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Chen L, Xu H, Gong T, Jin J, Lin L, Zhou Y, Huang J, Chen Z. Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing. Magn Reson Med 2024; 92:2616-2630. [PMID: 39044635 DOI: 10.1002/mrm.30233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/23/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024]
Abstract
PURPOSE To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy. METHOD A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve. RESULTS The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy. CONCLUSION The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.
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Affiliation(s)
- Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
| | - Haipeng Xu
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
| | - Tao Gong
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Junxian Jin
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Liangjie Lin
- Clinical & Technical Supports, Philips Healthcare, Beijing, China
| | - Yang Zhou
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jianpan Huang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen, China
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Kurmi Y, Viswanathan M, Zu Z. Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder. Magn Reson Med 2024; 92:2404-2419. [PMID: 39030953 DOI: 10.1002/mrm.30228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 05/28/2024] [Accepted: 07/01/2024] [Indexed: 07/22/2024]
Abstract
PURPOSE To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods. METHOD The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of one-dimensional convolutions, nonlinearity applications, and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis-processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric. RESULTS In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirm the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared with other methods. Although no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings. CONCLUSION The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared with other methods.
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Affiliation(s)
- Yashwant Kurmi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Malvika Viswanathan
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
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Wang K, Ju L, Song Y, Blair L, Xie K, Liu C, Li A, Zhu D, Xu F, Liu G, Heo HY, Yadav N, Oeltzschner G, Edden RAE, Qin Q, Kamson DO, Xu J. Whole-cerebrum guanidino and amide CEST mapping at 3 T by a 3D stack-of-spirals gradient echo acquisition. Magn Reson Med 2024; 92:1456-1470. [PMID: 38748853 PMCID: PMC11262991 DOI: 10.1002/mrm.30134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 04/01/2024] [Accepted: 04/09/2024] [Indexed: 05/22/2024]
Abstract
PURPOSE To develop a 3D, high-sensitivity CEST mapping technique based on the 3D stack-of-spirals (SOS) gradient echo readout, the proposed approach was compared with conventional acquisition techniques and evaluated for its efficacy in concurrently mapping of guanidino (Guan) and amide CEST in human brain at 3 T, leveraging the polynomial Lorentzian line-shape fitting (PLOF) method. METHODS Saturation time and recovery delay were optimized to achieve maximum CEST time efficiency. The 3DSOS method was compared with segmented 3D EPI (3DEPI), turbo spin echo, and gradient- and spin-echo techniques. Image quality, temporal SNR (tSNR), and test-retest reliability were assessed. Maps of Guan and amide CEST derived from 3DSOS were demonstrated on a low-grade glioma patient. RESULTS The optimized recovery delay/saturation time was determined to be 1.4/2 s for Guan and amide CEST. In addition to nearly doubling the slice number, the gradient echo techniques also outperformed spin echo sequences in tSNR: 3DEPI (193.8 ± 6.6), 3DSOS (173.9 ± 5.6), and GRASE (141.0 ± 2.7). 3DSOS, compared with 3DEPI, demonstrated comparable GuanCEST signal in gray matter (GM) (3DSOS: [2.14%-2.59%] vs. 3DEPI: [2.15%-2.61%]), and white matter (WM) (3DSOS: [1.49%-2.11%] vs. 3DEPI: [1.64%-2.09%]). 3DSOS also achieves significantly higher amideCEST in both GM (3DSOS: [2.29%-3.00%] vs. 3DEPI: [2.06%-2.92%]) and WM (3DSOS: [2.23%-2.66%] vs. 3DEPI: [1.95%-2.57%]). 3DSOS outperforms 3DEPI in terms of scan-rescan reliability (correlation coefficient: 3DSOS: 0.58-0.96 vs. 3DEPI: -0.02 to 0.75) and robustness to motion as well. CONCLUSION The 3DSOS CEST technique shows promise for whole-cerebrum CEST imaging, offering uniform contrast and robustness against motion artifacts.
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Affiliation(s)
- Kexin Wang
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Licheng Ju
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Lindsay Blair
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kevin Xie
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Claire Liu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Anna Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Dan Zhu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Feng Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanshu Liu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hye-Young Heo
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nirbhay Yadav
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Georg Oeltzschner
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Richard A. E. Edden
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Qin Qin
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David Olayinka Kamson
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Liu C, Li Z, Chen Z, Zhao B, Zheng Z, Song X. Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction. Magn Reson Med 2024; 92:688-701. [PMID: 38623899 DOI: 10.1002/mrm.30089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/31/2024] [Accepted: 03/02/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction. METHODS For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects. RESULTS In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones. CONCLUSION The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.
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Affiliation(s)
- Chuyu Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhongsen Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhensen Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Benqi Zhao
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Zhuozhao Zheng
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Xiaolei Song
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
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Gammaraccio F, Villano D, Irrera P, Anemone AA, Carella A, Corrado A, Longo DL. Development and Validation of Four Different Methods to Improve MRI-CEST Tumor pH Mapping in Presence of Fat. J Imaging 2024; 10:166. [PMID: 39057737 PMCID: PMC11277679 DOI: 10.3390/jimaging10070166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/28/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
CEST-MRI is an emerging imaging technique suitable for various in vivo applications, including the quantification of tumor acidosis. Traditionally, CEST contrast is calculated by asymmetry analysis, but the presence of fat signals leads to wrong contrast quantification and hence to inaccurate pH measurements. In this study, we investigated four post-processing approaches to overcome fat signal influences and enable correct CEST contrast calculations and tumor pH measurements using iopamidol. The proposed methods involve replacing the Z-spectrum region affected by fat peaks by (i) using a linear interpolation of the fat frequencies, (ii) applying water pool Lorentzian fitting, (iii) considering only the positive part of the Z-spectrum, or (iv) calculating a correction factor for the ratiometric value. In vitro and in vivo studies demonstrated the possibility of using these approaches to calculate CEST contrast and then to measure tumor pH, even in the presence of moderate to high fat fraction values. However, only the method based on the water pool Lorentzian fitting produced highly accurate results in terms of pH measurement in tumor-bearing mice with low and high fat contents.
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Affiliation(s)
- Francesco Gammaraccio
- Department of Molecular Biotechnology and Health Sciences, University of Turin, 10126 Torino, Italy
| | - Daisy Villano
- Department of Molecular Biotechnology and Health Sciences, University of Turin, 10126 Torino, Italy
| | - Pietro Irrera
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), 10126 Torino, Italy
| | - Annasofia A. Anemone
- Department of Molecular Biotechnology and Health Sciences, University of Turin, 10126 Torino, Italy
| | - Antonella Carella
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), 10126 Torino, Italy
| | - Alessia Corrado
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), 10126 Torino, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), 10126 Torino, Italy
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Kurmi Y, Viswanathan M, Zu Z. A Denoising Convolutional Autoencoder for SNR Enhancement in Chemical Exchange Saturation Transfer imaging: (DCAE-CEST). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597818. [PMID: 38895366 PMCID: PMC11185751 DOI: 10.1101/2024.06.07.597818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Purpose To develop a SNR enhancement method for chemical exchange saturation transfer (CEST) imaging using a denoising convolutional autoencoder (DCAE), and compare its performance with state-of-the-art denoising methods. Method The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of 1D convolutions, nonlinearity applications and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in-vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric. Results In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirms the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared to other methods. While no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings. Conclusion The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared to other methods.
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Affiliation(s)
- Yashwant Kurmi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Malvika Viswanathan
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, USA
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, USA
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Zhou IY, Ji Y, Zhao Y, Malvika V, Sun PZ, Zu Z. Specific and rapid guanidinium CEST imaging using double saturation power and QUASS analysis in a rodent model of global ischemia. Magn Reson Med 2024; 91:1512-1527. [PMID: 38098305 PMCID: PMC10872646 DOI: 10.1002/mrm.29960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 10/17/2023] [Accepted: 11/20/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE Guanidinium CEST is sensitive to metabolic changes and pH variation in ischemia, and it can offer advantages over conventional pH-sensitive amide proton transfer (APT) imaging by providing hyperintense contrast in stroke lesions. However, quantifying guanidinium CEST is challenging due to multiple overlapping components and a close frequency offset from water. This study aims to evaluate the applicability of a new rapid and model-free CEST quantification method using double saturation power, termed DSP-CEST, for isolating the guanidinium CEST effect from confounding factors in ischemia. To further reduce acquisition time, the DSP-CEST was combined with a quasi-steady state (QUASS) CEST technique to process non-steady-state CEST signals. METHODS The specificity and accuracy of the DSP-CEST method in quantifying the guanidinium CEST effect were assessed by comparing simulated CEST signals with/without the contribution from confounding factors. The feasibility of this method for quantifying guanidinium CEST was evaluated in a rat model of global ischemia induced by cardiac arrest and compared to a conventional multiple-pool Lorentzian fit method. RESULTS The DSP-CEST method was successful in removing all confounding components and quantifying the guanidinium CEST signal increase in ischemia. This suggests that the DSP-CEST has the potential to provide hyperintense contrast in stroke lesions. Additionally, the DSP-CEST was shown to be a rapid method that does not require the acquisition of the entire or a portion of the CEST Z-spectrum that is required in conventional model-based fitting approaches. CONCLUSION This study highlights the potential of DSP-CEST as a valuable tool for rapid and specific detection of viable tissues.
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Affiliation(s)
- Iris Y. Zhou
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, US
| | - Yang Ji
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Viswanathan Malvika
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
| | - Phillip Zhe Sun
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, US
- Primate Imaging Center, Emory National Primate Research Center, Emory University, Atlanta, GA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
- Department of Biomedical Engineering, Vanderbilt University, Nashville, US
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Ju L, Wang K, Schär M, Xu S, Rogers J, Zhu D, Qin Q, Weiss RG, Xu J. Simultaneous creatine and phosphocreatine mapping of skeletal muscle by CEST MRI at 3T. Magn Reson Med 2024; 91:942-954. [PMID: 37899691 PMCID: PMC10842434 DOI: 10.1002/mrm.29907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/20/2023] [Accepted: 10/11/2023] [Indexed: 10/31/2023]
Abstract
PURPOSE To confirm that CrCEST in muscle exhibits a slow-exchanging process, and to obtain high-resolution amide, creatine (Cr), and phosphocreatine (PCr) maps of skeletal muscle using a POlynomial and Lorentzian Line-shape Fitting (PLOF) CEST at 3T. METHODS We used dynamic changes in PCr/CrCEST of mouse hindlimb before and after euthanasia to assign the Cr and PCr CEST peaks in the Z-spectrum at 3T and to obtain the optimum saturation parameters. Segmented 3D EPI was employed to obtain multi-slice amide, PCr, and Cr CEST maps of human skeletal muscle. Subsequently, the PCrCEST maps were calibrated using the PCr concentrations determined by 31 P MRS. RESULTS A comparison of the Z-spectra in mouse hindlimb before and after euthanasia indicated that CrCEST is a slow-exchanging process in muscle (<150.7 s-1 ). This allowed us to simultaneously extract PCr/CrCEST signals at 3T using the PLOF method. We determined optimal B1 values ranging from 0.3 to 0.6 μT for CrCEST in muscle and 0.3-1.2 μT for PCrCEST. For the study on human calf muscle, we determined an optimum saturation time of 2 s for both PCr/CrCEST (B1 = 0.6 μT). The PCr/CrCEST using 3D EPI were found to be comparable to those obtained using turbo spin echo (TSE). (3D EPI/TSE PCr: (2.6 ± 0.3) %/(2.3 ± 0.1) %; Cr: (1.3 ± 0.1) %/(1.4 ± 0.07) %). CONCLUSIONS Our study showed that in vivo CrCEST is a slow-exchanging process. Hence, amide, Cr, and PCr CEST in the skeletal muscle can be mapped simultaneously at 3T by PLOF CEST.
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Affiliation(s)
- Licheng Ju
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kexin Wang
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Michael Schär
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Su Xu
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joshua Rogers
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Dan Zhu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Qin Qin
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert G. Weiss
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Wang K, Huang J, Ju L, Xu S, Gullapalli RP, Liang Y, Rogers J, Li Y, van Zijl PCM, Weiss RG, Chan KWY, Xu J. Creatine mapping of the brain at 3T by CEST MRI. Magn Reson Med 2024; 91:51-60. [PMID: 37814487 PMCID: PMC10843037 DOI: 10.1002/mrm.29876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE To assess the feasibility of CEST-based creatine (Cr) mapping in brain at 3T using the guanidino (Guan) proton resonance. METHODS Wild type and knockout mice with guanidinoacetate N-methyltransferase deficiency and low Cr and phosphocreatine (PCr) concentrations in the brain were used to assign the Cr and protein-based arginine contributions to the GuanCEST signal at 2.0 ppm. To quantify the Cr proton exchange rate, two-step Bloch-McConnell fitting was used to fit the extracted CrCEST line-shape and multi-B1 Z-spectral data. The pH response of GuanCEST was simulated to demonstrate its potential for pH mapping. RESULTS Brain Z-spectra of wild type and guanidinoacetate N-methyltransferase deficiency mice show a clear Guan proton peak at 2.0 ppm at 3T. The CrCEST signal contributes ∼23% to the GuanCEST signal at B1 = 0.8 μT, where a maximum CrCEST effect of 0.007 was detected. An exchange rate range of 200-300 s-1 was estimated for the Cr Guan protons. As revealed by the simulation, an elevated GuanCEST in the brain is observed when B1 is less than 0.4 μT at 3T, when intracellular pH reduces by 0.2. Conversely, the GuanCEST decreases when B1 is greater than 0.4 μT with the same pH drop. CONCLUSIONS CrCEST mapping is possible at 3T, which has potential for detecting intracellular pH and Cr concentration in brain.
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Affiliation(s)
- Kexin Wang
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jianpan Huang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Licheng Ju
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Su Xu
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rao P Gullapalli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yajie Liang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joshua Rogers
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yuguo Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter C. M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert G. Weiss
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kannie W. Y. Chan
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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10
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Chen X, Wu J, Yang Y, Chen H, Zhou Y, Lin L, Wei Z, Xu J, Chen Z, Chen L. Boosting quantification accuracy of chemical exchange saturation transfer MRI with a spatial-spectral redundancy-based denoising method. NMR IN BIOMEDICINE 2024; 37:e5027. [PMID: 37644611 DOI: 10.1002/nbm.5027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 07/14/2023] [Accepted: 07/27/2023] [Indexed: 08/31/2023]
Abstract
Chemical exchange saturation transfer (CEST) is a versatile technique that enables noninvasive detections of endogenous metabolites present in low concentrations in living tissue. However, CEST imaging suffers from an inherently low signal-to-noise ratio (SNR) due to the decreased water signal caused by the transfer of saturated spins. This limitation challenges the accuracy and reliability of quantification in CEST imaging. In this study, a novel spatial-spectral denoising method, called BOOST (suBspace denoising with nOnlocal lOw-rank constraint and Spectral local-smooThness regularization), was proposed to enhance the SNR of CEST images and boost quantification accuracy. More precisely, our method initially decomposes the noisy CEST images into a low-dimensional subspace by leveraging the global spectral low-rank prior. Subsequently, a spatial nonlocal self-similarity prior is applied to the subspace-based images. Simultaneously, the spectral local-smoothness property of Z-spectra is incorporated by imposing a weighted spectral total variation constraint. The efficiency and robustness of BOOST were validated in various scenarios, including numerical simulations and preclinical and clinical conditions, spanning magnetic field strengths from 3.0 to 11.7 T. The results demonstrated that BOOST outperforms state-of-the-art algorithms in terms of noise elimination. As a cost-effective and widely available post-processing method, BOOST can be easily integrated into existing CEST protocols, consequently promoting accuracy and reliability in detecting subtle CEST effects.
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Affiliation(s)
- Xinran Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Jian Wu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Yu Yang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Huan Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Yang Zhou
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Liangjie Lin
- Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Zhiliang Wei
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jiadi Xu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
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11
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Chen H, Chen X, Lin L, Cai S, Cai C, Chen Z, Xu J, Chen L. Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network. Magn Reson Med 2023; 90:2071-2088. [PMID: 37332198 DOI: 10.1002/mrm.29763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023]
Abstract
PURPOSE To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising. METHODS DECENT is composed of two parallel pathways with different convolution kernel sizes aiming to extract the global and spectral features embedded in CEST images. Each pathway consists of a modified U-Net with residual Encoder-Decoder network and 3D convolution. Fusion pathway with 1 × 1 × 1 convolution kernel is utilized to concatenate two parallel pathways, and the output of DECENT is noise-reduced CEST images. The performance of DECENT was validated in numerical simulations, egg white phantom experiments, and ischemic mouse brain and human skeletal muscle experiments in comparison with existing state-of-the-art denoising methods. RESULTS Rician noise was added to CEST images to mimic a low SNR situation for numerical simulation, egg white phantom experiment, and mouse brain experiments, while human skeletal muscle experiments were of inherently low SNR. From the denoising results evaluated by peak SNR (PSNR) and structural similarity index (SSIM), the proposed deep learning-based denoising method (DECENT) can achieve better performance compared to existing CEST denoising methods such as NLmCED, MLSVD, and BM4D, avoiding complicated parameter tuning or time-consuming iterative processes. CONCLUSIONS DECENT can well exploit the prior spatiotemporal correlation knowledge of CEST images and restore the noise-free images from their noisy observations, outperforming state-of-the-art denoising methods.
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Affiliation(s)
- Huan Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Xinran Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Liangjie Lin
- MSC Clinical & Technical Solutions, Philips Healthcare, Beijing, China
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Congbo Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Jiadi Xu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
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12
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Radke KL, Kamp B, Adriaenssens V, Stabinska J, Gallinnis P, Wittsack HJ, Antoch G, Müller-Lutz A. Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging. Diagnostics (Basel) 2023; 13:3326. [PMID: 37958222 PMCID: PMC10650582 DOI: 10.3390/diagnostics13213326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/18/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch-McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation.
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Affiliation(s)
- Karl Ludger Radke
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225 Dusseldorf, Germany (G.A.); (A.M.-L.)
| | - Benedikt Kamp
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225 Dusseldorf, Germany (G.A.); (A.M.-L.)
| | - Vibhu Adriaenssens
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225 Dusseldorf, Germany (G.A.); (A.M.-L.)
| | - Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA
- Division of MR Research, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Patrik Gallinnis
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225 Dusseldorf, Germany (G.A.); (A.M.-L.)
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225 Dusseldorf, Germany (G.A.); (A.M.-L.)
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225 Dusseldorf, Germany (G.A.); (A.M.-L.)
| | - Anja Müller-Lutz
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225 Dusseldorf, Germany (G.A.); (A.M.-L.)
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13
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Chen LM, Wang F, Mishra A, Yang PF, Sengupta A, Reed JL, Gore JC. Longitudinal multiparametric MRI of traumatic spinal cord injury in animal models. Magn Reson Imaging 2023; 102:184-200. [PMID: 37343904 PMCID: PMC10528214 DOI: 10.1016/j.mri.2023.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/14/2023] [Accepted: 06/17/2023] [Indexed: 06/23/2023]
Abstract
Multi-parametric MRI (mpMRI) technology enables non-invasive and quantitative assessments of the structural, molecular, and functional characteristics of various neurological diseases. Despite the recognized importance of studying spinal cord pathology, mpMRI applications in spinal cord research have been somewhat limited, partly due to technical challenges associated with spine imaging. However, advances in imaging techniques and improved image quality now allow longitudinal investigations of a comprehensive range of spinal cord pathological features by exploiting different endogenous MRI contrasts. This review summarizes the use of mpMRI techniques including blood oxygenation level-dependent (BOLD) functional MRI (fMRI), diffusion tensor imaging (DTI), quantitative magnetization transfer (qMT), and chemical exchange saturation transfer (CEST) MRI in monitoring different aspects of spinal cord pathology. These aspects include cyst formation and axonal disruption, demyelination and remyelination, changes in the excitability of spinal grey matter and the integrity of intrinsic functional circuits, and non-specific molecular changes associated with secondary injury and neuroinflammation. These approaches are illustrated with reference to a nonhuman primate (NHP) model of traumatic cervical spinal cord injuries (SCI). We highlight the benefits of using NHP SCI models to guide future studies of human spinal cord pathology, and demonstrate how mpMRI can capture distinctive features of spinal cord pathology that were previously inaccessible. Furthermore, the development of mechanism-based MRI biomarkers from mpMRI studies can provide clinically useful imaging indices for understanding the mechanisms by which injured spinal cords progress and repair. These biomarkers can assist in the diagnosis, prognosis, and evaluation of therapies for SCI patients, potentially leading to improved outcomes.
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Affiliation(s)
- Li Min Chen
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Feng Wang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Arabinda Mishra
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Pai-Feng Yang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anirban Sengupta
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jamie L Reed
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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14
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Zhao Y, Sun C, Zu Z. Isolation of amide proton transfer effect and relayed nuclear Overhauser enhancement effect at -3.5ppm using CEST with double saturation powers. Magn Reson Med 2023; 90:1025-1040. [PMID: 37154382 PMCID: PMC10646838 DOI: 10.1002/mrm.29691] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 03/18/2023] [Accepted: 04/16/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE Quantifications of amide proton transfer (APT) and nuclear Overhauser enhancement (rNOE(-3.5)) mediated saturation transfer with high specificity are challenging because their signals measured in a Z-spectrum are overlapped with confounding signals from direct water saturation (DS), semi-solid magnetization transfer (MT), and CEST of fast-exchange pools. In this study, based on two canonical CEST acquisitions with double saturation powers (DSP), a new data-postprocessing method is proposed to specifically quantify the effects of APT and rNOE. METHODS For CEST imaging with relatively low saturation powers (ω 1 2 $$ {\upomega}_1^2 $$ ), both the fast-exchange CEST effect and the semi-solid MT effect roughly depend onω 1 2 $$ {\upomega}_1^2 $$ , whereas the slow-exchange APT/rNOE(-3.5) effect do not, which is exploited to isolate a part of the APT and rNOE effects from the confounding signals in this study. After a mathematical derivation for the establishment of the proposed method, numerical simulations based on Bloch equations are then performed to demonstrate its specificity to detections of the APT and rNOE effects. Finally, an in vivo validation of the proposed method is conducted using an animal tumor model at a 4.7 T MRI scanner. RESULTS The simulations show that DSP-CEST can quantify the effects of APT and rNOE and substantially eliminate the confounding signals. The in vivo experiments demonstrate that the proposed DSP-CEST method is feasible for the imaging of tumors. CONCLUSION The data-postprocessing method proposed in this study can quantify the APT and rNOE effects with considerably increased specificities and a reduced cost of imaging time.
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Affiliation(s)
- Yu Zhao
- Vanderbilt University Institute of Imaging Science, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Casey Sun
- Vanderbilt University Institute of Imaging Science, Nashville, US
- Department of Chemistry, University of Florida, Gainesville, US
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
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15
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Hoffmann E, Schache D, Höltke C, Soltwisch J, Niland S, Krähling T, Bergander K, Grewer M, Geyer C, Groeneweg L, Eble JA, Vogl T, Roth J, Heindel W, Maus B, Helfen A, Faber C, Wildgruber M, Gerwing M, Hoerr V. Multiparametric chemical exchange saturation transfer MRI detects metabolic changes in breast cancer following immunotherapy. J Transl Med 2023; 21:577. [PMID: 37641066 PMCID: PMC10463706 DOI: 10.1186/s12967-023-04451-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/19/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND With metabolic alterations of the tumor microenvironment (TME) contributing to cancer progression, metastatic spread and response to targeted therapies, non-invasive and repetitive imaging of tumor metabolism is of major importance. The purpose of this study was to investigate whether multiparametric chemical exchange saturation transfer magnetic resonance imaging (CEST-MRI) allows to detect differences in the metabolic profiles of the TME in murine breast cancer models with divergent degrees of malignancy and to assess their response to immunotherapy. METHODS Tumor characteristics of highly malignant 4T1 and low malignant 67NR murine breast cancer models were investigated, and their changes during tumor progression and immune checkpoint inhibitor (ICI) treatment were evaluated. For simultaneous analysis of different metabolites, multiparametric CEST-MRI with calculation of asymmetric magnetization transfer ratio (MTRasym) at 1.2 to 2.0 ppm for glucose-weighted, 2.0 ppm for creatine-weighted and 3.2 to 3.6 ppm for amide proton transfer- (APT-) weighted CEST contrast was conducted. Ex vivo validation of MRI results was achieved by 1H nuclear magnetic resonance spectroscopy, matrix-assisted laser desorption/ionization mass spectrometry imaging with laser postionization and immunohistochemistry. RESULTS During tumor progression, the two tumor models showed divergent trends for all examined CEST contrasts: While glucose- and APT-weighted CEST contrast decreased and creatine-weighted CEST contrast increased over time in the 4T1 model, 67NR tumors exhibited increased glucose- and APT-weighted CEST contrast during disease progression, accompanied by decreased creatine-weighted CEST contrast. Already three days after treatment initiation, CEST contrasts captured response to ICI therapy in both tumor models. CONCLUSION Multiparametric CEST-MRI enables non-invasive assessment of metabolic signatures of the TME, allowing both for estimation of the degree of tumor malignancy and for assessment of early response to immune checkpoint inhibition.
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Affiliation(s)
- Emily Hoffmann
- Clinic of Radiology, University of Münster, Münster, Germany.
| | - Daniel Schache
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Carsten Höltke
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Jens Soltwisch
- Institute of Hygiene, University of Münster, Münster, Germany
| | - Stephan Niland
- Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, Münster, Germany
| | - Tobias Krähling
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Klaus Bergander
- Institute of Organic Chemistry, University of Münster, Münster, Germany
| | - Martin Grewer
- Clinic of Radiology, University of Münster, Münster, Germany
| | | | - Linda Groeneweg
- Institute of Immunology, University of Münster, Münster, Germany
| | - Johannes A Eble
- Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, Münster, Germany
| | - Thomas Vogl
- Institute of Immunology, University of Münster, Münster, Germany
| | - Johannes Roth
- Institute of Immunology, University of Münster, Münster, Germany
| | - Walter Heindel
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Bastian Maus
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Anne Helfen
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Cornelius Faber
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Moritz Wildgruber
- Clinic of Radiology, University of Münster, Münster, Germany
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, Münster, Germany
| | - Verena Hoerr
- Clinic of Radiology, University of Münster, Münster, Germany
- Heart Center Bonn, Department of Internal Medicine II, University Hospital Bonn, Bonn, Germany
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16
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Zhang Z, Wang K, Park S, Li A, Li Y, Weiss R, Xu J. The exchange rate of creatine CEST in mouse brain. Magn Reson Med 2023; 90:373-384. [PMID: 37036030 PMCID: PMC11054327 DOI: 10.1002/mrm.29662] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/19/2023] [Accepted: 03/20/2023] [Indexed: 04/11/2023]
Abstract
PURPOSE To estimate the exchange rate of creatine (Cr) CEST and to evaluate the pH sensitivity of guanidinium (Guan) CEST in the mouse brain. METHODS Polynomial and Lorentzian line-shape fitting (PLOF) were implemented to extract the amine, amide, and Guan CEST signals from the brain Z-spectrum at 11.7T. Wild-type (WT) and knockout mice with the guanidinoacetate N-methyltransferase deficiency (GAMT-/- ) that have low Cr and phosphocreatine (PCr) concentrations in the brain were used to extract the CrCEST signal. To quantify the CrCEST exchange rate, a two-step Bloch-McConnell (BM) fitting was used to fit the CrCEST line-shape, B1 -dependent CrCEST, and the pH response with different B1 values. The pH in the brain cells was altered by hypercapnia to measure the pH sensitivity of GuanCEST. RESULTS Comparison between the Z-spectra of WT and GAMT-/- mice suggest that the CrCEST is between 20% and 25% of the GuanCEST in the Z-spectrum at 1.95 ppm between B1 = 0.8 and 2 μT. The CrCEST exchange rate was found to be around 240-480 s-1 in the mouse brain, which is significantly lower than that in solutions (∼1000 s-1 ). The hypercapnia study on the mouse brain revealed that CrCEST at B1 = 2 μT and amineCEST at B1 = 0.8 μT are highly sensitive to pH change in the WT mouse brain. CONCLUSIONS The in vivo CrCEST exchange rate is slow, and the acquisition parameters for the CrCEST should be adjusted accordingly. CrCEST is the major contribution to the opposite pH-dependence of GuanCEST signal under different conditions of B1 in the brain.
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Affiliation(s)
- Ziqin Zhang
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kexin Wang
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sooyeon Park
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Anna Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Yuguo Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert Weiss
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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17
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Zhang Y, Zu T, Liu R, Zhou J. Acquisition sequences and reconstruction methods for fast chemical exchange saturation transfer imaging. NMR IN BIOMEDICINE 2023; 36:e4699. [PMID: 35067987 DOI: 10.1002/nbm.4699] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/02/2022] [Accepted: 01/17/2022] [Indexed: 05/23/2023]
Abstract
Chemical exchange saturation transfer (CEST) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that has been developed and employed in numerous diseases. Based on the unique saturation transfer principle, a family of CEST-detectable biomolecules in vivo have been found capable of providing valuable diagnostic information. However, CEST MRI needs a relatively long scan time due to the common long saturation labeling module and typical acquisition of multiple frequency offsets and signal averages, limiting its widespread clinical applications. So far, a plethora of imaging schemes and techniques has been developed to accelerate CEST MRI. In this review, the key acquisition and reconstruction methods for fast CEST imaging are summarized from a practical and systematic point of view. The first acquisition sequence section describes the major development of saturation schemes, readout patterns, ultrafast z-spectroscopy, and saturation-editing techniques for rapid CEST imaging. The second reconstruction method section lists the important advances of parallel imaging, compressed sensing, sparsity in the z-spectrum, and algorithms beyond the Fourier transform for speeding up CEST MRI.
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Affiliation(s)
- Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tao Zu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruibin Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jinyuan Zhou
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
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Xu J, Chung JJ, Jin T. Chemical exchange saturation transfer imaging of creatine, phosphocreatine, and protein arginine residue in tissues. NMR IN BIOMEDICINE 2023; 36:e4671. [PMID: 34978371 PMCID: PMC9250548 DOI: 10.1002/nbm.4671] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/06/2021] [Accepted: 12/02/2021] [Indexed: 05/05/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI has become a promising technique to assay target proteins and metabolites through their exchangeable protons, noninvasively. The ubiquity of creatine (Cr) and phosphocreatine (PCr) due to their pivotal roles in energy homeostasis through the creatine phosphate pathway has made them prime targets for CEST in the diagnosis and monitoring of disease pathologies, particularly in tissues heavily dependent on the maintenance of rich energy reserves. Guanidinium CEST from protein arginine residues (i.e. arginine CEST) can also provide information about the protein profile in tissue. However, numerous obfuscating factors stand as obstacles to the specificity of arginine, Cr, and PCr imaging through CEST, such as semisolid magnetization transfer, fast chemical exchanges such as primary amines, and the effects of nuclear Overhauser enhancement from aromatic and amide protons. In this review, the specific exchange properties of protein arginine residues, Cr, and PCr, along with their validation, are discussed, including the considerations necessary to target and tune their signal effects through CEST imaging. Additionally, strategies that have been employed to enhance the specificity of these exchanges in CEST imaging are described, along with how they have opened up possible applications of protein arginine residues, Cr and PCr CEST imaging in the study and diagnosis of pathology. A clear understanding of the capabilities and caveats of using CEST to image these vital metabolites and mitigation strategies is crucial to expanding the possibilities of this promising technology.
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Affiliation(s)
- Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Julius Juhyun Chung
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Tao Jin
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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19
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Glang F, Fabian MS, German A, Khakzar KM, Mennecke A, Liebert A, Herz K, Liebig P, Kasper BS, Schmidt M, Zuazua E, Nagel AM, Laun FB, Dörfler A, Scheffler K, Zaiss M. Linear projection-based chemical exchange saturation transfer parameter estimation. NMR IN BIOMEDICINE 2023; 36:e4697. [PMID: 35067998 DOI: 10.1002/nbm.4697] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 12/14/2021] [Accepted: 01/15/2022] [Indexed: 05/23/2023]
Abstract
Isolated evaluation of multiparametric in vivo chemical exchange saturation transfer (CEST) MRI often requires complex computational processing for both correction of B0 and B1 inhomogeneity and contrast generation. For that, sufficiently densely sampled Z-spectra need to be acquired. The list of acquired frequency offsets largely determines the total CEST acquisition time, while potentially representing redundant information. In this work, a linear projection-based multiparametric CEST evaluation method is introduced that offers fast B0 and B1 inhomogeneity correction, contrast generation and feature selection for CEST data, enabling reduction of the overall measurement time. To that end, CEST data acquired at 7 T in six healthy subjects and in one brain tumor patient were conventionally evaluated by interpolation-based inhomogeneity correction and Lorentzian curve fitting. Linear regression was used to obtain coefficient vectors that directly map uncorrected data to corrected Lorentzian target parameters. L1-regularization was applied to find subsets of the originally acquired CEST measurements that still allow for such a linear projection mapping. The linear projection method allows fast and interpretable mapping from acquired raw data to contrast parameters of interest, generalizing from healthy subject training data to unseen healthy test data and to the tumor patient dataset. The L1-regularization method shows that a fraction of the acquired CEST measurements is sufficient to preserve tissue contrasts, offering up to a 2.8-fold reduction of scan time. Similar observations as for the 7-T data can be made for data from a clinical 3-T scanner. Being a fast and interpretable computation step, the proposed method is complementary to neural networks that have recently been employed for similar purposes. The scan time acceleration offered by the L1-regularization ("CEST-LASSO") constitutes a step towards better applicability of multiparametric CEST protocols in a clinical context.
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Affiliation(s)
- Felix Glang
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Moritz S Fabian
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Alexander German
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Katrin M Khakzar
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Angelika Mennecke
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Andrzej Liebert
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
| | - Kai Herz
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany
| | | | - Burkhard S Kasper
- Department of Neurology, University Clinic of Friedrich Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Manuel Schmidt
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Enrique Zuazua
- Department of Data Science, Friedrich-Alexander-Universität Erlangen, Erlangen, Germany
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
| | - Arnd Dörfler
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Klaus Scheffler
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
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20
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Wang K, Park S, Kamson DO, Li Y, Liu G, Xu J. Guanidinium and amide CEST mapping of human brain by high spectral resolution CEST at 3 T. Magn Reson Med 2023; 89:177-191. [PMID: 36063502 PMCID: PMC9617768 DOI: 10.1002/mrm.29440] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE To extract guanidinium (Guan) and amide CEST on the human brain at 3 T MRI with the high spectral resolution (HSR) CEST combined with the polynomial Lorentzian line-shape fitting (PLOF). METHODS Continuous wave (cw) turbo spin-echo (TSE) CEST was implemented to obtain the optimum saturation parameters. Both Guan and amide CEST peaks were extracted and quantified using the PLOF method. The NMR spectra on the egg white phantoms were acquired to reveal the fitting range and the contributions to the amide and GuanCEST. Two types of CEST approaches, including cw gradient- and spin-echo (cwGRASE) and steady state EPI (ssEPI), were implemented to acquire multi-slice HSR-CEST. RESULTS GuanCEST can be extracted with the PLOF method at 3 T, and the optimumB 1 = 0.6 μ T $$ {\mathrm{B}}_1=0.6\kern0.2em \upmu \mathrm{T} $$ was determined for GuanCEST in white matter (WM) and 1.0 μT in gray matter (GM). The optimum B1 = 0.8-1 μT was found for amideCEST. AmideCEST is lower in both WM and GM collected with ssEPI compared to those by cwGRASE (ssEPI = [1.27-1.63]%; cwGRASE = [2.19-2.25]%). The coefficients of variation (COV) of the amide and Guan CEST in both WM and GM for ssEPI (COV: 28.6-33.4%) are significantly higher than those of cwGRASE (COV: 8.6-18.8%). Completely different WM/GM contrasts for Guan and amide CEST were observed between ssEPI and cwGRASE. The amideCEST was found to have originated from the unstructured amide protons as suggested by the NMR spectrum of the unfolded proteins in egg white. CONCLUSION Guan and amide CEST mapping can be achieved by the HSR-CEST at 3 T combing with the PLOF method.
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Affiliation(s)
- Kexin Wang
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sooyeon Park
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - David Olayinka Kamson
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland, USA
| | - Yuguo Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanshu Liu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Zhang Y, He W, Chen F, Wu J, He Y, Xu Z. Denoise ultra-low-field 3D magnetic resonance images using a joint signal-image domain filter. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 344:107319. [PMID: 36332511 DOI: 10.1016/j.jmr.2022.107319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 09/17/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Ultra-low-field magnetic resonance imaging (MRI) could suffer from heavy uncorrelated noise, and its removal could be a critical post-processing task. As a primary source of interference, Gaussian noise could corrupt the sampled MR signal (k-space data), especially at lower B0 field strength. For this reason, we consider both signal and image domains by proposing a new joint filter characterized by a Kalman filter with linear prediction and a nonlocal mean filter with higher-order singular value decomposition (HOSVD) for denoising 3D MR data. The Kalman filter first attenuates the noise in k-space, and then its reconstruction images are used to guide HOSVD denoising process with exploring self-similarity among 3D structures. The clearer prefiltered images could also generate improved HOSVD learned bases used to transform the noise corrupted patch groups in the original MR data. The flexibility of proposed method is also demonstrated by integrating other k-space filters into the algorithm scheme. Experimental data includes simulated MR images with the varying noise level and real MR images obtained from our 50 mT MRI scanner. The results reveal that our method has a better noise-removal ability and introduces lesser unexpected artifacts than other related MRI denoising approaches.
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Affiliation(s)
- Yuxiang Zhang
- State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China
| | - Wei He
- State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China
| | - Fangge Chen
- State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China
| | - Jiamin Wu
- Shenzhen Academy of Aerospace Technology, Shenzhen, China; Harbin Institute of Technology, Harbin, China
| | - Yucheng He
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Zheng Xu
- State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China.
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22
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Liu Z, Yang Q, Luo H, Luo D, Qian L, Liu X, Zheng H, Sun PZ, Wu Y. Demonstration of fast and equilibrium human muscle creatine CEST imaging at 3 T. Magn Reson Med 2022; 88:322-331. [PMID: 35324024 DOI: 10.1002/mrm.29223] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/23/2022] [Accepted: 02/20/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE Creatine chemical exchange saturation transfer (CrCEST) MRI is used increasingly in muscle imaging. However, the CrCEST measurement depends on the RF saturation duration (Ts) and relaxation delay (Td), and it is challenging to compare the results of different scan parameters. Therefore, this study aims to evaluate the quasi-steady-state (QUASS) CrCEST MRI on clinical 3T scanners. METHODS T1 and CEST MRI scans of Ts/Td of 1 s/1 s and 2 s/2 s were obtained from a multi-compartment creatine phantom and 5 healthy volunteers. The CrCEST effect was quantified with asymmetry analysis in the phantom, whereas 5-pool Lorentzian fitting was applied to isolate creatine from phosphocreatine, amide proton transfer, combined magnetization transfer and nuclear Overhauser enhancement effects, and direct water saturation in four major muscle groups of the lower leg. The routine and QUASS CrCEST measurements were compared under two different imaging conditions. Paired Student's t-test was performed with p-values less than 0.05 considered statistically significant. RESULTS The phantom study showed a substantial influence of Ts/Td on the routine CrCEST quantification (p = 0.02), and such impact was mitigated with the QUASS algorithm (p = 0.20). The volunteer experiment showed that the routine CrCEST, amide proton transfer, and combined magnetization transfer and nuclear Overhauser enhancement effects increased significantly with Ts and Td (p < 0.05) and were significantly smaller than the corresponding QUASS indices (p < 0.01). In comparison, the QUASS CrCEST MRI showed little dependence on Ts and Td, indicating its robustness and accuracy. CONCLUSION The QUASS CrCEST MRI is feasible to provide fast and accurate muscle creatine imaging.
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Affiliation(s)
- Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Qian Yang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Honghong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Long Qian
- MR Research, GE Healthcare, Beijing, China
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Phillip Zhe Sun
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Yin Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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23
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Huang J, Lai JHC, Han X, Chen Z, Xiao P, Liu Y, Chen L, Xu J, Chan KWY. Sensitivity schemes for dynamic glucose-enhanced magnetic resonance imaging to detect glucose uptake and clearance in mouse brain at 3 T. NMR IN BIOMEDICINE 2022; 35:e4640. [PMID: 34750891 DOI: 10.1002/nbm.4640] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 08/30/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
We investigated three dynamic glucose-enhanced (DGE) MRI methods for sensitively monitoring glucose uptake and clearance in both brain parenchyma and cerebrospinal fluid (CSF) at clinical field strength (3 T). By comparing three sequences, namely, Carr-Purcell-Meiboom-Gill (CPMG), on-resonance variable delay multipulse (onVDMP), and on-resonance spin-lock (onSL), a high-sensitivity DGE MRI scheme with truncated multilinear singular value decomposition (MLSVD) denoising was proposed. The CPMG method showed the highest sensitivity in detecting the parenchymal DGE signal among the three methods, while both onVDMP and onSL were more robust for CSF DGE imaging. Here, onVDMP was applied for CSF imaging, as it displayed the best stability of the DGE results in this study. The truncated MLSVD denoising method was incorporated to further improve the sensitivity. The proposed DGE MRI scheme was examined in mouse brain with 50%/25%/12.5% w/w D-glucose injections. The results showed that this combination could detect DGE signal changes from the brain parenchyma and CSF with as low as a 12.5% w/w D-glucose injection. The proposed DGE MRI schemes could sensitively detect the glucose signal change from brain parenchyma and CSF after D-glucose injection at a clinically relevant concentration, demonstrating high potential for clinical translation.
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Affiliation(s)
- Jianpan Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Joseph H C Lai
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Xiongqi Han
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Zilin Chen
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Peng Xiao
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Yang Liu
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Lin Chen
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Jiadi Xu
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kannie W Y Chan
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Centre for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- City University of Hong Kong Shenzhen Research Institute, Shenzhen, China
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Zhu D, He B, Zhang M, Wan Y, Liu R, Wang L, Zhang Y, Li Y, Gao F. A Multimodal MR Imaging Study of the Effect of Hippocampal Damage on Affective and Cognitive Functions in a Rat Model of Chronic Exposure to a Plateau Environment. Neurochem Res 2022; 47:979-1000. [PMID: 34981302 PMCID: PMC8891211 DOI: 10.1007/s11064-021-03498-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/18/2021] [Accepted: 11/23/2021] [Indexed: 02/05/2023]
Abstract
Prolonged exposure to high altitudes above 2500 m above sea level (a.s.l.) can cause cognitive and behavioral dysfunctions. Herein, we sought to investigate the effects of chronic exposure to plateau hypoxia on the hippocampus in a rat model by using voxel-based morphometry, creatine chemical exchange saturation transfer (CrCEST) and dynamic contrast-enhanced MR imaging techniques. 58 healthy 4-week-old male rats were randomized into plateau hypoxia rats (H group) as the experimental group and plain rats (P group) as the control group. H group rats were transported from Chengdu (500 m a.s.l.), a city in a plateau located in southwestern China, to the Qinghai-Tibet Plateau (4250 m a.s.l.), Yushu, China, and then fed for 8 months there, while P group rats were fed in Chengdu (500 m a.s.l.), China. After 8 months of exposure to plateau hypoxia, open-field and elevated plus maze tests revealed that the anxiety-like behavior of the H group rats was more serious than that of the P group rats, and the Morris water maze test revealed impaired spatial memory function in the H group rats. Multimodal MR imaging analysis revealed a decreased volume of the regional gray matter, lower CrCEST contrast and higher transport coefficient Ktrans in the hippocampus compared with the P group rats. Further correlation analysis found associations of quantitative MRI parameters of the hippocampus with the behavioral performance of H group rats. In this study, we validated the viability of using noninvasive multimodal MR imaging techniques to evaluate the effects of chronic exposure to a plateau hypoxic environment on the hippocampus.
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Affiliation(s)
- Dongyong Zhu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Road, Chengdu, 610041, China
| | - Bo He
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Road, Chengdu, 610041, China
| | - Mengdi Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Road, Chengdu, 610041, China
| | - Yixuan Wan
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Road, Chengdu, 610041, China
| | - Ruibin Liu
- Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310030, China
| | - Lei Wang
- Molecular Imaging Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yi Zhang
- Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310030, China
| | - Yunqing Li
- Department of Anatomy and KK Leung Brain Research Centre, The Fourth Military Medical University, Xi'an, 710032, China
| | - Fabao Gao
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Road, Chengdu, 610041, China. .,Molecular Imaging Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
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25
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Han P, Cheema K, Lee HL, Zhou Z, Cao T, Ma S, Wang N, Han H, Christodoulou AG, Li D. Whole-brain steady-state CEST at 3 T using MR Multitasking. Magn Reson Med 2021; 87:2363-2371. [PMID: 34843114 DOI: 10.1002/mrm.29109] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/11/2021] [Accepted: 11/12/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To perform fast 3D steady-state CEST (ss-CEST) imaging using MR Multitasking. METHODS A continuous acquisition sequence with repetitive ss-CEST modules was developed. Each ss-CEST module contains a single-lobe Gaussian saturation pulse, followed by a spoiler gradient and eight FLASH readouts (one "training line" + seven "imaging lines"). Three-dimensional Cartesian encoding was used for k-space acquisition. Reconstructed CEST images were quantified with four-pool Lorentzian fitting. RESULTS Steady-state CEST with whole-brain coverage was performed in 5.6 s per saturation frequency offset at the spatial resolution of 1.7 × 1.7 × 3.0 mm3 . The total scan time was 5.5 min for 55 different frequency offsets. Quantitative CEST maps from multipool fitting showed consistent image quality across the volume. CONCLUSION Three-dimensional ss-CEST with whole-brain coverage can be done at 3 T within 5.5 min using MR Multitasking.
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Affiliation(s)
- Pei Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Karandeep Cheema
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Hsu-Lei Lee
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Zhengwei Zhou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Anthony G Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
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Capozza M, Anemone A, Dhakan C, Della Peruta M, Bracesco M, Zullino S, Villano D, Terreno E, Longo DL, Aime S. GlucoCEST MRI for the Evaluation Response to Chemotherapeutic and Metabolic Treatments in a Murine Triple-Negative Breast Cancer: A Comparison with[ 18F]F-FDG-PET. Mol Imaging Biol 2021; 24:126-134. [PMID: 34383241 DOI: 10.1007/s11307-021-01637-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/30/2021] [Accepted: 07/28/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Triple-negative breast cancer (TNBC) patients have usually poor outcome after chemotherapy and early prediction of therapeutic response would be helpful. [18F]F-FDG-PET/CT acquisitions are often carried out to monitor variation in metabolic activity associated with response to the therapy, despite moderate accuracy and radiation exposure limit its application. The glucoCEST technique relies on the use of unlabelled D-glucose to assess glucose uptake with conventional MRI scanners and is currently under active investigations at clinical level. This work aims at validating the potential of MRI-glucoCEST in monitoring the therapeutic responses in a TNBC tumor murine model. PROCEDURES Breast tumor (4T1)-bearing mice were treated with doxorubicin or dichloroacetate for 1 week. PET/CT with [18F]F-FDG and MRI-glucoCEST were performed at baseline and after 3 cycles of treatment. Metabolic changes measured with [18F]F-FDG-PET and glucoCEST were compared and evaluated with changes in tumor volumes. RESULTS Doxorubicin-treated mice showed a significant decrease in tumor growth when compared to the control group. GlucoCEST imaging provided metabolic response after three cycles of treatment. Conversely, no variations were detected in [18F]F-FDG uptake. Dichloroacetate-treated mice did not show any decrease either in tumor volume or in tumor metabolic activity as assessed by both glucoCEST and [18F]F-FDG-PET. CONCLUSIONS Metabolic changes during doxorubicin treatment can be predicted by glucoCEST imaging that appears more sensitive than [18F]F-FDG-PET in reporting on therapeutic response. These findings support the view that glucoCEST may be a sensitive technique for monitoring metabolic response, but future studies are needed to explore the accuracy of this approach in other tumor types and treatments.
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Affiliation(s)
- Martina Capozza
- Center for Preclinical Imaging, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy
| | - Annasofia Anemone
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy
| | - Chetan Dhakan
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Via Nizza 52, Turin, 10126, Italy
| | - Melania Della Peruta
- Center for Preclinical Imaging, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy
| | - Martina Bracesco
- Center for Preclinical Imaging, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy
| | - Sara Zullino
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy
| | - Daisy Villano
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy
| | - Enzo Terreno
- Center for Preclinical Imaging, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy.,Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy.,Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Via Nizza 52, Turin, 10126, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Via Nizza 52, Turin, 10126, Italy
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza, 52, Turin, 10126, Italy.,Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Via Nizza 52, Turin, 10126, Italy
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Chen L, van Zijl PC, Wei Z, Lu H, Duan W, Wong PC, Li T, Xu J. Early detection of Alzheimer's disease using creatine chemical exchange saturation transfer magnetic resonance imaging. Neuroimage 2021; 236:118071. [PMID: 33878375 PMCID: PMC8321389 DOI: 10.1016/j.neuroimage.2021.118071] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 04/07/2021] [Accepted: 04/11/2021] [Indexed: 01/29/2023] Open
Abstract
Detecting Alzheimer's disease (AD) at an early stage brings a lot of benefits including disease management and actions to slow the progression of the disease. Here, we demonstrate that reduced creatine chemical exchange saturation transfer (CrCEST) contrast has the potential to serve as a new biomarker for early detection of AD. The results on wild type (WT) mice and two age-matched AD models, namely tauopathy (Tau) and Aβ amyloidosis (APP), indicated that CrCEST contrasts of the cortex and corpus callosum in the APP and Tau mice were significantly reduced compared to WT counterpart at an early stage (6-7 months) (p < 0.011). Two main causes of the reduced CrCEST contrast, i.e. cerebral pH and creatine concentration, were investigated. From phantom and hypercapnia experiments, CrCEST showed excellent sensitivity to pH variations. From MRS results, the creatine concentration in WT and AD mouse brain was equivalent, which suggests that the reduced CrCEST contrast was dominated by cerebral pH change involved in the progression of AD. Immunohistochemical analysis revealed that the abnormal cerebral pH in AD mice may relate to neuroinflammation, a known factor that can cause pH reduction.
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Affiliation(s)
- Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Peter C.M. van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zhiliang Wei
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hanzhang Lu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wenzhen Duan
- Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Philip C. Wong
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tong Li
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Sui R, Chen L, Li Y, Huang J, Chan KWY, Xu X, van Zijl PCM, Xu J. Whole-brain amide CEST imaging at 3T with a steady-state radial MRI acquisition. Magn Reson Med 2021; 86:893-906. [PMID: 33772859 DOI: 10.1002/mrm.28770] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/15/2021] [Accepted: 02/18/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop a steady-state saturation with radial readout chemical exchange saturation transfer (starCEST) for acquiring CEST images at 3 Tesla (T). The polynomial Lorentzian line-shape fitting approach was further developed for extracting amideCEST intensities at this field. METHOD StarCEST MRI using periodically rotated overlapping parallel lines with enhanced reconstruction-based spatial sampling was implemented to acquire Z-spectra that are robust to brain motion. Multi-linear singular value decomposition postprocessing was applied to enhance the CEST SNR. The egg white phantom studies were performed at 3T to reveal the contributions to the 3.5 ppm CEST signal. Based on the phantom validation, the amideCEST peak was quantified using the polynomial Lorentzian line-shape fitting, which exploits the inverse relationship between Z-spectral intensity and the longitudinal relaxation rate in the rotating frame. The 3D turbo spin echo CEST was also performed to compare with the starCEST method. RESULTS The amideCEST peak showed a negligible peak B1 dependence between 1.2 µT and 2.4 µT. The amideCEST images acquired with starCEST showed much improved image quality, SNR, and motion robustness compared to the conventional 3D turbo spin echo CEST method with the same scan time. The amideCEST contrast extracted by the polynomial Lorentzian line-shape fitting method trended toward a stronger gray matter signal (1.32% ± 0.30%) than white matter (0.92% ± 0.08%; P = .02, n = 5). When calculating the magnetization transfer contrast and T1 -corrected rotating frame relaxation rate maps, amideCEST again was not significantly different for white matter and gray matter. CONCLUSION Rapid multi-slice amideCEST mapping can be achieved by the starCEST method (< 5 min) at 3T by combing with the polynomial Lorentzian line-shape fitting method.
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Affiliation(s)
- Ran Sui
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA.,Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lin Chen
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA.,Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, People's Republic of China
| | - Yuguo Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA.,Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jianpan Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, People's Republic of China
| | - Kannie W Y Chan
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, People's Republic of China
| | - Xiang Xu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Peter C M van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA.,Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA.,Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Romdhane F, Villano D, Irrera P, Consolino L, Longo DL. Evaluation of a similarity anisotropic diffusion denoising approach for improving in vivo CEST-MRI tumor pH imaging. Magn Reson Med 2021; 85:3479-3496. [PMID: 33496986 DOI: 10.1002/mrm.28676] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 12/18/2020] [Accepted: 12/18/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE Chemical exchange saturation transfer MRI provides new approaches for investigating tumor microenvironment, including tumor acidosis that plays a key role in tumor progression and resistance to therapy. Following iopamidol injection, the detection of the contrast agent inside the tumor tissue allows measurements of tumor extracellular pH. However, accurate tumor pH quantifications are hampered by the low contrast efficiency of the CEST technique and by the low SNR of the acquired CEST images, hence in a reduced detectability of the injected agent. This work aims to investigate a novel denoising method for improving both tumor pH quantification and accuracy of CEST-MRI pH imaging. METHODS An hybrid denoising approach was investigated for CEST-MRI pH imaging based on the combination of the nonlocal mean filter and the anisotropic diffusion tensor method. The denoising approach was tested in simulated and in vitro data and compared with previously reported methods for CEST imaging and with established denoising approaches. Finally, it was validated with in vivo data to improve the accuracy of tumor pH maps. RESULTS The proposed method outperforms current denoising methods in CEST contrast quantification and detection of the administered contrast agent at several increasing noise levels with simulated data. In addition, it achieved a better pH quantification in in vitro data and demonstrated a marked improvement in contrast detection and a substantial improvement in tumor pH accuracy in in vivo data. CONCLUSION The proposed approach effectively reduces the noise in CEST images and increases the sensitivity detection in CEST-MRI pH imaging.
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Affiliation(s)
- Feriel Romdhane
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,National Engineering School of Tunis, University al Manar, Tunis, Tunisia
| | - Daisy Villano
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Pietro Irrera
- University of Campania "Luigi Vanvitelli,", Caserta, Italy.,Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Torino, Italy
| | - Lorena Consolino
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Torino, Italy
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