Xu*, “ In vivo imaging of phosphocreatine with artificial neural networks”, Nature Communications, 11, 1072 (2020).
Multilinear singular value decomposition (MLSVD) CEST
This Matlab code is used to apply MLSVD method for denosing CEST images to achieve high-sensitivity CEST Mapping.
| MLSVD Matlab Code |
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Please kindly cite the following papers when using these programs:
a. Lin Chen*, Suyi Cao, Raymond C. Koehler, Peter CM van Zijl, Jiadi Xu, “High-sensitivity CEST Mapping Using the Spatiotemporal Correlation Enhanced Method”, Magnetic Resonance in Medicine, (2020) https://doi.org/10.1002/mrm.28380.