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Huemer M, Stilianu C, Maier O, Fabian MS, Schmidt M, Doerfler A, Bredies K, Zaiss M, Stollberger R. Improved quantification in CEST-MRI by joint spatial total generalized variation. Magn Reson Med 2024; 92:1683-1697. [PMID: 38703028 DOI: 10.1002/mrm.30129] [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: 10/17/2023] [Revised: 03/19/2024] [Accepted: 04/07/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE In this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool-Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal-to-noise ratio (SNR) was investigated. THEORY AND METHODS The joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel-wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss-Newton method combined with a Primal-Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal-means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values. RESULTS The proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities. CONCLUSION The proposed fitting method using TGV regularization leads to improved results over a range of different data-sets and noise levels. Furthermore, it can be applied to all Z-spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence.
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Affiliation(s)
- Markus Huemer
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
| | - Clemens Stilianu
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
| | - Oliver Maier
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
| | - Moritz Simon Fabian
- Institute of Neuroradiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - Manuel Schmidt
- Institute of Neuroradiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - Arnd Doerfler
- Institute of Neuroradiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - Kristian Bredies
- Department of Mathematics and Scientific Computing, University of Graz, Graz, Austria
- BioTechMed Graz, Graz, Austria
| | - Moritz Zaiss
- Institute of Neuroradiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
- High-Field Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rudolf Stollberger
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
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Heckel R, Jacob M, Chaudhari A, Perlman O, Shimron E. Deep learning for accelerated and robust MRI reconstruction. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01173-8. [PMID: 39042206 DOI: 10.1007/s10334-024-01173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/24/2024] [Accepted: 05/28/2024] [Indexed: 07/24/2024]
Abstract
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.Affiliations [3 and 6] has been split into two different affiliations. Please check if action taken is appropriate and amend if necessary.looks good.
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Affiliation(s)
- Reinhard Heckel
- Department of computer engineering, Technical University of Munich, Munich, Germany
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa, 52242, IA, USA
| | - Akshay Chaudhari
- Department of Radiology, Stanford University, Stanford, 94305, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Efrat Shimron
- Department of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, 3200004, Israel.
- Department of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, 3200004, Israel.
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Liu Q, Ning L, Shaik IA, Liao C, Gagoski B, Bilgic B, Grissom W, Nielsen JF, Zaitsev M, Rathi Y. Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI. Magn Reson Med 2024; 92:246-256. [PMID: 38469671 PMCID: PMC11055665 DOI: 10.1002/mrm.30062] [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: 12/07/2023] [Revised: 01/31/2024] [Accepted: 02/05/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform. METHODS We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). RESULTS Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences. CONCLUSION The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.
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Affiliation(s)
- Qiang Liu
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Lipeng Ning
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Imam Ahmed Shaik
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States
| | - Berkin Bilgic
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
| | - William Grissom
- Department of Biomedical Engineering, Case School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Jon-Fredrik Nielsen
- fMRI Laboratory and Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Yogesh Rathi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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Schüre JR, Weinmüller S, Kamm L, Herz K, Zaiss M. Sidebands in CEST MR-How to recognize and avoid them. Magn Reson Med 2024; 91:2391-2402. [PMID: 38317286 DOI: 10.1002/mrm.30011] [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: 10/18/2023] [Revised: 12/04/2023] [Accepted: 12/27/2023] [Indexed: 02/07/2024]
Abstract
PURPOSE Clinical scanners require pulsed CEST sequences to maintain amplifier and specific absorption rate limits. During off-resonant RF irradiation and interpulse delay, the magnetization can accumulate specific relative phases within the pulse train. In this work, we show that these phases are important to consider, as they can lead to unexpected artifacts when no interpulse gradient spoiling is performed during the saturation train. METHODS We investigated sideband artifacts using a CEST-3D snapshot gradient-echo sequence at 3 T. Initially, Bloch-McConnell simulations were carried out with Pulseq-CEST, while measurements were performed in vitro and in vivo. RESULTS Sidebands can be hidden in Z-spectra, and their structure becomes clearly visible only at high sampling. Sidebands are further influenced by B0 inhomogeneities and the RF phase cycling within the pulse train. In vivo, sidebands are mostly visible in liquid compartments such as CSF. Multi-pulse sidebands can be suppressed by interpulse gradient spoiling. CONCLUSION We provide new insights into sidebands occurring in pulsed CEST experiments and show that, similar as in imaging sequences, gradient and RF spoiling play an important role. Gradient spoiling avoids misinterpretations of sidebands as CEST effects especially in liquid environments including pathological tissue or for CEST resonances close to water. It is recommended to simulate pulsed CEST sequences in advance to avoid artifacts.
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Affiliation(s)
- Jan-Rüdiger Schüre
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Simon Weinmüller
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Lukas Kamm
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Kai Herz
- Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
| | - Moritz Zaiss
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Stilianu C, Graf C, Huemer M, Diwoky C, Soellradl M, Rund A, Zaiss M, Stollberger R. Enhanced and robust contrast in CEST MRI: Saturation pulse shape design via optimal control. Magn Reson Med 2024. [PMID: 38818538 DOI: 10.1002/mrm.30164] [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: 10/24/2023] [Revised: 04/10/2024] [Accepted: 05/07/2024] [Indexed: 06/01/2024]
Abstract
PURPOSE To employ optimal control for the numerical design of Chemical Exchange Saturation Transfer (CEST) saturation pulses to maximize contrast and stability againstB 0 $$ {\mathrm{B}}_0 $$ inhomogeneities. THEORY AND METHODS We applied an optimal control framework for the design pulse shapes for CEST saturation pulse trains. The cost functional minimized both the pulse energy and the discrepancy between the corresponding CEST spectrum and the target spectrum based on a continuous radiofrequency (RF) pulse. The optimization is subject to hardware limitations. In measurements on a 7 T preclinical scanner, the optimal control pulses were compared to continuous-wave and Gaussian saturation methods. We conducted a comparison of the optimal control pulses with Gaussian, block pulse trains, and adiabatic spin-lock pulses. RESULTS The optimal control pulse train demonstrated saturation levels comparable to continuous-wave saturation and surpassed Gaussian saturation by up to 50 % in phantom measurements. In phantom measurements at 3 T the optimized pulses not only showcased the highest CEST contrast, but also the highest stability against field inhomogeneities. In contrast, block pulse saturation resulted in severe artifacts. Dynamic Bloch-McConnell simulations were employed to identify the source of these artifacts, and underscore theB 0 $$ {\mathrm{B}}_0 $$ robustness of the optimized pulses. CONCLUSION In this work, it was shown that a substantial improvement in pulsed saturation CEST imaging can be achieved by using Optimal Control design principles. It is possible to overcome the sensitivity of saturation to B0 inhomogeneities while achieving CEST contrast close to continuous wave saturation.
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Affiliation(s)
- Clemens Stilianu
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
| | - Christina Graf
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
| | - Markus Huemer
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
| | - Clemens Diwoky
- Institute of Molecular Biosciences, University of Graz, Graz, Austria
| | - Martin Soellradl
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
- Department of Radiology and Radiological Sciences, Monash University, Melbourne, Australia
| | - Armin Rund
- Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Moritz Zaiss
- Institute of Neuroradiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
- High-Field Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Rudolf Stollberger
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
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Schache D, Peddi A, Nahardani A, Faber C, Hoerr V. Corrections for Rabi oscillations in cardiac chemical exchange saturation transfer MRI under the influence of very short preparation pulses. NMR IN BIOMEDICINE 2024; 37:e5081. [PMID: 38113906 DOI: 10.1002/nbm.5081] [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: 10/04/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023]
Abstract
Very short chemical exchange saturation transfer (CEST) pulses are beneficial in cardiac continuous wave (cw) CEST MRI, especially in small animals because of their rapid heartbeat; however, they result in signal modulations caused by Rabi oscillations. Therefore, we implemented two different filter techniques, DOwnsampling by SEparation of CEST spectrum into two parts (DOSE) and time domain (TD)-based filtering, to correct for these signal corruptions, allowing a reliable quantification of glucose-weighted CEST (glucoCEST) MRI contrast. In our study, cw CEST measurements were performed on a 9.4-T small animal BioSpec system using CEST pulses in the range of 10 to 200 ms. Experimental dependencies of Rabi oscillations on key MRI parameters were validated by Bloch-McConnell (BM) simulations. Filter efficiency was explored in a glucose concentration series as well as in the myocardium of healthy mice (n = 8), and glucoCEST contrast was subsequently quantified. The experimental results showed that the impact of Rabi oscillations on CEST spectra increased with decreasing CEST pulse length, optimized B0 homogeneity, and shorter T2 relaxation time, in accordance with results from BM simulations. Both investigated filter techniques reduced these signal modulations significantly, with DOSE filtering preserving the amplitude and TD filtering the spectral information of CEST data more accurately. Upon filter application, a significant decrease in glucoCEST contrast in the myocardium of healthy mice was observed after glucose infusion (pTD = 0.0079, pDOSE = 0.0044). To conclude, this study offers comprehensive experimental insights into Rabi oscillations within CEST MRI data along with methodological considerations that could be further advanced into a robust and precise cardiac cw CEST protocol by integrating DOSE and TD filtering into the standard CEST analysis pipeline.
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Affiliation(s)
- Daniel Schache
- Translational Research Imaging Center, Clinic of Radiology, University of Münster, Münster, Germany
| | - Ajay Peddi
- Translational Research Imaging Center, Clinic of Radiology, University of Münster, Münster, Germany
| | - Ali Nahardani
- Heart Center Bonn, Department of Internal Medicine II, University Hospital Bonn, Bonn, Germany
| | - Cornelius Faber
- Translational Research Imaging Center, Clinic of Radiology, University of Münster, Münster, Germany
| | - Verena Hoerr
- Translational Research Imaging Center, 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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Lee NG, Bauman G, Bieri O, Nayak KS. Replication of the bSTAR sequence and open-source implementation. Magn Reson Med 2024; 91:1464-1477. [PMID: 38044680 PMCID: PMC10872427 DOI: 10.1002/mrm.29947] [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: 07/02/2023] [Revised: 11/11/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE The reproducibility of scientific reports is crucial to advancing human knowledge. This paper is a summary of our experience in replicating a balanced SSFP half-radial dual-echo imaging technique (bSTAR) using open-source frameworks as a response to the 2023 ISMRM "repeat it with me" Challenge. METHODS We replicated the bSTAR technique for thoracic imaging at 0.55T. The bSTAR pulse sequence is implemented in Pulseq, a vendor neutral open-source rapid sequence prototyping environment. Image reconstruction is performed with the open-source Berkeley Advanced Reconstruction Toolbox (BART). The replication of bSTAR, termed open-source bSTAR, is tested by replicating several figures from the published literature. Original bSTAR, using the pulse sequence and image reconstruction developed by the original authors, and open-source bSTAR, with pulse sequence and image reconstruction developed in this work, were performed in healthy volunteers. RESULTS Both echo images obtained from open-source bSTAR contain no visible artifacts and show identical spatial resolution and image quality to those in the published literature. A direct head-to-head comparison between open-source bSTAR and original bSTAR on a healthy volunteer indicates that open-source bSTAR provides adequate SNR, spatial resolution, level of artifacts, and conspicuity of pulmonary vessels comparable to original bSTAR. CONCLUSION We have successfully replicated bSTAR lung imaging at 0.55T using two open-source frameworks. Full replication of a research method solely relying on information on a research paper is unfortunately rare in research, but our success gives greater confidence that a research methodology can be indeed replicated as described.
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Affiliation(s)
- Nam G. Lee
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Grzegorz Bauman
- Division of Radiological Physics, Department of Radiology, University of Basel Hospital, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Oliver Bieri
- Division of Radiological Physics, Department of Radiology, University of Basel Hospital, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Krishna S. Nayak
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, California, USA
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
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Schüre JR, Casagranda S, Sedykh M, Liebig P, Papageorgakis C, Mancini L, Bisdas S, Nichelli L, Pinter N, Mechtler L, Jafari R, Boddaert N, Dangouloff-Ros V, Poujol J, Schmidt M, Doerfler A, Zaiss M. Fluid suppression in amide proton transfer-weighted (APTw) CEST imaging: New theoretical insights and clinical benefits. Magn Reson Med 2024; 91:1354-1367. [PMID: 38073061 DOI: 10.1002/mrm.29915] [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: 06/07/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE Amide proton transfer-weighted (APTw) MRI at 3T provides a unique contrast for brain tumor imaging. However, APTw imaging suffers from hyperintensities in liquid compartments such as cystic or necrotic structures and provides a distorted APTw signal intensity. Recently, it has been shown that heuristically motivated fluid suppression can remove such artifacts and significantly improve the readability of APTw imaging. THEORY AND METHODS In this work, we show that the fluid suppression can actually be understood by the known concept of spillover dilution, which itself can be derived from the Bloch-McConnell equations in comparison to the heuristic approach. Therefore, we derive a novel post-processing formula that efficiently removes fluid artifact, and explains previous approaches. We demonstrate the utility of this APTw assessment in silico, in vitro, and in vivo in brain tumor patients acquired at MR scanners from different vendors. RESULTS Our results show a reduction of the CEST signals from fluid environments while keeping the APTw-CEST signal intensity almost unchanged for semi-solid tissue structures such as the contralateral normal appearing white matter. This further allows us to use the same color bar settings as for conventional APTw imaging. CONCLUSION Fluid suppression has considerable value in improving the readability of APTw maps in the neuro-oncological field. In this work, we derive a novel post-processing formula from the underlying Bloch-McConnell equations that efficiently removes fluid artifact, and explains previous approaches which justify the derivation of this metric from a theoretical point of view, to reassure the scientific and medical field about its use.
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Affiliation(s)
- Jan-Rüdiger Schüre
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefano Casagranda
- Department of R&D Advanced Applications, Olea Medical, La Ciotat, France
| | - Maria Sedykh
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | | | | | - Laura Mancini
- Lysholm Department of Neuroradiology, University College of London Hospitals NHS Foundation Trus, London, UK
- Institute of Neurology UCL, London, UK
| | - Sotirios Bisdas
- Lysholm Department of Neuroradiology, University College of London Hospitals NHS Foundation Trus, London, UK
- Institute of Neurology UCL, London, UK
| | - Lucia Nichelli
- Department of Neuroradiology, Sorbonne Université, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière-Charles Foix, Paris, France
| | - Nandor Pinter
- DENT Neurologic Institute, Buffalo, New York, USA
- Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, State University of New York, Buffalo, New York, USA
| | | | - Ramin Jafari
- Philips Healthcare, Cambridge, Massachusetts, USA
| | - Nathalie Boddaert
- Necker-Enfants Malades Hospital, AP-HP, Pediatric Radiology Department, Université Paris, Paris, France
- Imagine Institute, INSERM U1163, Université Paris cité, Paris, France
| | - Volodia Dangouloff-Ros
- Necker-Enfants Malades Hospital, AP-HP, Pediatric Radiology Department, Université Paris, Paris, France
- Imagine Institute, INSERM U1163, Université Paris cité, Paris, France
| | | | - Manuel Schmidt
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arnd Doerfler
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Moritz Zaiss
- Institute of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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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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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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11
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Nagar D, Vladimirov N, Farrar CT, Perlman O. Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals. Sci Rep 2023; 13:18291. [PMID: 37880343 PMCID: PMC10600114 DOI: 10.1038/s41598-023-45548-8] [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: 05/30/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023] Open
Abstract
Model-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of image contrast and served as a powerful tool for designing the imaging protocols that are now routinely used in the clinic. Recently, various advanced imaging techniques have relied on these models for image reconstruction, quantitative tissue parameter extraction, and automatic optimization of acquisition protocols. In molecular MRI, however, the increased complexity of the imaging scenario, where the signals from various chemical compounds and multiple proton pools must be accounted for, results in exceedingly long model simulation times, severely hindering the progress of this approach and its dissemination for various clinical applications. Here, we show that a deep-learning-based system can capture the nonlinear relations embedded in the molecular MRI Bloch-McConnell model, enabling a rapid and accurate generation of biologically realistic synthetic data. The applicability of this simulated data for in-silico, in-vitro, and in-vivo imaging applications is then demonstrated for chemical exchange saturation transfer (CEST) and semisolid macromolecule magnetization transfer (MT) analysis and quantification. The proposed approach yielded 63-99% acceleration in data synthesis time while retaining excellent agreement with the ground truth (Pearson's r > 0.99, p < 0.0001, normalized root mean square error < 3%).
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Affiliation(s)
- Dinor Nagar
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Nikita Vladimirov
- Department of Biomedical Engineering, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, 6997801, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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12
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Sedykh M, Liebig P, Herz K, Fabian MS, Mennecke A, Weinmüller S, Schmidt M, Dörfler A, Zaiss M. Snapshot CEST++: Advancing rapid whole-brain APTw-CEST MRI at 3 T. NMR IN BIOMEDICINE 2023; 36:e4955. [PMID: 37076984 DOI: 10.1002/nbm.4955] [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: 10/28/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 05/03/2023]
Abstract
APTw CEST MRI suffers from long preparation times and consequently long acquisition times (~5 min). Recently, a consensus on the preparation module for clinical APTw CEST at 3 T was found in the community, and we present a fast whole-brain APTw CEST MRI sequence following this consensus preparation of pulsed RF irradiation of 2 s duration at 90% RF duty-cycle and a B1,rms of 2 μT. After optimization of the snapshot CEST approach for APTw imaging regarding flip angle, voxel size and frequency offset sampling, we extend it by undersampled GRE acquisition and compressed sensing reconstruction. This allows 2 mm isotropic whole-brain APTw imaging for clinical research at 3 T below 2 min. With this sequence, a fast snapshot APTw imaging method is now available for larger clinical studies of brain tumors.
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Affiliation(s)
- Maria Sedykh
- Institute of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | - Kai Herz
- Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Moritz S Fabian
- Institute of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Angelika Mennecke
- Institute of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Simon Weinmüller
- Institute of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Manuel Schmidt
- Institute of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Arnd Dörfler
- Institute of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Moritz Zaiss
- Institute of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Magnetic Resonance Center, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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13
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Vinogradov E, Keupp J, Dimitrov IE, Seiler S, Pedrosa I. CEST-MRI for body oncologic imaging: are we there yet? NMR IN BIOMEDICINE 2023; 36:e4906. [PMID: 36640112 PMCID: PMC10200773 DOI: 10.1002/nbm.4906] [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: 03/02/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 05/23/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI has gained recognition as a valuable addition to the molecular imaging and quantitative biomarker arsenal, especially for characterization of brain tumors. There is also increasing interest in the use of CEST-MRI for applications beyond the brain. However, its translation to body oncology applications lags behind those in neuro-oncology. The slower migration of CEST-MRI to non-neurologic applications reflects the technical challenges inherent to imaging of the torso. In this review, we discuss the application of CEST-MRI to oncologic conditions of the breast and torso (i.e., body imaging), emphasizing the challenges and potential solutions to address them. While data are still limited, reported studies suggest that CEST signal is associated with important histology markers such as tumor grade, receptor status, and proliferation index, some of which are often associated with prognosis and response to therapy. However, further technical development is still needed to make CEST a reliable clinical application for body imaging and establish its role as a predictive and prognostic biomarker.
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Affiliation(s)
- Elena Vinogradov
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Ivan E Dimitrov
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Philips Healthcare, Gainesville, FL, USA
| | - Stephen Seiler
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
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14
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Mennecke A, Khakzar KM, German A, Herz K, Fabian MS, Liebert A, Blümcke I, Kasper BS, Nagel AM, Laun FB, Schmidt M, Winkler J, Dörfler A, Zaiss M. 7 tricks for 7 T CEST: Improving the reproducibility of multipool evaluation provides insights into the effects of age and the early stages of Parkinson's disease. NMR IN BIOMEDICINE 2023; 36:e4717. [PMID: 35194865 DOI: 10.1002/nbm.4717] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/10/2022] [Accepted: 02/21/2022] [Indexed: 05/23/2023]
Abstract
The objective of the current study was to optimize the postprocessing pipeline of 7 T chemical exchange saturation transfer (CEST) imaging for reproducibility and to prove this optimization for the detection of age differences and differences between patients with Parkinson's disease versus normal subjects. The following 7 T CEST MRI experiments were analyzed: repeated measurements of a healthy subject, subjects of two age cohorts (14 older, seven younger subjects), and measurements of 12 patients with Parkinson's disease. A slab-selective, B 1 + -homogeneous parallel transmit protocol was used. The postprocessing, consisting of motion correction, smoothing, B 0 -correction, normalization, denoising, B 1 + -correction and Lorentzian fitting, was optimized regarding the intrasubject and intersubject coefficient of variation (CoV) of the amplitudes of the amide pool and the aliphatic relayed nuclear Overhauser effect (rNOE) pool within the brain. Seven "tricks" for postprocessing accomplished an improvement of the mean voxel CoV of the amide pool and the aliphatic rNOE pool amplitudes of less than 5% and 3%, respectively. These postprocessing steps are: motion correction with interpolation of the motion of low-signal offsets (1) using the amide pool frequency offset image as reference (2), normalization of the Z-spectrum using the outermost saturated measurements (3), B 0 correction of the Z-spectrum with moderate spline smoothing (4), denoising using principal component analysis preserving the 11 highest intensity components (5), B 1 + correction using a linear fit (6) and Lorentzian fitting using the five-pool fit model (7). With the optimized postprocessing pipeline, a significant age effect in the amide pool can be detected. Additionally, for the first time, an aliphatic rNOE contrast between subjects with Parkinson's disease and age-matched healthy controls in the substantia nigra is detected. We propose an optimized postprocessing pipeline for CEST multipool evaluation. It is shown that by the use of these seven "tricks", the reproducibility and, thus, the statistical power of a CEST measurement, can be greatly improved and subtle changes can be detected.
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Affiliation(s)
- Angelika Mennecke
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Katrin M Khakzar
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Alexander German
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Kai Herz
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Moritz S Fabian
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Andrzej Liebert
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Ingmar Blümcke
- Institute of Neuropathology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Burkhard S Kasper
- Department of Neurology, Epilepsy Centre, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik B Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Manuel Schmidt
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jürgen Winkler
- Department of Neurology, Epilepsy Centre, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arnd Dörfler
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Moritz Zaiss
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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15
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Bie C, van Zijl P, Xu J, Song X, Yadav NN. Radiofrequency labeling strategies in chemical exchange saturation transfer MRI. NMR IN BIOMEDICINE 2023; 36:e4944. [PMID: 37002814 PMCID: PMC10312378 DOI: 10.1002/nbm.4944] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/19/2023] [Accepted: 03/27/2023] [Indexed: 05/23/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI has generated great interest for molecular imaging applications because it can image low-concentration solute molecules in vivo with enhanced sensitivity. CEST effects are detected indirectly through a reduction in the bulk water signal after repeated perturbation of the solute proton magnetization using one or more radiofrequency (RF) irradiation pulses. The parameters used for these RF pulses-frequency offset, duration, shape, strength, phase, and interpulse spacing-determine molecular specificity and detection sensitivity, thus their judicious selection is critical for successful CEST MRI scans. This review article describes the effects of applying RF pulses on spin systems and compares conventional saturation-based RF labeling with more recent excitation-based approaches that provide spectral editing capabilities for selectively detecting molecules of interest and obtaining maximal contrast.
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Affiliation(s)
- Chongxue Bie
- Department of Information Science and Technology, Northwest University, No.1 Xuefu Avenue, Xi’an, Shaanxi 710127 (China)
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N. Broadway, Baltimore MD 21205 (USA)
- The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, 720 Rutland Ave, Baltimore, MD 21205 (USA)
| | - Peter van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N. Broadway, Baltimore MD 21205 (USA)
- The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, 720 Rutland Ave, Baltimore, MD 21205 (USA)
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N. Broadway, Baltimore MD 21205 (USA)
- The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, 720 Rutland Ave, Baltimore, MD 21205 (USA)
| | - Xiaolei Song
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Haidian District, Beijing 100084 (China)
| | - Nirbhay N. Yadav
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 N. Broadway, Baltimore MD 21205 (USA)
- The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, 720 Rutland Ave, Baltimore, MD 21205 (USA)
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16
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Yadav NN, Xu J, Heo HY, van Zijl PCM. Special issue on chemical exchange saturation transfer MRI. NMR IN BIOMEDICINE 2023; 36:e4960. [PMID: 37182903 DOI: 10.1002/nbm.4960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Affiliation(s)
- Nirbhay N Yadav
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jiadi Xu
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- The Russell H. Morgan Department of Radiology, The 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
- The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Peter C M van Zijl
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- The Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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17
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Weigand-Whittier J, Sedykh M, Herz K, Coll-Font J, Foster AN, Gerstner ER, Nguyen C, Zaiss M, Farrar CT, Perlman O. Accelerated and quantitative three-dimensional molecular MRI using a generative adversarial network. Magn Reson Med 2023; 89:1901-1914. [PMID: 36585915 PMCID: PMC9992146 DOI: 10.1002/mrm.29574] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 12/13/2022] [Accepted: 12/15/2022] [Indexed: 01/01/2023]
Abstract
PURPOSE To substantially shorten the acquisition time required for quantitative three-dimensional (3D) chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) imaging and allow for rapid chemical exchange parameter map reconstruction. METHODS Three-dimensional CEST and MT magnetic resonance fingerprinting (MRF) datasets of L-arginine phantoms, whole-brains, and calf muscles from healthy volunteers, cancer patients, and cardiac patients were acquired using 3T clinical scanners at three different sites, using three different scanner models and coils. A saturation transfer-oriented generative adversarial network (GAN-ST) supervised framework was then designed and trained to learn the mapping from a reduced input data space to the quantitative exchange parameter space, while preserving perceptual and quantitative content. RESULTS The GAN-ST 3D acquisition time was 42-52 s, 70% shorter than CEST-MRF. The quantitative reconstruction of the entire brain took 0.8 s. An excellent agreement was observed between the ground truth and GAN-based L-arginine concentration and pH values (Pearson's r > 0.95, ICC > 0.88, NRMSE < 3%). GAN-ST images from a brain-tumor subject yielded a semi-solid volume fraction and exchange rate NRMSE of3 . 8 ± 1 . 3 % $$ 3.8\pm 1.3\% $$ and4 . 6 ± 1 . 3 % $$ 4.6\pm 1.3\% $$ , respectively, and SSIM of96 . 3 ± 1 . 6 % $$ 96.3\pm 1.6\% $$ and95 . 0 ± 2 . 4 % $$ 95.0\pm 2.4\% $$ , respectively. The mapping of the calf-muscle exchange parameters in a cardiac patient, yielded NRMSE < 7% and SSIM > 94% for the semi-solid exchange parameters. In regions with large susceptibility artifacts, GAN-ST has demonstrated improved performance and reduced noise compared to MRF. CONCLUSION GAN-ST can substantially reduce the acquisition time for quantitative semi-solid MT/CEST mapping, while retaining performance even when facing pathologies and scanner models that were not available during training.
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Affiliation(s)
- Jonah Weigand-Whittier
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Maria Sedykh
- Institute of Neuroradiology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - Kai Herz
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Jaume Coll-Font
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
- Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Anna N. Foster
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
- Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Elizabeth R. Gerstner
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts
| | - Christopher Nguyen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
- Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Charlestown, Massachusetts
- Health Science Technology, Harvard-MIT, Cambridge, Massachusetts
- Cardiovascular Innovation Research Center, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Moritz Zaiss
- Institute of Neuroradiology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Christian T. Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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18
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Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response. Int J Mol Sci 2023; 24:ijms24043151. [PMID: 36834563 PMCID: PMC9959624 DOI: 10.3390/ijms24043151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.
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19
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Yang Y, Wang C, Liu Y, Chen Z, Liu X, Zheng H, Liang D, Zhu Y. A robust adiabatic constant amplitude spin-lock preparation module for myocardial T 1ρ quantification at 3 T. NMR IN BIOMEDICINE 2023; 36:e4830. [PMID: 36093600 DOI: 10.1002/nbm.4830] [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: 05/14/2022] [Revised: 08/25/2022] [Accepted: 09/08/2022] [Indexed: 06/15/2023]
Abstract
T1ρ quantification has the potential to assess myocardial fibrosis without contrast agent. However, its preparation spin-lock pulse is sensitive to B1 and B0 inhomogeneities, resulting in severe banding artifacts in the heart region, especially at high magnetic field such as 3 T. We aimed to design a robust spin-lock (SL) preparation module that can be used in myocardial T1ρ quantification at 3 T. We used the tan/tanh pulse to tip up and tip down the magnetization in the spin-lock preparation module (tan/tanh-SL). Bloch simulation was used to optimize pulse shape parameters of the tan/tanh with a pulse duration (Tp ) of 8, 4, and 2 ms, respectively. The designed tan/tanh-SL modules were implemented on a 3-T MR scanner. They were evaluated in phantom studies under three different cases of B0 and B1 inhomogeneities, and tested in cardiac T1ρ quantification of healthy volunteers. The performance of the tan/tanh-SL was compared with the composite SL preparation pulses and the commonly used hyperbolic secant pulse for spin-lock (HS-SL). Feasible pulse shape parameters were obtained using Bloch simulation. Compared with HS-SL, the quantification error of tan/tanh-SL was reduced by 27.7% for Tp = 8 ms (mean ∆Q = 126.15 vs. 174.42) and 75.6% for Tp = 4 ms (mean ∆Q = 136.65 vs. 559.53). In the phantom study, tan/tanh-SL was less sensitive to B1 and B0 inhomogeneity compared with composite SL pulses and HS-SL. In cardiac T1ρ quantification, the T1ρ maps using tan/tanh-SL showed fewer banding artifacts than using composite SL pulses and HS-SL. The proposed tan/tanh-SL preparation module greatly improves the robustness to B0 and B1 field inhomogeneities and can be used in cardiac T1ρ quantification at 3 T.
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Affiliation(s)
- Yuxin Yang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Department of Biomedical Engineering, Chongqing University of Technology, Chongqing, China
| | - Che Wang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Department of Biomedical Engineering, Chongqing University of Technology, Chongqing, China
| | - Yuanyuan Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Zhongmin Chen
- Department of Biomedical Engineering, Chongqing University of Technology, Chongqing, China
| | - Xin Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Dong Liang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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20
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Zaiss M, Jin T, Kim SG, Gochberg DF. Theory of chemical exchange saturation transfer MRI in the context of different magnetic fields. NMR IN BIOMEDICINE 2022; 35:e4789. [PMID: 35704180 DOI: 10.1002/nbm.4789] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 05/31/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a versatile MRI method that provides contrast based on the level of molecular and metabolic activity. This contrast arises from indirect measurement of protons in low concentration molecules that are exchanging with the abundant water proton pool. The indirect measurement is based on magnetization transfer of radio frequency (rf)-prepared magnetization from the small pool to the water pool. The signal can be modeled by the Bloch-McConnell equations combining standard magnetization dynamics and chemical exchange processes. In this article, we review analytical solutions of the Bloch-McConnell equations and especially the derived CEST signal equations and their implications. The analytical solutions give direct insight into the dependency of measurable CEST effects on underlying parameters such as the exchange rate and concentration of the solute pools, but also on the system parameters such as the rf irradiation field B1 , as well as the static magnetic field B0 . These theoretical field-strength dependencies and their influence on sequence design are highlighted herein. In vivo results of different groups making use of these field-strength benefits/dependencies are reviewed and discussed.
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Affiliation(s)
- Moritz Zaiss
- High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Tao Jin
- NeuroImaging Laboratory, Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Daniel F Gochberg
- 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 Physics and Astronomy, Vanderbilt University, Nashville, Tennessee, USA
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21
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Glang F, Mueller S, Herz K, Loktyushin A, Scheffler K, Zaiss M. MR-double-zero - Proof-of-concept for a framework to autonomously discover MRI contrasts. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107237. [PMID: 35714389 DOI: 10.1016/j.jmr.2022.107237] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 04/02/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
PURPOSE A framework for supervised design of MR sequences for any given target contrast is proposed, based on fully automatic acquisition and reconstruction of MR data on a real MR scanner. The proposed method does not require any modeling of MR physics and thus allows even unknown contrast mechanisms to be addressed. METHODS A derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the MR scanner to acquire data. In each iteration, the acquired data are mapped to a given target contrast by linear regression. RESULTS It is shown that with the proposed framework it is possible to find an MR sequence that yields a predefined target contrast. In the present case, as a proof-of principle, a sequence mapping absolute creatine concentration, which cannot be extracted from T1 or T2-weighted scans directly, is discovered. The sequence was designed in a comparatively short time and with no human interaction. CONCLUSIONS New MR contrasts for mapping a given target can be discovered by derivative-free optimization of parametrized sequences that are directly executed on a real MRI scanner. This is demonstrated by 're-discovery' of a chemical exchange weighted sequence. The proposed method is considered to be a paradigm shift towards autonomous, model-free and target-driven sequence design.
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Affiliation(s)
- Felix Glang
- High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
| | - Sebastian Mueller
- High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany; Department of Biomedical Magnetic Resonance, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Kai Herz
- High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany; Department of Biomedical Magnetic Resonance, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Alexander Loktyushin
- High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
| | - Klaus Scheffler
- High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany; Department of Biomedical Magnetic Resonance, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Moritz Zaiss
- High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany; Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany.
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22
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Karakuzu A, Biswas L, Cohen-Adad J, Stikov N. Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI. Magn Reson Med 2022; 88:1212-1228. [PMID: 35657066 DOI: 10.1002/mrm.29292] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 12/20/2022]
Abstract
PURPOSE We developed an end-to-end workflow that starts with a vendor-neutral acquisition and tested the hypothesis that vendor-neutral sequences decrease inter-vendor variability of T1, magnetization transfer ratio (MTR), and magnetization transfer saturation-index (MTsat) measurements. METHODS We developed and deployed a vendor-neutral 3D spoiled gradient-echo (SPGR) sequence on three clinical scanners by two MRI vendors. We then acquired T1 maps on the ISMRM-NIST system phantom, as well as T1, MTR, and MTsat maps in three healthy participants. We performed hierarchical shift function analysis in vivo to characterize the differences between scanners when the vendor-neutral sequence is used instead of commercial vendor implementations. Inter-vendor deviations were compared for statistical significance to test the hypothesis. RESULTS In the phantom, the vendor-neutral sequence reduced inter-vendor differences from 8% to 19.4% to 0.2% to 5% with an overall accuracy improvement, reducing ground truth T1 deviations from 7% to 11% to 0.2% to 4%. In vivo, we found that the variability between vendors is significantly reduced (p = 0.015) for all maps (T1, MTR, and MTsat) using the vendor-neutral sequence. CONCLUSION We conclude that vendor-neutral workflows are feasible and compatible with clinical MRI scanners. The significant reduction of inter-vendor variability using vendor-neutral sequences has important implications for qMRI research and for the reliability of multicenter clinical trials.
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Affiliation(s)
- Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada.,Montréal Heart Institute, Montréal, Quebec, Canada
| | - Labonny Biswas
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada.,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, Quebec, Canada.,Mila - Quebec AI Institute, Montreal, Quebec, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada.,Montréal Heart Institute, Montréal, Quebec, Canada.,Center for Advanced Interdisciplinary Research, Ss. Cyril and Methodius University, Skopje, North Macedonia
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23
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Perlman O, Zhu B, Zaiss M, Rosen MS, Farrar CT. An end-to-end AI-based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST). Magn Reson Med 2022. [PMID: 35092076 DOI: 10.6084/m9.figshare.14877765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
PURPOSE To develop an automated machine-learning-based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols. METHODS An MR physics-governed AI system was trained to generate optimized acquisition schedules and the corresponding quantitative reconstruction neural network. The system (termed AutoCEST) is composed of a CEST saturation block, a spin dynamics module, and a deep reconstruction network, all differentiable and jointly connected. The method was validated using a variety of chemical exchange phantoms and in vivo mouse brains at 9.4T. RESULTS The acquisition times for AutoCEST optimized schedules ranged from 35 to 71 s, with a quantitative image reconstruction time of only 29 ms. The resulting exchangeable proton concentration maps for the phantoms were in good agreement with the known solute concentrations for AutoCEST sequences (mean absolute error = 2.42 mM; Pearson's r=0.992 , p<0.0001 ), but not for an unoptimized sequence (mean absolute error = 65.19 mM; Pearson's r=-0.161 , p=0.522 ). Similarly, improved exchange rate agreement was observed between AutoCEST and quantification of exchange using saturation power (QUESP) methods (mean absolute error: 35.8 Hz, Pearson's r=0.971 , p<0.0001 ) compared to an unoptimized schedule and QUESP (mean absolute error = 58.2 Hz; Pearson's r=0.959 , p<0.0001 ). The AutoCEST in vivo mouse brain semi-solid proton volume fractions were lower in the cortex (12.77% ± 0.75%) compared to the white matter (19.80% ± 0.50%), as expected. CONCLUSION AutoCEST can automatically generate optimized CEST/MT acquisition protocols that can be rapidly reconstructed into quantitative exchange parameter maps.
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Affiliation(s)
- Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Bo Zhu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute For Biological Cybernetics, Tübingen, Germany
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
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24
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Perlman O, Ito H, Herz K, Shono N, Nakashima H, Zaiss M, Chiocca EA, Cohen O, Rosen MS, Farrar CT. Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning. Nat Biomed Eng 2022; 6:648-657. [PMID: 34764440 PMCID: PMC9091056 DOI: 10.1038/s41551-021-00809-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 07/07/2021] [Indexed: 12/17/2022]
Abstract
Non-invasive imaging methods for detecting intratumoural viral spread and host responses to oncolytic virotherapy are either slow, lack specificity or require the use of radioactive or metal-based contrast agents. Here we show that in mice with glioblastoma multiforme, the early apoptotic responses to oncolytic virotherapy (characterized by decreased cytosolic pH and reduced protein synthesis) can be rapidly detected via chemical-exchange-saturation-transfer magnetic resonance fingerprinting (CEST-MRF) aided by deep learning. By leveraging a deep neural network trained with simulated magnetic resonance fingerprints, CEST-MRF can generate quantitative maps of intratumoural pH and of protein and lipid concentrations by selectively labelling the exchangeable amide protons of endogenous proteins and the exchangeable macromolecule protons of lipids, without requiring exogenous contrast agents. We also show that in a healthy volunteer, CEST-MRF yielded molecular parameters that are in good agreement with values from the literature. Deep-learning-aided CEST-MRF may also be amenable to the characterization of host responses to other cancer therapies and to the detection of cardiac and neurological pathologies.
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Affiliation(s)
- Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
| | - Hirotaka Ito
- Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kai Herz
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Naoyuki Shono
- Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Hiroshi Nakashima
- Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Neuroradiology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany
| | - E Antonio Chiocca
- Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ouri Cohen
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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25
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Zhou J, Zaiss M, Knutsson L, Sun PZ, Ahn SS, Aime S, Bachert P, Blakeley JO, Cai K, Chappell MA, Chen M, Gochberg DF, Goerke S, Heo HY, Jiang S, Jin T, Kim SG, Laterra J, Paech D, Pagel MD, Park JE, Reddy R, Sakata A, Sartoretti-Schefer S, Sherry AD, Smith SA, Stanisz GJ, Sundgren PC, Togao O, Vandsburger M, Wen Z, Wu Y, Zhang Y, Zhu W, Zu Z, van Zijl PCM. Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors. Magn Reson Med 2022; 88:546-574. [PMID: 35452155 PMCID: PMC9321891 DOI: 10.1002/mrm.29241] [Citation(s) in RCA: 73] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/26/2022] [Accepted: 03/02/2022] [Indexed: 12/16/2022]
Abstract
Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.
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Affiliation(s)
- Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Institute of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Linda Knutsson
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Medical Radiation Physics, Lund University, Lund, Sweden.,F.M. Kirby Research Center for Functional Brain Imaging, Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
| | - Phillip Zhe Sun
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, USA
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Peter Bachert
- Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.,Faculty of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany
| | - Jaishri O Blakeley
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kejia Cai
- Department of Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Michael A Chappell
- Mental Health and Clinical Neurosciences and Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Daniel F Gochberg
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Physics, Vanderbilt University, Nashville, Tennessee, USA
| | - Steffen Goerke
- Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tao Jin
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Seong-Gi Kim
- Center for Neuroscience Imaging Research, Institute for Basic Science and Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - John Laterra
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
| | - Daniel Paech
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany.,Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Mark D Pagel
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Ravinder Reddy
- Center for Advance Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | - A Dean Sherry
- Advanced Imaging Research Center and Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Department of Chemistry and Biochemistry, University of Texas at Dallas, Richardson, Texas, USA
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science (VUIIS), 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
| | - Greg J Stanisz
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Pia C Sundgren
- Department of Diagnostic Radiology/Clinical Sciences Lund, Lund University, Lund, Sweden.,Lund University Bioimaging Center, Lund University, Lund, Sweden.,Department of Medical Imaging and Physiology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | | | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yin Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Peter C M van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, USA
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26
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Booth TC, Wiegers EC, Warnert EAH, Schmainda KM, Riemer F, Nechifor RE, Keil VC, Hangel G, Figueiredo P, Álvarez-Torres MDM, Henriksen OM. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics. Front Oncol 2022; 11:811425. [PMID: 35340697 PMCID: PMC8948428 DOI: 10.3389/fonc.2021.811425] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/28/2021] [Indexed: 01/16/2023] Open
Abstract
Objective To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments. Methods The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging). Results The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application. Conclusion Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.
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Affiliation(s)
- Thomas C. Booth
- School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Ruben E. Nechifor
- Department of Clinical Psychology and Psychotherapy International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location VUmc, Amsterdam, Netherlands
| | - Gilbert Hangel
- Department of Neurosurgery & High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | - Patrícia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics - Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | | | - Otto M. Henriksen
- Department of Clinical Physiology, Nuclear medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
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27
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Molecular Imaging of Brain Tumors and Drug Delivery Using CEST MRI: Promises and Challenges. Pharmaceutics 2022; 14:pharmaceutics14020451. [PMID: 35214183 PMCID: PMC8880023 DOI: 10.3390/pharmaceutics14020451] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 12/10/2022] Open
Abstract
Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) detects molecules in their natural forms in a sensitive and non-invasive manner. This makes it a robust approach to assess brain tumors and related molecular alterations using endogenous molecules, such as proteins/peptides, and drugs approved for clinical use. In this review, we will discuss the promises of CEST MRI in the identification of tumors, tumor grading, detecting molecular alterations related to isocitrate dehydrogenase (IDH) and O-6-methylguanine-DNA methyltransferase (MGMT), assessment of treatment effects, and using multiple contrasts of CEST to develop theranostic approaches for cancer treatments. Promising applications include (i) using the CEST contrast of amide protons of proteins/peptides to detect brain tumors, such as glioblastoma multiforme (GBM) and low-grade gliomas; (ii) using multiple CEST contrasts for tumor stratification, and (iii) evaluation of the efficacy of drug delivery without the need of metallic or radioactive labels. These promising applications have raised enthusiasm, however, the use of CEST MRI is not trivial. CEST contrast depends on the pulse sequences, saturation parameters, methods used to analyze the CEST spectrum (i.e., Z-spectrum), and, importantly, how to interpret changes in CEST contrast and related molecular alterations in the brain. Emerging pulse sequence designs and data analysis approaches, including those assisted with deep learning, have enhanced the capability of CEST MRI in detecting molecules in brain tumors. CEST has become a specific marker for tumor grading and has the potential for prognosis and theranostics in brain tumors. With increasing understanding of the technical aspects and associated molecular alterations detected by CEST MRI, this young field is expected to have wide clinical applications in the near future.
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28
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Lingl JP, Wunderlich A, Goerke S, Paech D, Ladd ME, Liebig P, Pala A, Kim SY, Braun M, Schmitz BL, Beer M, Rosskopf J. The Value of APTw CEST MRI in Routine Clinical Assessment of Human Brain Tumor Patients at 3T. Diagnostics (Basel) 2022; 12:diagnostics12020490. [PMID: 35204583 PMCID: PMC8871436 DOI: 10.3390/diagnostics12020490] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 12/10/2022] Open
Abstract
Background. With fast-growing evidence in literature for clinical applications of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI), this prospective study aimed at applying amide proton transfer-weighted (APTw) CEST imaging in a clinical setting to assess its diagnostic potential in differentiation of intracranial tumors at 3 tesla (T). Methods. Using the asymmetry magnetization transfer ratio (MTRasym) analysis, CEST signals were quantitatively investigated in the tumor areas and in a similar sized region of the normal-appearing white matter (NAWM) on the contralateral hemisphere of 27 patients with intracranial tumors. Area under curve (AUC) analyses were used and results were compared to perfusion-weighted imaging (PWI). Results. Using APTw CEST, contrast-enhancing tumor areas showed significantly higher APTw CEST metrics than contralateral NAWM (AUC = 0.82; p < 0.01). In subgroup analyses of each tumor entity vs. NAWM, statistically significant effects were yielded for glioblastomas (AUC = 0.96; p < 0.01) and for meningiomas (AUC = 1.0; p < 0.01) but not for lymphomas as well as metastases (p > 0.05). PWI showed results comparable to APTw CEST in glioblastoma (p < 0.01). Conclusions. This prospective study confirmed the high diagnostic potential of APTw CEST imaging in a routine clinical setting to differentiate brain tumors.
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Affiliation(s)
- Julia P. Lingl
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Arthur Wunderlich
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Steffen Goerke
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (S.G.); (M.E.L.)
| | - Daniel Paech
- German Cancer Research Center (DKFZ), Division of Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;
- Department of Neuroradiology, Venusberg-Campus 1, Bonn University, 53127 Bonn, Germany
| | - Mark E. Ladd
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (S.G.); (M.E.L.)
- Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
- Faculty of Physics and Astronomy, University of Heidelberg, Im Neuenheimer Feld 226, 69120 Heidelberg, Germany
| | - Patrick Liebig
- Siemens Healthcare GmbH, Henkestraße 127, 91052 Erlangen, Germany;
| | - Andrej Pala
- Department of Neurosurgery, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany;
| | - Soung Yung Kim
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Michael Braun
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Bernd L. Schmitz
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
| | - Meinrad Beer
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
| | - Johannes Rosskopf
- Department of Radiology, Ulm University, Albert-Einstein-Allee 23, 89081 Ulm, Germany; (J.P.L.); (A.W.); (S.Y.K.); (M.B.); (B.L.S.); (M.B.)
- Section of Neuroradiology, Bezirkskrankenhaus Guenzburg, Lindenallee 2, 89312 Guenzburg, Germany
- Correspondence:
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Perlman O, Farrar CT, Heo HY. MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification. NMR IN BIOMEDICINE 2022; 36:e4710. [PMID: 35141967 PMCID: PMC9808671 DOI: 10.1002/nbm.4710] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/18/2022] [Accepted: 02/04/2022] [Indexed: 05/11/2023]
Abstract
Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast-weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep-learning algorithms have been developed to further boost the performance and speed of CEST and semi-solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST-MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)-based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data.
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Affiliation(s)
- Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Christian T. Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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30
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Tang PLY, Méndez Romero A, Jaspers JPM, Warnert EAH. The potential of advanced MR techniques for precision radiotherapy of glioblastoma. MAGMA (NEW YORK, N.Y.) 2022; 35:127-143. [PMID: 35129718 PMCID: PMC8901515 DOI: 10.1007/s10334-021-00997-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 06/14/2023]
Abstract
As microscopic tumour infiltration of glioblastomas is not visible on conventional magnetic resonance (MR) imaging, an isotropic expansion of 1-2 cm around the visible tumour is applied to define the clinical target volume for radiotherapy. An opportunity to visualize microscopic infiltration arises with advanced MR imaging. In this review, various advanced MR biomarkers are explored that could improve target volume delineation for radiotherapy of glioblastomas. Various physiological processes in glioblastomas can be visualized with different advanced MR techniques. Combining maps of oxygen metabolism (CMRO2), relative cerebral blood volume (rCBV), vessel size imaging (VSI), and apparent diffusion coefficient (ADC) or amide proton transfer (APT) can provide early information on tumour infiltration and high-risk regions of future recurrence. Oxygen consumption is increased 6 months prior to tumour progression being visible on conventional MR imaging. However, presence of the Warburg effect, marking a switch from an infiltrative to a proliferative phenotype, could result in CMRO2 to appear unaltered in high-risk regions. Including information on biomarkers representing angiogenesis (rCBV and VSI) and hypercellularity (ADC) or protein concentration (APT) can omit misinterpretation due to the Warburg effect. Future research should evaluate these biomarkers in radiotherapy planning to explore the potential of advanced MR techniques to personalize target volume delineation with the aim to improve local tumour control and/or reduce radiation-induced toxicity.
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Affiliation(s)
- Patrick L Y Tang
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands.
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
| | - Alejandra Méndez Romero
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Jaap P M Jaspers
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Esther A H Warnert
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
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Perlman O, Zhu B, Zaiss M, Rosen MS, Farrar CT. An end-to-end AI-based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST). Magn Reson Med 2022; 87:2792-2810. [PMID: 35092076 PMCID: PMC9305180 DOI: 10.1002/mrm.29173] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 01/04/2022] [Accepted: 01/07/2022] [Indexed: 12/28/2022]
Abstract
Purpose To develop an automated machine‐learning‐based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols. Methods An MR physics‐governed AI system was trained to generate optimized acquisition schedules and the corresponding quantitative reconstruction neural network. The system (termed AutoCEST) is composed of a CEST saturation block, a spin dynamics module, and a deep reconstruction network, all differentiable and jointly connected. The method was validated using a variety of chemical exchange phantoms and in vivo mouse brains at 9.4T. Results The acquisition times for AutoCEST optimized schedules ranged from 35 to 71 s, with a quantitative image reconstruction time of only 29 ms. The resulting exchangeable proton concentration maps for the phantoms were in good agreement with the known solute concentrations for AutoCEST sequences (mean absolute error = 2.42 mM; Pearson’s r=0.992, p<0.0001), but not for an unoptimized sequence (mean absolute error = 65.19 mM; Pearson’s r=‐0.161, p=0.522). Similarly, improved exchange rate agreement was observed between AutoCEST and quantification of exchange using saturation power (QUESP) methods (mean absolute error: 35.8 Hz, Pearson’s r=0.971, p<0.0001) compared to an unoptimized schedule and QUESP (mean absolute error = 58.2 Hz; Pearson’s r=0.959, p<0.0001). The AutoCEST in vivo mouse brain semi‐solid proton volume fractions were lower in the cortex (12.77% ± 0.75%) compared to the white matter (19.80% ± 0.50%), as expected. Conclusion AutoCEST can automatically generate optimized CEST/MT acquisition protocols that can be rapidly reconstructed into quantitative exchange parameter maps.
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Affiliation(s)
- Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Bo Zhu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute For Biological Cybernetics, Tübingen, Germany.,Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.,Department of Physics, Harvard University, Cambridge, MA, USA
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
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