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Liu C, Li Z, Chen Z, Zhao B, Zheng Z, Song X. Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction. Magn Reson Med 2024; 92:688-701. [PMID: 38623899 DOI: 10.1002/mrm.30089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/31/2024] [Accepted: 03/02/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction. METHODS For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects. RESULTS In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones. CONCLUSION The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.
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Affiliation(s)
- Chuyu Liu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhongsen Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhensen Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Benqi Zhao
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Zhuozhao Zheng
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Xiaolei Song
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
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2
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Shaghaghi M, Damen FC, Li W, Tai LM, Cai K. Induced saturation transfer recovery steady states (iSTRESS) for proton exchange rate mapping with CEST MRI, a preliminary study. Magn Reson Imaging 2024; 109:264-270. [PMID: 38522624 DOI: 10.1016/j.mri.2024.03.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 03/26/2024]
Abstract
Proton exchange underpins essential mechanisms in diverse MR imaging contrasts. Omega plots have proven effective in mapping proton exchange rates (kex) in live human brains, enabling the differentiation of MS lesion activities and characterization of ischemic stroke. However, Omega plots require extended saturation durations (typically 5 to 10 s), resulting in high specific absorption rates (SAR) that can hinder clinical feasibility. In this study, we introduce a novel kex mapping approach, named induced Saturation Transfer Recovery Steady-States (iSTRESS). iSTRESS integrates an excitation flip angle pulse prior to chemical exchange saturation transfer (CEST) saturation, effectively aligning the magnetization with its steady-state value. This innovation reduces saturation times and mitigates SAR concerns. The formula for iSTRESS-based kex quantification was derived theoretically, involving two measurements with distinct excitation flip angles and saturation B1 values. Bloch-McConnell simulations confirmed that iSTRESS-based kex values closely matched input values (R2 > 0.99). An iSTRESS MRI sequence was implemented on a 9.4 T preclinical MRI, imaging protein phantoms with pH values ranging from 6.2 to 7.4 (n = 4). Z-spectra were acquired using excitation flip angles of 30° and 60°, followed by CEST saturation at powers of 30 and 120 Hz respectively, with a total saturation time of <1 s, resulting in two iSTRESS states for kex mapping. kex maps derived from the phantom study exhibited a linear correlation (R2 > 0.99) with Omega plot results. The developed iSTRESS method allows for kex quantification with significantly reduced saturation times, effectively minimizing SAR concerns.
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Affiliation(s)
- Mehran Shaghaghi
- Department of Radiology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Frederick C Damen
- Department of Radiology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Weiguo Li
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
| | - Leon M Tai
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Kejia Cai
- Department of Radiology, University of Illinois at Chicago, Chicago, IL 60612, USA; Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
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3
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Wu Q, Qi Y, Gong P, Huang B, Cheng G, Liang D, Zheng H, Sun PZ, Wu Y. Fast and robust pulsed chemical exchange saturation transfer (CEST) MRI using a quasi-steady-state (QUASS) algorithm at 3 T. Magn Reson Imaging 2024; 105:29-36. [PMID: 37898416 DOI: 10.1016/j.mri.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/21/2023] [Accepted: 10/22/2023] [Indexed: 10/30/2023]
Abstract
Chemical exchange saturation transfer (CEST) has emerged as a powerful technique to image dilute labile protons. However, its measurement depends on the RF saturation duration (Tsat) and relaxation delay (Trec). Although the recently developed quasi-steady-state (QUASS) solution can reconstruct equilibrium CEST effects under continuous-wave RF saturation, it does not apply to pulsed-CEST MRI on clinical scanners with restricted hardware or specific absorption rate limits. This study proposed a QUASS algorithm for pulsed-CEST MRI and evaluated its performance in muscle CEST measurement. An approximated expression of a steady-state pulsed-CEST signal was incorporated in the off-resonance spin-lock model, from which the QUASS pulsed-CEST effect was derived. Numerical simulation, creatine phantom, and healthy volunteer scans were conducted at 3 T. The CEST effect was quantified with asymmetry analysis in the simulation and phantom experiments. CEST effects of creatine, amide proton transfer, phosphocreatine, and combined magnetization transfer and nuclear Overhauser effects were isolated from a multi-pool Lorentzian model in muscles. Apparent and QUASS CEST measurements were compared under different Tsat/Trec and duty cycles. Paired Student's t-test was employed with P < 0.05 as statistically significant. The simulation, phantom, and human studies showed the strong impact of Tsat/Trec on apparent CEST measurements, which were significantly smaller than the corresponding QUASS CEST measures, especially under short Tsat/Trec times. In comparison, the QUASS algorithm mitigates such impact and enables accurate CEST measurements under short Tsat/Trec times. In conclusion, the QUASS algorithm can accelerate robust pulsed-CEST MRI, promising the efficient detection and evaluation of muscle diseases in clinical settings.
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Affiliation(s)
- Qiting Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Yulong Qi
- Department of Medical Imaging, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Pengcheng Gong
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Guanxun Cheng
- Department of Medical Imaging, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Phillip Zhe Sun
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Yin Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
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4
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Sun C, Zhao Y, Zu Z. Validation of the presence of fast exchanging amine CEST effect at low saturation powers and its influence on the quantification of APT. Magn Reson Med 2023; 90:1502-1517. [PMID: 37317709 PMCID: PMC10614282 DOI: 10.1002/mrm.29742] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 06/16/2023]
Abstract
PURPOSE Accurately quantifying the amide proton transfer (APT) effect and the underlying exchange parameters is crucial for its applications, but previous studies have reported conflicting results. In these quantifications, the CEST effect from the fast exchange amine was always ignored because it was considered weak with low saturation powers. This paper aims to evaluate the influence of the fast exchange amine CEST on the quantification of APT at low saturation powers. METHODS A quantification method with low and high saturation powers was used to distinguish APT from the fast exchange amine CEST effect. Simulations were conducted to assess the method's capability to separate APT from the fast exchange amine CEST effect. Animal experiments were performed to assess the relative contributions from the fast exchange amine and amide to CEST signals at 3.5 ppm. Three APT quantification methods, each with varying degrees of contamination from the fast exchange amine, were employed to process the animal data to assess the influence of the amine on the quantification of APT effect and the exchange parameters. RESULTS The relative size of the fast exchange amine CEST effect to APT effect gradually increases with increasing saturation power. At 9.4 T, it increases from approximately 20% to 40% of APT effect with a saturation power increase from 0.25 to 1 μT. CONCLUSION The fast exchange amine CEST effect leads overestimation of APT effect, fitted amide concentration, and amide-water exchange rate, potentially contributing to the conflicting results reported in previous studies.
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Affiliation(s)
- Casey Sun
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Chemistry, University of Florida, Gainesville, US
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, US
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, US
- Department of Biomedical Engineering, Vanderbilt University, Nashville, US
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Zhang Y, Zu T, Liu R, Zhou J. Acquisition sequences and reconstruction methods for fast chemical exchange saturation transfer imaging. NMR IN BIOMEDICINE 2023; 36:e4699. [PMID: 35067987 DOI: 10.1002/nbm.4699] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/02/2022] [Accepted: 01/17/2022] [Indexed: 05/23/2023]
Abstract
Chemical exchange saturation transfer (CEST) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that has been developed and employed in numerous diseases. Based on the unique saturation transfer principle, a family of CEST-detectable biomolecules in vivo have been found capable of providing valuable diagnostic information. However, CEST MRI needs a relatively long scan time due to the common long saturation labeling module and typical acquisition of multiple frequency offsets and signal averages, limiting its widespread clinical applications. So far, a plethora of imaging schemes and techniques has been developed to accelerate CEST MRI. In this review, the key acquisition and reconstruction methods for fast CEST imaging are summarized from a practical and systematic point of view. The first acquisition sequence section describes the major development of saturation schemes, readout patterns, ultrafast z-spectroscopy, and saturation-editing techniques for rapid CEST imaging. The second reconstruction method section lists the important advances of parallel imaging, compressed sensing, sparsity in the z-spectrum, and algorithms beyond the Fourier transform for speeding up CEST MRI.
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Affiliation(s)
- Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tao Zu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ruibin Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jinyuan Zhou
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
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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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7
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Zu T, Sun Y, Wu D, Zhang Y. Joint K-space and Image-space Parallel Imaging (KIPI) for accelerated chemical exchange saturation transfer acquisition. Magn Reson Med 2023; 89:922-936. [PMID: 36336741 DOI: 10.1002/mrm.29480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/25/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop an auto-calibrated technique by joint K-space and Image-space Parallel Imaging (KIPI) for accelerated CEST acquisition. THEORY AND METHODS The KIPI method selects a calibration frame with a low acceleration factor (AF) and auto-calibration signals (ACS) acquired, from which the coil sensitivity profiles and artifact correction maps are calculated after restoring the k-space by GRAPPA. Then the other frames with high AF and without ACS can be reconstructed by SENSE and artifact suppression. The signal leakage due to the T2 -decay filtering in k-space compromises the SENSE reconstruction, which can be corrected by the artifact suppression algorithm of KIPI. The 2D and 3D imaging experiments were done on the phantom, healthy volunteer, and brain tumor patient with a 3T scanner. RESULTS The proposed KIPI method was evaluated by retrospectively undersampled data with variable AFs and compared against existing parallel imaging methods (SENSE/auto, GRAPPA, and ESPIRiT). KIPI enabled CEST frames with random AFs to achieve similar image quality, eliminated the strong aliasing artifacts, and generated significantly smaller errors than the other methods (p < 0.01). The KIPI method permitted an AF up to 12-fold in both phase-encoding and slice-encoding directions for 3D CEST source images, achieving an overall 8.2-fold speedup in scan time. CONCLUSION KIPI is a novel auto-calibrated parallel imaging method that enables variable AFs for different CEST frames, achieves a significant reduction in scan time, and does not compromise the accuracy of CEST maps.
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Affiliation(s)
- Tao Zu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Sun
- MR Collaboration, Siemens Healthcare Ltd., Shanghai, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
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Vladimirov N, Perlman O. Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response. Int J Mol Sci 2023; 24:3151. [PMID: 36834563 PMCID: PMC9959624 DOI: 10.3390/ijms24043151] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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Affiliation(s)
- Nikita Vladimirov
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
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Chen Y, Dang X, Hu W, Sun Y, Bai Y, Wang X, He X, Wang M, Song X. Reassembled saturation transfer (REST) MR images at 2 B 1 values for in vivo exchange-dependent imaging of amide and nuclear Overhauser enhancement. Magn Reson Med 2023; 89:620-635. [PMID: 36253943 DOI: 10.1002/mrm.29471] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE Design an efficient CEST scheme for exchange-dependent images with high contrast-to-noise ratio. THEORY Reassembled saturation transfer (REST) signals were defined as Δ $$ \Delta $$ r.Z = r.Zref - r.ZCEST and the reassembled exchange-dependen magnetization transfer ratio r.MTRRex = r.1/Zref - r.1/ZCEST , utilizing the averages over loosely sampled reference frequency offsets as Zref and over densely sampled target offsets as ZCEST . Using r.MTRRex measured under 2 B1,sat values, exchange rate could be estimated. METHODS The REST approach was optimized and assessed quantitatively by simulations for various exchange rates, pool concentration, and water T1 . In vivo evaluation was performed on ischemic rat brains at 7 Tesla and human brains at 3 Tesla, in comparison with conventional asymmetrical analysis, Lorentzian difference (LD), an MTRRex_ LD. RESULTS For a broad choice of Δ ω ref $$ \Delta {\omega}_{ref} $$ ranges and numbers, Δr.Z and r.MTRRex exhibited comparable quantification features with conventional LD and MTRRex _LD, respectively, when B1,sat ≤ 1 μT. The subtraction of 2 REST values under distinct B1,sat values showed linear relationships with exchange rate and obtained immunity to field inhomogeneity and variation in MT and water T1 . For both rat and human studies, REST images exhibited similar contrast distribution to MTRRex _LD, with superiority in contrast-to-noise ratio and acquisition efficiency. Compared with MTRRex _LD, 2-B1,sat subtraction REST images displayed better resistance to B1 inhomogeneity, with more specific enhanced regions. They also showed higher signals for amide than for nuclear Overhauser enhancement effect in human brain, presumably reflecting the higher increment from faster-exchanging species as B1,sat increased. CONCLUSION Featuring high contrast-to-noise ratio efficiency, REST could be a practical exchange-dependent approach readily applicable to either retrospective Z-spectra analysis or perspective 6-offset acquisition.
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Affiliation(s)
- Yanrong Chen
- School of Information Sciences and Technology, Northwest University, Xi'an, People's Republic of China.,Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, People's Republic of China
| | - Xujian Dang
- School of Information Sciences and Technology, Northwest University, Xi'an, People's Republic of China
| | - Wanting Hu
- School of Information Sciences and Technology, Northwest University, Xi'an, People's Republic of China
| | - Yaozong Sun
- School of Information Sciences and Technology, Northwest University, Xi'an, People's Republic of China
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xiaoli Wang
- Department of Medical Imaging, Weifang Medical University, Weifang, People's Republic of China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, People's Republic of China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & the People's Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xiaolei Song
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, People's Republic of China
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10
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Cohen O, Yu VY, Tringale KR, Young RJ, Perlman O, Farrar CT, Otazo R. CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction. Magn Reson Med 2023; 89:233-249. [PMID: 36128888 PMCID: PMC9617776 DOI: 10.1002/mrm.29448] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 08/09/2022] [Accepted: 08/19/2022] [Indexed: 01/21/2023]
Abstract
PURPOSE To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction. METHODS A CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time under 2 min. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the six parameter DRONE reconstruction was tested in simulations using a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter and gray matter. The clinical utility of CEST-MRF was demonstrated on four patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core, and necrotic core regions and the CEST-MRF values compared to the contra-lateral side. RESULTS DRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean concordance correlation coefficient for all six parameters was 0.98 ± 0.01 in white matter and 0.98 ± 0.02 in gray matter. The CEST-MRF values in nearly all tumor regions were significantly different (P = 0.05) from each other and the contra-lateral side. CONCLUSION Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.
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Affiliation(s)
- Ouri Cohen
- Department of Medical PhysicsMemorial Sloan Kettering Cancer Center
New YorkNew YorkUSA
| | - Victoria Y. Yu
- Department of Medical PhysicsMemorial Sloan Kettering Cancer Center
New YorkNew YorkUSA
| | - Kathryn R. Tringale
- Department of Radiation OncologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Robert J. Young
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMassachusettsUSA
- Department of Biomedical EngineeringTel Aviv UniversityTel AvivIsrael
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Christian T. Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Ricardo Otazo
- Department of Medical PhysicsMemorial Sloan Kettering Cancer Center
New YorkNew YorkUSA
- Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
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11
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Jabehdar Maralani P, Chan RW, Lam WW, Oakden W, Oglesby R, Lau A, Mehrabian H, Heyn C, Chan AK, Soliman H, Sahgal A, Stanisz GJ. Chemical Exchange Saturation Transfer MRI: What Neuro-Oncology Clinicians Need To Know. Technol Cancer Res Treat 2023; 22:15330338231208613. [PMID: 37872686 PMCID: PMC10594966 DOI: 10.1177/15330338231208613] [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: 07/11/2023] [Revised: 09/19/2023] [Accepted: 09/28/2023] [Indexed: 10/25/2023] Open
Abstract
Chemical exchange saturation transfer (CEST) is a relatively novel magnetic resonance imaging (MRI) technique with an image contrast designed for in vivo measurement of certain endogenous molecules with protons that are exchangeable with water protons, such as amide proton transfer commonly used for neuro-oncology applications. Recent technological advances have made it feasible to implement CEST on clinical grade scanners within practical acquisition times, creating new opportunities to integrate CEST in clinical workflow. In addition, the majority of CEST applications used in neuro-oncology are performed without the use gadolinium-based contrast agents which are another appealing feature of this technique. This review is written for clinicians involved in neuro-oncologic care (nonphysicists) as the target audience explaining what they need to know as CEST makes its way into practice. The purpose of this article is to (1) review the basic physics and technical principles of CEST MRI, and (2) review the practical applications of CEST in neuro-oncology.
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Affiliation(s)
- Pejman Jabehdar Maralani
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Rachel W. Chan
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Wilfred W. Lam
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Wendy Oakden
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Ryan Oglesby
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Angus Lau
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Hatef Mehrabian
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Chris Heyn
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Aimee K.M. Chan
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Hany Soliman
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Greg J. Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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12
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Shaghaghi M, Cai K. Toward In Vivo MRI of the Tissue Proton Exchange Rate in Humans. BIOSENSORS 2022; 12:bios12100815. [PMID: 36290953 PMCID: PMC9599426 DOI: 10.3390/bios12100815] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 09/19/2022] [Accepted: 09/29/2022] [Indexed: 05/28/2023]
Abstract
Quantification of proton exchange rate (kex) is a challenge in MR studies. Current techniques either have low resolutions or are dependent on the estimation of parameters that are not measurable. The Omega plot method, on the other hand, provides a direct way for determining kex independent of the agent concentration. However, it cannot be used for in vivo studies without some modification due to the contributions from the water signal. In vivo tissue proton exchange rate (kex) MRI, based on the direct saturation (DS) removed Omega plot, quantifies the weighted average of kex of the endogenous tissue metabolites. This technique has been successfully employed for imaging the variation in the kex of ex vivo phantoms, as well as in vivo human brains in healthy subjects, and stroke or multiple sclerosis (MS) patients. In this paper, we present a brief review of the methods used for kex imaging with a focus on the development of in vivo kex MRI technique based on the DS-removed Omega plot. We then review the recent clinical studies utilizing this technique for better characterizing brain lesions. We also outline technical challenges for the presented technique and discuss its prospects for detecting tissue microenvironmental changes under oxidative stress.
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Affiliation(s)
- Mehran Shaghaghi
- Department of Radiology, University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Kejia Cai
- Department of Radiology, University of Illinois at Chicago, Chicago, IL 60612, USA
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
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13
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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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14
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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] [MESH Headings] [Grants] [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 RadiologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMAUSA
| | - Bo Zhu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMAUSA
| | - Moritz Zaiss
- Magnetic Resonance CenterMax Planck Institute For Biological CyberneticsTübingenGermany
- Department of NeuroradiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Matthew S. Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMAUSA
- Department of PhysicsHarvard UniversityCambridgeMAUSA
| | - Christian T. Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMAUSA
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15
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Kang B, Kim B, Park H, Heo HY. Learning-based optimization of acquisition schedule for magnetization transfer contrast MR fingerprinting. NMR IN BIOMEDICINE 2022; 35:e4662. [PMID: 34939236 PMCID: PMC9761585 DOI: 10.1002/nbm.4662] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/12/2021] [Accepted: 11/16/2021] [Indexed: 05/03/2023]
Abstract
Magnetization transfer contrast MR fingerprinting (MTC-MRF) is a novel quantitative imaging method that simultaneously quantifies free bulk water and semisolid macromolecule parameters using pseudo-randomized scan parameters. To improve acquisition efficiency and reconstruction accuracy, the optimization of MRF sequence design has been of recent interest in the MRF field, but has been challenging due to the large number of degrees of freedom to be optimized in the sequence. Herein, we propose a framework for learning-based optimization of the acquisition schedule (LOAS), which optimizes RF saturation-encoded MRF acquisitions with a minimal number of scan parameters for tissue parameter determination. In a supervised learning framework, scan parameters were subsequently updated to minimize a predefined loss function that can directly represent tissue quantification errors. We evaluated the performance of the proposed approach with a numerical phantom and in in vivo experiments. For validation, MRF images were synthesized using the tissue parameters estimated from a fully connected neural network framework and compared with references. Our results showed that LOAS outperformed existing indirect optimization methods with regard to quantification accuracy and acquisition efficiency. The proposed LOAS method could be a powerful optimization tool in the design of MRF pulse sequences.
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Affiliation(s)
- Beomgu Kang
- Department of Electrical Engineering, Korea Advanced
Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of
Korea
| | - Byungjai Kim
- Department of Electrical Engineering, Korea Advanced
Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of
Korea
- Divison of MR Research, Department of Radiology, Johns
Hopkins University, Baltimore, Maryland, USA
| | - HyunWook Park
- Department of Electrical Engineering, Korea Advanced
Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of
Korea
| | - Hye-Young Heo
- Divison of MR Research, Department of Radiology, Johns
Hopkins University, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging,
Kennedy Krieger Institute, Baltimore, Maryland, USA
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16
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Chemical Exchange Saturation Transfer for Pancreatic Ductal Adenocarcinoma Evaluation. Pancreas 2022; 51:463-468. [PMID: 35858211 DOI: 10.1097/mpa.0000000000002059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
OBJECTIVES The aims of the study are to evaluate the feasibility of using pH-sensitive magnetic resonance imaging, chemical exchange saturation transfer (CEST) in pancreatic imaging and to differentiate pancreatic ductal adenocarcinoma (PDAC) with the nontumor pancreas (upstream and downstream) and normal control pancreas. METHODS Sixteen CEST images with PDAC and 12 CEST images with normal volunteers were acquired and magnetization transfer ratio with asymmetric analysis were measured in areas of PDAC, upstream, downstream, and normal control pancreas. One-way analysis of variance and receiver operating characteristic curve were used to differentiate tumor from nontumor pancreas. RESULTS Areas with PDAC showed higher signal intensity than upstream and downstream on CEST images. The mean (standard deviation) values of magnetization transfer ratio with asymmetric analysis were 0.015 (0.034), -0.044 (0.030), -0.019 (0.027), and -0.037 (0.031), respectively, in PDAC area, upstream, downstream, and nontumor area in patient group and -0.008 (0.024) in normal pancreas. Significant differences were found between PDAC and upstream ( P < 0.001), between upstream and normal pancreas ( P = 0.04). Area under curve is 0.857 in differentiating PDAC with nontumor pancreas. CONCLUSIONS pH-sensitive CEST MRI is feasible in pancreatic imaging and can be used to differentiate PDAC from nontumor pancreas. This provides a novel metabolic imaging method in PDAC.
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17
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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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18
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Liu J, Liu H, Liu Q, Xu J, Liu X, Zheng H, Wu Y. Encoding capability prediction of acquisition schedules in CEST MR fingerprinting for pH quantification. Magn Reson Med 2021; 87:2044-2052. [PMID: 34752642 DOI: 10.1002/mrm.29074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/30/2021] [Accepted: 10/19/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To identify a reliable metric for predicting the encoding capability of CEST MR fingerprinting acquisition schedules for pH quantification, which may facilitate CEST MR fingerprinting protocol optimization. METHODS Numerical simulations and Cr phantom MRI experiments were conducted at 3 Tesla under representative CEST MR fingerprinting sampling scenarios, including the pseudorandomization of imaging parameters (e.g., saturation power B1 , saturation frequency offset, saturation time, and relaxation time), and variation of the maximum saturation power B1max , B1 number, and sampling pattern. The CEST effect at 2 ppm was measured using asymmetry analysis and matched to a predefined dictionary to determine the pH. The pH quantification error was assessed using RMSE. Three metrics, namely the Cramer-Rao bound, dot product, and Euclidean distance, were calculated for each sampling scenario, and their relationships with the pH RMSE were investigated to examine their effectiveness for predicting the encoding capability of sampling schedules for pH quantification. RESULTS Both simulation and phantom studies revealed that the Cramer-Rao bound metric consistently exhibited superior performance for predicting the pH quantification error. Although dot product exhibited good encoding capability prediction in most sampling scenarios, it failed in the scenario with varied B1 numbers. In contrast, Euclidean distance exhibited the worst performance among the 3 metrics in all scenarios. CONCLUSION Superior over dot product and Euclidean distance, the Cramer-Rao bound metric may reliably predicting the encoding capability of CEST MR fingerprinting sampling strategies and may be useful for guiding CEST MRI protocol optimization.
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Affiliation(s)
- Jie Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China.,Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Hui Liu
- UIH America Inc., Houston, Texas, USA
| | - Qi Liu
- UIH America Inc., Houston, Texas, USA
| | - Jian Xu
- UIH America Inc., Houston, Texas, USA
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China.,Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China.,Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Yin Wu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China.,Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
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19
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Ding H, Velasco C, Ye H, Lindner T, Grech-Sollars M, O’Callaghan J, Hiley C, Chouhan MD, Niendorf T, Koh DM, Prieto C, Adeleke S. Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer. Cancers (Basel) 2021; 13:4742. [PMID: 34638229 PMCID: PMC8507535 DOI: 10.3390/cancers13194742] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/08/2021] [Accepted: 09/16/2021] [Indexed: 11/25/2022] Open
Abstract
Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.
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Affiliation(s)
- Hao Ding
- Imperial College School of Medicine, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London SE1 7EH, UK; (C.V.); (C.P.)
| | - Huihui Ye
- State Key Laboratory of Modern Optical instrumentation, Zhejiang University, Hangzhou 310027, China;
| | - Thomas Lindner
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg Eppendorf, 20246 Hamburg, Germany;
| | - Matthew Grech-Sollars
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Surrey GU2 7XX, UK;
- Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK
| | - James O’Callaghan
- UCL Centre for Medical Imaging, Division of Medicine, University College London, London W1W 7TS, UK; (J.O.); (M.D.C.)
| | - Crispin Hiley
- Cancer Research UK, Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK;
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Manil D. Chouhan
- UCL Centre for Medical Imaging, Division of Medicine, University College London, London W1W 7TS, UK; (J.O.); (M.D.C.)
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck, Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany;
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SM2 5NG, UK;
- Department of Radiology, Royal Marsden Hospital, London SW3 6JJ, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London SE1 7EH, UK; (C.V.); (C.P.)
| | - Sola Adeleke
- High Dimensional Neurology Group, Queen’s Square Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Oncology, Guy’s & St Thomas’ Hospital, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, King’s College London, London WC2R 2LS, UK
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20
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van Zijl PCM, Brindle K, Lu H, Barker PB, Edden R, Yadav N, Knutsson L. Hyperpolarized MRI, functional MRI, MR spectroscopy and CEST to provide metabolic information in vivo. Curr Opin Chem Biol 2021; 63:209-218. [PMID: 34298353 PMCID: PMC8384704 DOI: 10.1016/j.cbpa.2021.06.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/12/2021] [Accepted: 06/15/2021] [Indexed: 12/13/2022]
Abstract
Access to metabolic information in vivo using magnetic resonance (MR) technologies has generally been the niche of MR spectroscopy (MRS) and spectroscopic imaging (MRSI). Metabolic fluxes can be studied using the infusion of substrates labeled with magnetic isotopes, with the use of hyperpolarization especially powerful. Unfortunately, these promising methods are not yet accepted clinically, where fast, simple, and reliable measurement and diagnosis are key. Recent advances in functional MRI and chemical exchange saturation transfer (CEST) MRI allow the use of water imaging to study oxygen metabolism and tissue metabolite levels. These, together with the use of novel data analysis approaches such as machine learning for all of these metabolic MR approaches, are increasing the likelihood of their clinical translation.
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Affiliation(s)
- Peter C M van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA.
| | - Kevin Brindle
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Hanzhang Lu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter B Barker
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Richard Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Nirbhay Yadav
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Linda Knutsson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medical Radiation Physics, Lund University, Lund, Sweden
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21
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Tippareddy C, Zhao W, Sunshine JL, Griswold M, Ma D, Badve C. Magnetic resonance fingerprinting: an overview. Eur J Nucl Med Mol Imaging 2021; 48:4189-4200. [PMID: 34037831 DOI: 10.1007/s00259-021-05384-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/25/2021] [Indexed: 12/17/2022]
Abstract
Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.
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Affiliation(s)
- Charit Tippareddy
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Walter Zhao
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Jeffrey L Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA.,Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA.
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22
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Kim K, Gu Y, Wang CY, Clifford B, Huang S, Liang ZP, Yu X. Quantification of creatine kinase reaction rate in mouse hindlimb using phosphorus-31 magnetic resonance spectroscopic fingerprinting. NMR IN BIOMEDICINE 2021; 34:e4435. [PMID: 33111456 PMCID: PMC8324327 DOI: 10.1002/nbm.4435] [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: 03/20/2020] [Revised: 09/10/2020] [Accepted: 10/08/2020] [Indexed: 06/11/2023]
Abstract
The goal of this study was to evaluate the accuracy, reproducibility, and efficiency of a 31 P magnetic resonance spectroscopic fingerprinting (31 P-MRSF) method for fast quantification of the forward rate constant of creatine kinase (CK) in mouse hindlimb. The 31 P-MRSF method acquired spectroscopic fingerprints using interleaved acquisition of phosphocreatine (PCr) and γATP with ramped flip angles and a saturation scheme sensitive to chemical exchange between PCr and γATP. Parameter estimation was performed by matching the acquired fingerprints to a dictionary of simulated fingerprints generated from the Bloch-McConnell model. The accuracy of 31 P-MRSF measurements was compared with the magnetization transfer (MT-MRS) method in mouse hindlimb at 9.4 T (n = 8). The reproducibility of 31 P-MRSF was also assessed by repeated measurements. Estimation of the CK rate constant using 31 P-MRSF (0.39 ± 0.03 s-1 ) showed a strong agreement with that using MT-MRS measurements (0.40 ± 0.05 s-1 ). Variations less than 10% were achieved with 2 min acquisition of 31 P-MRSF data. Application of the 31 P-MRSF method to mice subjected to an electrical stimulation protocol detected an increase in CK rate constant in response to stimulation-induced muscle contraction. These results demonstrated the potential of the 31 P-MRSF framework for rapid, accurate, and reproducible quantification of the chemical exchange rate of CK in vivo.
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Affiliation(s)
- Kihwan Kim
- Department of Biomedical Engineering and Case Center for Imaging Research, Case Western Reserve University, Cleveland, Ohio
| | - Yuning Gu
- Department of Biomedical Engineering and Case Center for Imaging Research, Case Western Reserve University, Cleveland, Ohio
| | - Charlie Y. Wang
- Department of Biomedical Engineering and Case Center for Imaging Research, Case Western Reserve University, Cleveland, Ohio
| | - Bryan Clifford
- Department of Electrical and Computer Engineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Sherry Huang
- Department of Biomedical Engineering and Case Center for Imaging Research, Case Western Reserve University, Cleveland, Ohio
| | - Zhi-Pei Liang
- Department of Electrical and Computer Engineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Xin Yu
- Department of Biomedical Engineering and Case Center for Imaging Research, Case Western Reserve University, Cleveland, Ohio
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23
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Ropella-Panagis K, Seiberlich N. Magnetic Resonance Fingerprinting: Basic Concepts and Applications in Molecular Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00067-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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24
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Perlman O, Ito H, Gilad AA, McMahon MT, Chiocca EA, Nakashima H, Farrar CT. Redesigned reporter gene for improved proton exchange-based molecular MRI contrast. Sci Rep 2020; 10:20664. [PMID: 33244130 PMCID: PMC7692519 DOI: 10.1038/s41598-020-77576-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 11/05/2020] [Indexed: 02/07/2023] Open
Abstract
Reporter gene imaging allows for non-invasive monitoring of molecular processes in living cells, providing insights on the mechanisms underlying pathology and therapy. A lysine-rich protein (LRP) chemical exchange saturation transfer (CEST) MRI reporter gene has previously been developed and used to image tumor cells, cardiac viral gene transfer, and oncolytic virotherapy. However, the highly repetitive nature of the LRP reporter gene sequence leads to DNA recombination events and the expression of a range of truncated LRP protein fragments, thereby greatly limiting the CEST sensitivity. Here we report the use of a redesigned LRP reporter (rdLRP), aimed to provide excellent stability and CEST sensitivity. The rdLRP contains no DNA repeats or GC rich regions and 30% less positively charged amino-acids. RT-PCR of cell lysates transfected with rdLRP demonstrated a stable reporter gene with a single distinct band corresponding to full-length DNA. A distinct increase in CEST-MRI contrast was obtained in cell lysates of rdLRP transfected cells and in in vivo LRP expressing mouse brain tumors ([Formula: see text], n = 10).
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Affiliation(s)
- Or Perlman
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 149 13th Street, Suite 2301, Charlestown, MA, 02129, USA
| | - Hirotaka Ito
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Assaf A Gilad
- Department of Biomedical Engineering, Michigan State University, East Lansing, MI, USA
- The Institute of Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
- Department of Radiology, Michigan State University, East Lansing, MI, USA
| | - Michael T McMahon
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- Division of MR Research, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - E Antonio Chiocca
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Hiroshi Nakashima
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Christian T Farrar
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 149 13th Street, Suite 2301, Charlestown, MA, 02129, USA.
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25
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Ji S, Yang D, Lee J, Choi SH, Kim H, Kang KM. Synthetic MRI: Technologies and Applications in Neuroradiology. J Magn Reson Imaging 2020; 55:1013-1025. [PMID: 33188560 DOI: 10.1002/jmri.27440] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 10/29/2020] [Accepted: 10/29/2020] [Indexed: 12/14/2022] Open
Abstract
Synthetic MRI is a technique that synthesizes contrast-weighted images from multicontrast MRI data. There have been advances in synthetic MRI since the technique was introduced. Although a number of synthetic MRI methods have been developed for quantifying one or more relaxometric parameters and for generating multiple contrast-weighted images, this review focuses on several methods that quantify all three relaxometric parameters (T1 , T2 , and proton density) and produce multiple contrast-weighted images. Acquisition, quantification, and image synthesis techniques are discussed for each method. We discuss the image quality and diagnostic accuracy of synthetic MRI methods and their clinical applications in neuroradiology. Based on this analysis, we highlight areas that need to be addressed for synthetic MRI to be widely implemented in the clinic. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Sooyeon Ji
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Dongjin Yang
- Department of Radiology, Daegu Fatima Hospital, Daegu, Republic of Korea
| | - Jongho Lee
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Seung Hong Choi
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyeonjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
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26
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Demetriou E, Kujawa A, Golay X. Pulse sequences for measuring exchange rates between proton species: From unlocalised NMR spectroscopy to chemical exchange saturation transfer imaging. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2020; 120-121:25-71. [PMID: 33198968 DOI: 10.1016/j.pnmrs.2020.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 06/27/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
Within the field of NMR spectroscopy, the study of chemical exchange processes through saturation transfer techniques has a long history. In the context of MRI, chemical exchange techniques have been adapted to increase the sensitivity of imaging to small fractions of exchangeable protons, including the labile protons of amines, amides and hydroxyls. The MR contrast is generated by frequency-selective irradiation of the labile protons, which results in a reduction of the water signal associated with transfer of the labile protons' saturated magnetization to the protons of the surrounding free water. The signal intensity depends on the rate of chemical exchange and the concentration of labile protons as well as on the properties of the irradiation field. This methodology is referred to as CEST (chemical exchange saturation transfer) imaging. Applications of CEST include imaging of molecules with short transverse relaxation times and mapping of physiological parameters such as pH, temperature, buffer concentration and chemical composition due to the dependency of this chemical exchange effect on all these parameters. This article aims to describe these effects both theoretically and experimentally. In depth analysis and mathematical modelling are provided for all pulse sequences designed to date to measure the chemical exchange rate. Importantly, it has become clear that the background signal from semi-solid protons and the presence of the Nuclear Overhauser Effect (NOE), either through direct dipole-dipole mechanisms or through exchange-relayed signals, complicates the analysis of CEST effects. Therefore, advanced methods to suppress these confounding factors have been developed, and these are also reviewed. Finally, the experimental work conducted both in vitro and in vivo is discussed and the progress of CEST imaging towards clinical practice is presented.
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Affiliation(s)
- Eleni Demetriou
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.
| | - Aaron Kujawa
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.
| | - Xavier Golay
- Brain Repair & Rehabilitation, Institute of Neurology, University College London, United Kingdom.
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27
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Meng Y, Cheung J, Sun PZ. Improved MR fingerprinting for relaxation measurement in the presence of semisolid magnetization transfer. Magn Reson Med 2020; 84:727-737. [PMID: 31898839 PMCID: PMC7180097 DOI: 10.1002/mrm.28159] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 12/11/2019] [Accepted: 12/11/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE To characterize and minimize the magnetization transfer (MT) effect in MR fingerprinting (MRF) relaxation measurements with a 2-pool (2P) MT model of multiple tissue types. THEORY AND METHODS Semisolid MT effect in MRF was modeled using 2P Bloch-McConnell equations. The combinations of MT parameters of multiple tissues (white [WM] and gray matter [GM]) were used to build the MRF dictionary. Both 1-pool (1P) and 2P models were simulated to characterize the dependence on MT. Relaxations measured using MRF with spin-echo saturation-recovery (SR) or inversion-recovery preparations were compared with conventional SR-prepared T1 and multiple spin-echo T2 measurements. The simulations results were validated with phantoms and brain tissue samples. RESULTS The MRF signal was different from the 1P and 2P models. 1P MRF produced significantly (P < .05) underestimated T1 in WM (20-30%) and GM (7-10%), while 2P MRF measured consistent T1 and T2 in both WM and GM with conventional measurements (pairwise test P > .1; correlated P < .05). Simulations showed that SR-prepared MRF measuring T1 had much less errors against the variation of the macromolecular fraction. Compared with inversion-recovery preparation, SR-prepared MRF produced higher relaxation correlations (R > 0.9) with conventional measurements in both WM and GM across samples, suggesting that SR-prepared MRF was less sensitive to the compositive effect of multiple MT parameters variations. CONCLUSIONS 2P MRF using a combination of MT parameters for multiple tissue types can measure consistent relaxations with conventional methods. With the 2P models, SR-prepared MRF would provide an option for robust relaxation measurement under heterogeneous MT.
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Affiliation(s)
- Yuguang Meng
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
| | - Jesse Cheung
- Department of Anthropology and Human Biology, Emory University, Atlanta, GA, USA
| | - Phillip Zhe Sun
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
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28
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Hilbert T, Xia D, Block KT, Yu Z, Lattanzi R, Sodickson DK, Kober T, Cloos MA. Magnetization transfer in magnetic resonance fingerprinting. Magn Reson Med 2020; 84:128-141. [PMID: 31762101 PMCID: PMC7083689 DOI: 10.1002/mrm.28096] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/18/2019] [Accepted: 11/04/2019] [Indexed: 01/28/2023]
Abstract
PURPOSE To study the effects of magnetization transfer (MT, in which a semi-solid spin pool interacts with the free pool), in the context of magnetic resonance fingerprinting (MRF). METHODS Simulations and phantom experiments were performed to study the impact of MT on the MRF signal and its potential influence on T1 and T2 estimation. Subsequently, an MRF sequence implementing off-resonance MT pulses and a dictionary with an MT dimension, generated by incorporating a two-pool model, were used to estimate the fractional pool size in addition to the B 1 + , T1 , and T2 values. The proposed method was evaluated in the human brain. RESULTS Simulations and phantom experiments showed that an MRF signal obtained from a cross-linked bovine serum sample is influenced by MT. Using a dictionary based on an MT model, a better match between simulations and acquired MR signals can be obtained (NRMSE 1.3% vs. 4.7%). Adding off-resonance MT pulses can improve the differentiation of MT from T1 and T2 . In vivo results showed that MT affects the MRF signals from white matter (fractional pool-size ~16%) and gray matter (fractional pool-size ~10%). Furthermore, longer T1 (~1060 ms vs. ~860 ms) and T2 values (~47 ms vs. ~35 ms) can be observed in white matter if MT is accounted for. CONCLUSION Our experiments demonstrated a potential influence of MT on the quantification of T1 and T2 with MRF. A model that encompasses MT effects can improve the accuracy of estimated relaxation parameters and allows quantification of the fractional pool size.
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Affiliation(s)
- Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Ding Xia
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Zidan Yu
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| | - Riccardo Lattanzi
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Martijn A Cloos
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
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29
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McGivney DF, Boyacioğlu R, Jiang Y, Poorman ME, Seiberlich N, Gulani V, Keenan KE, Griswold MA, Ma D. Magnetic resonance fingerprinting review part 2: Technique and directions. J Magn Reson Imaging 2020; 51:993-1007. [PMID: 31347226 PMCID: PMC6980890 DOI: 10.1002/jmri.26877] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/05/2019] [Accepted: 07/05/2019] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR-sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993-1007.
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Affiliation(s)
- Debra F. McGivney
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioğlu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Megan E. Poorman
- Department of Physics, University of Colorado Boulder, Boulder, Colorado, USA
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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30
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Protein-based amide proton transfer-weighted MR imaging of amnestic mild cognitive impairment. NEUROIMAGE-CLINICAL 2019; 25:102153. [PMID: 31901792 PMCID: PMC6948365 DOI: 10.1016/j.nicl.2019.102153] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/07/2019] [Accepted: 12/26/2019] [Indexed: 02/06/2023]
Abstract
Amide proton transfer-weighted (APTw) MRI is a novel molecular imaging technique that can noninvasively detect endogenous cellular proteins and peptides in tissue. Here, we demonstrate the feasibility of protein-based APTw MRI in characterizing amnestic mild cognitive impairment (aMCI). Eighteen patients with confirmed aMCI and 18 matched normal controls were scanned at 3 Tesla. The APTw, as well as conventional magnetization transfer ratio (MTR), signal differences between aMCI and normal groups were assessed by the independent samples t-test, and the receiver-operator-characteristic analysis was used to assess the diagnostic performance of APTw. When comparing the normal control group, aMCI brains typically had relatively higher APTw signals. Quantitatively, APTw intensity values were significantly higher in nine of 12 regions of interest in aMCI patients than in normal controls. The largest areas under the receiver-operator-characteristic curves were 0.88 (gray matter in occipital lobe) and 0.82 (gray matter in temporal lobe, white matter in occipital lobe) in diagnosing aMCI patients. On the contrary, MTR intensity values were significantly higher in only three of 12 regions of interest in the aMCI group. Additionally, the age dependency analyses revealed that these cross-sectional APTw/MTR signals had an increasing trend with age in most brain regions for normal controls, but a decreasing trend with age in most brain regions for aMCI patients. Our early results show the potential of the APTw signal as a new imaging biomarker for the noninvasive molecular diagnosis of aMCI.
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31
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Liu T, Chen Y, Thomas AM, Song X. CEST MRI with distribution-based analysis for assessment of early stage disease activity in a mouse model of multiple sclerosis: An initial study. NMR IN BIOMEDICINE 2019; 32:e4139. [PMID: 31342587 DOI: 10.1002/nbm.4139] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/14/2019] [Accepted: 06/14/2019] [Indexed: 06/10/2023]
Abstract
Imaging biomarkers that can detect pathological changes at an early stage of multiple sclerosis (MS) may allow earlier therapeutic intervention with an improved outcome. Using a mouse model of MS, termed as experimental autoimmune encephalomyelitis (EAE), we performed chemical exchange saturation transfer (CEST) MRI at a very early stage before symptom onset (6 days post-induction) for assessment of changes in tissues that appear "normal" with conventional MRI. The collected CEST Z-spectra signals (Ssat /S0 ) were analyzed using a histogram-guided method to determine the contributions from various offset frequencies. Histogram analysis showed that EAE mice exhibit a more heterogeneous distribution with lower peak heights in the hindbrain compared with naïve mice at saturation offsets of 1 and 2 ppm. At these two offsets, both the mean Ssat /S0 and the mean MTRasym values in the cerebellum and brain stem are significantly different between EAE and naïve mice (P < 0.05). Immunofluorescent staining validated the presence of neuroinflammation, with IBA1-positive cells detected throughout the hindbrain including the cerebellum and brain stem. Follow-up MRI at the symptom onset (score = 1.5-2.5, 13 days post-induction) confirmed gadolinium-enhanced periventricular lesions. CEST Z-spectra signals also changed by this time. The proposed three-level histogram-oriented analysis is simple to execute and robust for detecting subtle changes in Z-spectra signals, which does not require a priori knowledge of damage locations or contributing offset components. CEST MRI signals at 1 and 2 ppm were sensitive to the subtle pathological changes at an early stage in EAE mice, and have potential as novel imaging biomarkers complementary to functional and physiological MRI measures.
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Affiliation(s)
- Tao Liu
- Russell H. Morgan Dept. of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- Dept. of Neurology, Hainan General Hospital, Haikou, Hainan, China
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Yanrong Chen
- Russell H. Morgan Dept. of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- Dept. of Information Sciences and Technology, Northwest University, Xi'an, Shaanxi, China
| | - Aline M Thomas
- Russell H. Morgan Dept. of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Xiaolei Song
- Russell H. Morgan Dept. of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, Maryland
- Cellular Imaging Section and Vascular Biology Program, Institute for Cell Engineering, The Johns Hopkins University School of Medicine, Baltimore, Maryland
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32
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van Zijl P, Knutsson L. In vivo magnetic resonance imaging and spectroscopy. Technological advances and opportunities for applications continue to abound. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 306:55-65. [PMID: 31377150 PMCID: PMC6703925 DOI: 10.1016/j.jmr.2019.07.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 06/19/2019] [Accepted: 07/08/2019] [Indexed: 05/07/2023]
Abstract
Over the past decades, the field of in vivo magnetic resonance (MR) has built up an impressive repertoire of data acquisition and analysis technologies for anatomical, functional, physiological, and molecular imaging, the description of which requires many book volumes. As such it is impossible for a few authors to have an authoritative overview of the field and for a brief article to be inclusive. We will therefore focus mainly on data acquisition and attempt to give some insight into the principles underlying current advanced methods in the field and the potential for further innovation. In our view, the foreseeable future is expected to show continued rapid progress, for instance in imaging of microscopic tissue properties in vivo, assessment of functional and anatomical connectivity, higher resolution physiologic and metabolic imaging, and even imaging of receptor binding. In addition, acquisition speed and information content will continue to increase due to the continuous development of approaches for parallel imaging (including simultaneous multi-slice imaging), compressed sensing, and MRI fingerprinting. Finally, artificial intelligence approaches are becoming more realistic and will have a tremendous effect on both acquisition and analysis strategies. Together, these developments will continue to provide opportunity for scientific discovery and, in combination with large data sets from other fields such as genomics, allow the ultimate realization of precision medicine in the clinic.
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Affiliation(s)
- Peter van Zijl
- Department of Radiology, Johns Hopkins University, F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
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33
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Perlman O, Herz K, Zaiss M, Cohen O, Rosen MS, Farrar CT. CEST MR-Fingerprinting: Practical considerations and insights for acquisition schedule design and improved reconstruction. Magn Reson Med 2019; 83:462-478. [PMID: 31400034 DOI: 10.1002/mrm.27937] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/19/2019] [Accepted: 07/17/2019] [Indexed: 01/13/2023]
Abstract
PURPOSE To understand the influence of various acquisition parameters on the ability of CEST MR-Fingerprinting (MRF) to discriminate different chemical exchange parameters and to provide tools for optimal acquisition schedule design and parameter map reconstruction. METHODS Numerical simulations were conducted using a parallel computing implementation of the Bloch-McConnell equations, examining the effect of TR, TE, flip-angle, water T 1 and T 2 , saturation-pulse duration, power, and frequency on the discrimination ability of CEST-MRF. A modified Euclidean distance matching metric was evaluated and compared to traditional dot product matching. L-Arginine phantoms of various concentrations and pH were scanned at 4.7T and the results compared to numerical findings. RESULTS Simulations for dot product matching demonstrated that the optimal flip-angle and saturation times are 30 ∘ and 1100 ms, respectively. The optimal maximal saturation power was 3.4 μT for concentrated solutes with a slow exchange rate, and 5.2 μT for dilute solutes with medium-to-fast exchange rates. Using the Euclidean distance matching metric, much lower maximum saturation powers were required (1.6 and 2.4 μT, respectively), with a slightly longer saturation time (1500 ms) and 90 ∘ flip-angle. For both matching metrics, the discrimination ability increased with the repetition time. The experimental results were in agreement with simulations, demonstrating that more than a 50% reduction in scan-time can be achieved by Euclidean distance-based matching. CONCLUSIONS Optimization of the CEST-MRF acquisition schedule is critical for obtaining the best exchange parameter accuracy. The use of Euclidean distance-based matching of signal trajectories simultaneously improved the discrimination ability and reduced the scan time and maximal saturation power required.
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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, Massachusetts
| | - Kai Herz
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Ouri Cohen
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts.,Department of Physics, Harvard University, Cambridge, Massachusetts
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
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Poorman ME, Martin MN, Ma D, McGivney DF, Gulani V, Griswold MA, Keenan KE. Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations. J Magn Reson Imaging 2019; 51:675-692. [PMID: 31264748 DOI: 10.1002/jmri.26836] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 05/31/2019] [Indexed: 12/11/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a powerful quantitative MRI technique capable of acquiring multiple property maps simultaneously in a short timeframe. The MRF framework has been adapted to a wide variety of clinical applications, but faces challenges in technical development, and to date has only demonstrated repeatability and reproducibility in small studies. In this review, we discuss the current implementations of MRF and their use in a clinical setting. Based on this analysis, we highlight areas of need that must be addressed before MRF can be fully adopted into the clinic and make recommendations to the MRF community on standardization and validation strategies of MRF techniques. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:675-692.
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Affiliation(s)
- Megan E. Poorman
- Department of PhysicsUniversity of Colorado Boulder Boulder Colorado USA
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
| | - Michele N. Martin
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
| | - Dan Ma
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Debra F. McGivney
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Vikas Gulani
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Mark A. Griswold
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Kathryn E. Keenan
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
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Ropella-Panagis KM, Seiberlich N, Gulani V. Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 3:388-399. [PMID: 32864537 DOI: 10.1109/trpms.2019.2897425] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Magnetic Resonance Imaging (MRI) can be used to assess anatomical structure, and its sensitivity to a variety of tissue properties enables superb contrast between tissues as well as the ability to characterize these tissues. However, despite vast potential for quantitative and functional evaluation, MRI is typically used qualitatively, in which the underlying tissue properties are not measured, and thus the brightness of each pixel is not quantitatively meaningful. Positron Emission Tomography (PET) is an inherently quantitative imaging modality that interrogates functional activity within a tissue, probed by a molecule of interest coupled with an appropriate tracer. These modalities can complement one another to provide clinical information regarding both structure and function, but there are still technical and practical hurdles in the way of the integrated use of both modalities. Recent advances in MRI have moved the field in an increasingly quantitative direction, which is complementary to PET, and could also potentially help solve some of the challenges in PET/MR. Magnetic Resonance Fingerprinting (MRF) is a recently described MRI-based technique which can efficiently and simultaneously quantitatively map several tissue properties in a single exam. Here, the basic principles behind the quantitative approach of MRF are laid out, and the potential implications for combined PET/MR are discussed.
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Affiliation(s)
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
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36
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Zhou J, Heo HY, Knutsson L, van Zijl PCM, Jiang S. APT-weighted MRI: Techniques, current neuro applications, and challenging issues. J Magn Reson Imaging 2019; 50:347-364. [PMID: 30663162 DOI: 10.1002/jmri.26645] [Citation(s) in RCA: 217] [Impact Index Per Article: 43.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Revised: 12/26/2018] [Accepted: 12/27/2018] [Indexed: 02/06/2023] Open
Abstract
Amide proton transfer-weighted (APTw) imaging is a molecular MRI technique that generates image contrast based predominantly on the amide protons in mobile cellular proteins and peptides that are endogenous in tissue. This technique, the most studied type of chemical exchange saturation transfer imaging, has been used successfully for imaging of protein content and pH, the latter being possible due to the strong dependence of the amide proton exchange rate on pH. In this article we briefly review the basic principles and recent technical advances of APTw imaging, which is showing promise clinically, especially for characterizing brain tumors and distinguishing recurrent tumor from treatment effects. Early applications of this approach to stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury are also illustrated. Finally, we outline the technical challenges for clinical APT-based imaging and discuss several controversies regarding the origin of APTw imaging signals in vivo. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:347-364.
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Affiliation(s)
- Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Linda Knutsson
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Peter C M van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA.,F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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37
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Heo HY, Han Z, Jiang S, Schär M, van Zijl PCM, Zhou J. Quantifying amide proton exchange rate and concentration in chemical exchange saturation transfer imaging of the human brain. Neuroimage 2019; 189:202-213. [PMID: 30654175 DOI: 10.1016/j.neuroimage.2019.01.034] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 01/09/2019] [Accepted: 01/13/2019] [Indexed: 12/31/2022] Open
Abstract
Current chemical exchange saturation transfer (CEST) neuroimaging protocols typically acquire CEST-weighted images, and, as such, do not essentially provide quantitative proton-specific exchange rates (or brain pH) and concentrations. We developed a dictionary-free MR fingerprinting (MRF) technique to allow CEST parameter quantification with a reduced data set. This was accomplished by subgrouping proton exchange models (SPEM), taking amide proton transfer (APT) as an example, into two-pool (water and semisolid macromolecules) and three-pool (water, semisolid macromolecules, and amide protons) models. A variable radiofrequency saturation scheme was used to generate unique signal evolutions for different tissues, reflecting their CEST parameters. The proposed MRF-SPEM method was validated using Bloch-McConnell equation-based digital phantoms with known ground-truth, which showed that MRF-SPEM can achieve a high degree of accuracy and precision for absolute CEST parameter quantification and CEST phantoms. For in-vivo studies at 3 T, using the same model as in the simulations, synthetic Z-spectra were generated using rates and concentrations estimated from the MRF-SPEM reconstruction and compared with experimentally measured Z-spectra as the standard for optimization. The MRF-SPEM technique can provide rapid and quantitative human brain CEST mapping.
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Affiliation(s)
- Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Zheng Han
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Michael Schär
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Peter C M van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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