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Chen Q, Worthoff WA, Shah NJ. Accelerated multiple-quantum-filtered sodium magnetic resonance imaging using compressed sensing at 7 T. Magn Reson Imaging 2024; 107:138-148. [PMID: 38171423 DOI: 10.1016/j.mri.2023.12.011] [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: 03/06/2023] [Revised: 07/17/2023] [Accepted: 12/28/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE Multiple-quantum-filtered (MQF) sodium magnetic resonance imaging (MRI), such as enhanced single-quantum and triple-quantum-filtered imaging of 23Na (eSISTINA), enables images to be weighted towards restricted sodium, a promising biomarker in clinical practice, but often suffers from clinically infeasible acquisition times and low image quality. This study aims to mitigate the above limitation by implementing a novel eSISTINA sequence at 7 T with the application of compressed sensing (CS) to accelerate eSISTINA acquisitions without a noticeable loss of information. METHODS A novel eSISTINA sequence with a 3D spiral-based sampling scheme was implemented at 7 T for the application of CS. Fully sampled datasets were obtained from one phantom and ten healthy subjects, and were then retrospectively undersampled by various undersampling factors. CS undersampled reconstructions were compared to fully sampled and undersampled nonuniform fast Fourier transform (NUFFT) reconstructions. Reconstruction performance was evaluated based on structural similarity (SSIM), signal-to-noise ratio (SNR), weightings towards total and compartmental sodium, and in vivo quantitative estimates. RESULTS CS-based phantom and in vivo images have less noise and better structural delineation while maintaining the weightings towards total, non-restricted (predominantly extracellular), and restricted (primarily intracellular) sodium. CS generally outperforms NUFFT with a higher SNR and a better SSIM, except for the SSIM in TQ brain images, which is likely due to substantial noise contamination. CS enables in vivo quantitative estimates with <15% errors at an undersampling factor of up to two. CONCLUSIONS Successful implementation of an eSISTINA sequence with an incoherent sampling scheme at 7 T was demonstrated. CS can accelerate eSISTINA by up to twofold at 7 T with reduced noise levels compared to NUFFT, while maintaining major structural information, reasonable weightings towards total and compartmental sodium, and relatively reliable in vivo quantification. The associated reduction in acquisition time has the potential to facilitate the clinical applicability of MQF sodium MRI.
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Affiliation(s)
- Qingping Chen
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Jülich GmbH, Jülich, Germany; Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Wieland A Worthoff
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Jülich GmbH, Jülich, Germany.
| | - N Jon Shah
- Institute of Neuroscience and Medicine - 4, Forschungszentrum Jülich GmbH, Jülich, Germany; Institute of Neuroscience and Medicine - 11, Forschungszentrum Jülich GmbH, Jülich, Germany; JARA-BRAIN-Translational Medicine, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
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Cho NS, Sanvito F, Thakuria S, Wang C, Hagiwara A, Nagaraj R, Oshima S, Lopez Kolkovsky AL, Lu J, Raymond C, Liau LM, Everson RG, Patel KS, Kim W, Yang I, Bergsneider M, Nghiemphu PL, Lai A, Nathanson DA, Cloughesy TF, Ellingson BM. Multi-nuclear sodium, diffusion, and perfusion MRI in human gliomas. J Neurooncol 2023; 163:417-427. [PMID: 37294422 PMCID: PMC10322966 DOI: 10.1007/s11060-023-04363-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 06/02/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE There is limited knowledge about the associations between sodium and proton MRI measurements in brain tumors. The purpose of this study was to quantify intra- and intertumoral correlations between sodium, diffusion, and perfusion MRI in human gliomas. METHODS Twenty glioma patients were prospectively studied on a 3T MRI system with multinuclear capabilities. Three mutually exclusive tumor volumes of interest (VOIs) were segmented: contrast-enhancing tumor (CET), T2/FLAIR hyperintense non-enhancing tumor (NET), and necrosis. Median and voxel-wise associations between apparent diffusion coefficient (ADC), normalized relative cerebral blood volume (nrCBV), and normalized sodium measurements were quantified for each VOI. RESULTS Both relative sodium concentration and ADC were significantly higher in areas of necrosis compared to NET (P = 0.003 and P = 0.008, respectively) and CET (P = 0.02 and P = 0.02). Sodium concentration was higher in CET compared to NET (P = 0.04). Sodium and ADC were higher in treated compared to treatment-naïve gliomas within NET (P = 0.006 and P = 0.01, respectively), and ADC was elevated in CET (P = 0.03). Median ADC and sodium concentration were positively correlated across patients in NET (r = 0.77, P < 0.0001) and CET (r = 0.84, P < 0.0001), but not in areas of necrosis (r = 0.45, P = 0.12). Median nrCBV and sodium concentration were negatively correlated across patients in areas of NET (r=-0.63, P = 0.003). Similar associations were observed when examining voxel-wise correlations within VOIs. CONCLUSION Sodium MRI is positively correlated with proton diffusion MRI measurements in gliomas, likely reflecting extracellular water. Unique areas of multinuclear MRI contrast may be useful in future studies to understand the chemistry of the tumor microenvironment.
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Affiliation(s)
- Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shruti Thakuria
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Raksha Nagaraj
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sonoko Oshima
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Alfredo L Lopez Kolkovsky
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
| | - Jianwen Lu
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kunal S Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Won Kim
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Isaac Yang
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Marvin Bergsneider
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL) Professor of Radiology, Psychiatry, and Neurosurgery Departments of Radiological Sciences, Psychiatry, and Neurosurgery David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
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Chen Q, Shah NJ, Worthoff WA. Compressed Sensing in Sodium Magnetic Resonance Imaging: Techniques, Applications, and Future Prospects. J Magn Reson Imaging 2021; 55:1340-1356. [PMID: 34918429 DOI: 10.1002/jmri.28029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/01/2021] [Accepted: 12/03/2021] [Indexed: 11/06/2022] Open
Abstract
Sodium (23 Na) yields the second strongest nuclear magnetic resonance (NMR) signal in biological tissues and plays a vital role in cell physiology. Sodium magnetic resonance imaging (MRI) can provide insights into cell integrity and tissue viability relative to pathologies without significant anatomical alternations, and thus it is considered to be a potential surrogate biomarker that provides complementary information for standard hydrogen (1 H) MRI in a noninvasive and quantitative manner. However, sodium MRI suffers from a relatively low signal-to-noise ratio and long acquisition times due to its relatively low NMR sensitivity. Compressed sensing-based (CS-based) methods have been shown to accelerate sodium imaging and/or improve sodium image quality significantly. In this manuscript, the basic concepts of CS and how CS might be applied to improve sodium MRI are described, and the historical milestones of CS-based sodium MRI are briefly presented. Representative advanced techniques and evaluation methods are discussed in detail, followed by an expose of clinical applications in multiple anatomical regions and diseases as well as thoughts and suggestions on potential future research prospects of CS in sodium MRI. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Qingping Chen
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich GmbH, Jülich, Germany.,Faculty of Medicine, RWTH Aachen University, Aachen, Germany.,Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - N Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich GmbH, Jülich, Germany.,Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany.,JARA-BRAIN-Translational Medicine, Aachen, Germany.,Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Wieland A Worthoff
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich GmbH, Jülich, Germany
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Adlung A, Paschke NK, Golla AK, Bauer D, Mohamed SA, Samartzi M, Fatar M, Neumaier-Probst E, Zöllner FG, Schad LR. 23 Na MRI in ischemic stroke: Acquisition time reduction using postprocessing with convolutional neural networks. NMR IN BIOMEDICINE 2021; 34:e4474. [PMID: 33480128 DOI: 10.1002/nbm.4474] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/18/2020] [Accepted: 12/31/2020] [Indexed: 06/12/2023]
Abstract
Quantitative 23 Na magnetic resonance imaging (MRI) provides tissue sodium concentration (TSC), which is connected to cell viability and vitality. Long acquisition times are one of the most challenging aspects for its clinical establishment. K-space undersampling is an approach for acquisition time reduction, but generates noise and artifacts. The use of convolutional neural networks (CNNs) is increasing in medical imaging and they are a useful tool for MRI postprocessing. The aim of this study is 23 Na MRI acquisition time reduction by k-space undersampling. CNNs were applied to reduce the resulting noise and artifacts. A retrospective analysis from a prospective study was conducted including image datasets from 46 patients (aged 72 ± 13 years; 25 women, 21 men) with ischemic stroke; the 23 Na MRI acquisition time was 10 min. The reconstructions were performed with full dataset (FI) and with a simulated dataset an image that was acquired in 2.5 min (RI). Eight different CNNs with either U-Net-based or ResNet-based architectures were implemented with RI as input and FI as label, using batch normalization and the number of filters as varying parameters. Training was performed with 9500 samples and testing included 400 samples. CNN outputs were evaluated based on signal-to-noise ratio (SNR) and structural similarity (SSIM). After quantification, TSC error was calculated. The image quality was subjectively rated by three neuroradiologists. Statistical significance was evaluated by Student's t-test. The average SNR was 21.72 ± 2.75 (FI) and 10.16 ± 0.96 (RI). U-Nets increased the SNR of RI to 43.99 and therefore performed better than ResNet. SSIM of RI to FI was improved by three CNNs to 0.91 ± 0.03. CNNs reduced TSC error by up to 15%. The subjective rating of CNN-generated images showed significantly better results than the subjective image rating of RI. The acquisition time of 23 Na MRI can be reduced by 75% due to postprocessing with a CNN on highly undersampled data.
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Affiliation(s)
- Anne Adlung
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Nadia K Paschke
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Alena-Kathrin Golla
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dominik Bauer
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sherif A Mohamed
- Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Melina Samartzi
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Marc Fatar
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Eva Neumaier-Probst
- Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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