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Jin T, Wang J, Chung J, Hitchens TK, Sun D, Mettenburg J, Wang P. Amide proton transfer MRI at 9.4 T for differentiating tissue acidosis in a rodent model of ischemic stroke. Magn Reson Med 2024. [PMID: 38923094 DOI: 10.1002/mrm.30194] [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: 11/28/2023] [Revised: 04/08/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024]
Abstract
PURPOSE Differentiating ischemic brain damage is critical for decision making in acute stroke treatment for better outcomes. We examined the sensitivity of amide proton transfer (APT) MRI, a pH-weighted imaging technique, to achieve this differentiation. METHODS In a rat stroke model, the ischemic core, oligemia, and the infarct-growth region (IGR) were identified by tracking the progression of the lesions. APT MRI signals were measured alongside ADC, T1, and T2 maps to evaluate their sensitivity in distinguishing ischemic tissues. Additionally, stroke under hyperglycemic conditions was studied. RESULTS The APT signal in the IGR decreased by about 10% shortly after stroke onset, and further decreased to 35% at 5 h, indicating a progression from mild to severe acidosis as the lesion evolved into infarction. Although ADC, T1, and T2 contrasts can only detect significant differences between the IGR and oligemia for a portion of the stroke duration, APT contrast consistently differentiates between them at all time points. However, the contrast to variation ratio at 1 h is only about 20% of the contrast to variation ratio between the core and normal tissues, indicating limited sensitivity. In the ischemic core, the APT signal decreases to about 45% and 33% of normal tissue level at 1 h for the normoglycemic and hyperglycemic groups, respectively, confirming more severe acidosis under hyperglycemia. CONCLUSION The sensitivity of APT MRI is high in detecting severe acidosis of the ischemic core but is much lower in detecting mild acidosis, which may affect the accuracy of differentiation between the IGR and oligemia.
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Affiliation(s)
- Tao Jin
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jicheng Wang
- Department of Urology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Julius Chung
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - T Kevin Hitchens
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Dandan Sun
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Joseph Mettenburg
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ping Wang
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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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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Wang K, Ju L, Song Y, Blair L, Xie K, Liu C, Li AM, Zhu D, Xu F, Liu G, Heo HY, Yadav NN, 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. [PMID: 38748853 DOI: 10.1002/mrm.30134] [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: 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, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Licheng Ju
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, 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, Maryland, USA
| | - Claire Liu
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Anna M Li
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Dan Zhu
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Feng Xu
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Guanshu Liu
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hye-Young Heo
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Nirbhay Narayan Yadav
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Richard A E Edden
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Qin Qin
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, 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, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Viswanathan M, Yin L, Kurmi Y, Zu Z. Machine learning-based amide proton transfer imaging using partially synthetic training data. Magn Reson Med 2024; 91:1908-1922. [PMID: 38098340 PMCID: PMC10955622 DOI: 10.1002/mrm.29970] [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: 08/12/2023] [Revised: 10/30/2023] [Accepted: 11/26/2023] [Indexed: 12/20/2023]
Abstract
PURPOSE Machine learning (ML) has been increasingly used to quantify CEST effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, whereas training with fully simulated data may introduce bias because of limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect. METHODS Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9 L tumors. RESULTS Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data. CONCLUSION Partially synthetic CEST data can address the challenges in conventional ML methods.
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Affiliation(s)
- Malvika Viswanathan
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Biomedical Engineering, Vanderbilt University, Nashville, US
| | - Leqi Yin
- School of Engineering, Vanderbilt University, Nashville, US
| | - Yashwant Kurmi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Biomedical Engineering, Vanderbilt University, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
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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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Viswanathan M, Kurmi Y, Zu Z. A rapid method for phosphocreatine-weighted imaging in muscle using double saturation power-chemical exchange saturation transfer. NMR IN BIOMEDICINE 2024; 37:e5089. [PMID: 38114069 DOI: 10.1002/nbm.5089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/21/2023]
Abstract
Monitoring the variation in phosphocreatine (PCr) levels following exercise provides valuable insights into muscle function. Chemical exchange saturation transfer (CEST) has emerged as a sensitive method with which to measure PCr levels in muscle, surpassing conventional MR spectroscopy. However, existing approaches for quantifying PCr CEST signals rely on time-consuming fitting methods that require the acquisition of the entire or a section of the CEST Z-spectrum. Additionally, traditional fitting methods often necessitate clear CEST peaks, which may be challenging to obtain at low magnetic fields. This paper evaluated the application of a new model-free method using double saturation power (DSP), termed DSP-CEST, to estimate the PCr CEST signal in muscle. The DSP-CEST method requires the acquisition of only two or a few CEST signals at the PCr frequency offset with two different saturation powers, enabling rapid dynamic imaging. Additionally, the DSP-CEST approach inherently eliminates confounding signals, offering enhanced robustness compared with fitting methods. Furthermore, DSP-CEST does not demand clear CEST peaks, making it suitable for low-field applications. We evaluated the capability of DSP-CEST to enhance the specificity of PCr CEST imaging through simulations and experiments on muscle tissue phantoms at 4.7 T. Furthermore, we applied DSP-CEST to animal leg muscle both before and after euthanasia and observed successful reduction of confounding signals. The DSP-CEST signal still has contaminations from a residual magnetization transfer (MT) effect and an aromatic nuclear Overhauser enhancement effect, and thus only provides a PCr-weighted imaging. The residual MT effect can be reduced by a subtraction of DSP-CEST signals at 2.6 and 5 ppm. Results show that the residual MT-corrected DSP-CEST signal at 2.6 ppm has significant variation in postmortem tissues. By contrast, both the CEST signal at 2.6 ppm and a conventional Lorentzian difference analysis of CEST signal at 2.6 ppm demonstrate no significant variation in postmortem tissues.
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Affiliation(s)
- Malvika Viswanathan
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - 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
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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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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Heo HY, Singh M, Yedavalli V, Jiang S, Zhou J. CEST and nuclear Overhauser enhancement imaging with deep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility and reliability studies. Magn Reson Med 2024; 91:1002-1015. [PMID: 38009996 PMCID: PMC10842109 DOI: 10.1002/mrm.29937] [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: 06/13/2023] [Revised: 10/18/2023] [Accepted: 11/04/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR. METHODS A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics. RESULTS The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 μT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema. CONCLUSION The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.
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Affiliation(s)
- Hye-Young Heo
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Munendra Singh
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Vivek Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Shanshan Jiang
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jinyuan Zhou
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
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Viswanathan M, Yin L, Kurmi Y, Zu Z. Amide Proton Transfer (APT) imaging in tumor with a machine learning approach using partially synthetic data. ARXIV 2023:arXiv:2311.01683v2. [PMID: 37961738 PMCID: PMC10635304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Purpose Machine learning (ML) has been increasingly used to quantify chemical exchange saturation transfer (CEST) effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, while training with fully simulated data may introduce bias due to limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect. Methods Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were generated by varying APT simulation parameters and applying scaling factors to adjust the measured components, achieving a balance between simulation flexibility and fidelity. First, tissue-mimicking CEST signals along with ground truth information were created using multiple-pool model simulations to validate this method. Second, an ML model was trained individually on partially synthetic data, in vivo data, and fully simulated data, to predict APT effect in rat brains bearing 9L tumors. Results Experiments on tissue-mimicking data suggest that the ML method using the partially synthetic data is accurate in predicting APT. In vivo experiments suggest that our method provides more accurate and robust prediction than the training using in vivo data and fully synthetic data. Conclusion Partially synthetic CEST data can address the challenges in conventional ML methods.
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Affiliation(s)
- Malvika Viswanathan
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Biomedical Engineering, Vanderbilt University, Nashville, US
| | - Leqi Yin
- School of Engineering, Vanderbilt University, Nashville, US
| | - Yashwant Kurmi
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Biomedical Engineering, Vanderbilt University, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
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