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Zhang X, de Moura HL, Monga A, Zibetti MVW, Kijowski R, Regatte RR. Repeatability of Quantitative Knee Cartilage T 1, T 2, and T 1ρ Mapping With 3D-MRI Fingerprinting. J Magn Reson Imaging 2024; 60:688-699. [PMID: 37885320 PMCID: PMC11045656 DOI: 10.1002/jmri.29068] [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: 07/13/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Three-dimensional MR fingerprinting (3D-MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited. PURPOSE To assess the intra-day and inter-day repeatabilities of knee cartilage T1, T2, and T1ρ maps using a 3D-MRF sequence for simultaneous measurement. STUDY TYPE Prospective. SUBJECTS Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. FIELD STRENGTH/SEQUENCE 3 T/3D-MRF, T1, T2, and T1ρ maps. ASSESSMENT The acquisition of 3D-MRF cartilage (simultaneous acquisition of T1, T2, and T1ρ maps) were acquired using a dictionary pattern-matching approach. Conventional cartilage T1, T2, and T1ρ maps were acquired using variable flip angles and a modified 3D-Turbo-Flash sequence with different echo and spin-lock times, respectively, and were fitted using mono-exponential models. Each sequence was acquired on Day 1 and Day 7 with two scans on each day. STATISTICAL TESTS The mean and SD for cartilage T1, T2, and T1ρ were calculated in five manually segmented regions of interest (ROIs), including lateral femur, lateral tibia, medial femur, medial tibia, and patella cartilages. Intra-subject and inter-subject repeatabilities were assessed using coefficient of variation (CV) and intra-class correlation coefficient (ICC), respectively, on the same day and among different days. Regression and Bland-Altman analysis were performed to compare maps between the conventional and 3D-MRF sequences. RESULTS The CV in all ROIs was lower than 7.4%, 8.4%, and 7.5% and the ICC was higher than 0.56, 0.51, and 0.52 for cartilage T1, T2, and T1ρ, respectively. The MRF results had a good agreement with the conventional methods with a linear regression slope >0.61 and R2 > 0.59. CONCLUSION The 3D-MRF sequence had high intra-subject and inter-subject repeatabilities for simultaneously measuring knee cartilage T1, T2, and T1ρ with good agreement with conventional sequences. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Hector L. de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Anmol Monga
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Marcelo V. W. Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Richard Kijowski
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Ravinder R. Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
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Gast LV, Platt T, Nagel AM, Gerhalter T. Recent technical developments and clinical research applications of sodium ( 23Na) MRI. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2023; 138-139:1-51. [PMID: 38065665 DOI: 10.1016/j.pnmrs.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 12/18/2023]
Abstract
Sodium is an essential ion that plays a central role in many physiological processes including the transmembrane electrochemical gradient and the maintenance of the body's homeostasis. Due to the crucial role of sodium in the human body, the sodium nucleus is a promising candidate for non-invasively assessing (patho-)physiological changes. Almost 10 years ago, Madelin et al. provided a comprehensive review of methods and applications of sodium (23Na) MRI (Madelin et al., 2014) [1]. More recent review articles have focused mainly on specific applications of 23Na MRI. For example, several articles covered 23Na MRI applications for diseases such as osteoarthritis (Zbyn et al., 2016, Zaric et al., 2020) [2,3], multiple sclerosis (Petracca et al., 2016, Huhn et al., 2019) [4,5] and brain tumors (Schepkin, 2016) [6], or for imaging certain organs such as the kidneys (Zollner et al., 2016) [7], the brain (Shah et al., 2016, Thulborn et al., 2018) [8,9], and the heart (Bottomley, 2016) [10]. Other articles have reviewed technical developments such as radiofrequency (RF) coils for 23Na MRI (Wiggins et al., 2016, Bangerter et al., 2016) [11,12], pulse sequences (Konstandin et al., 2014) [13], image reconstruction methods (Chen et al., 2021) [14], and interleaved/simultaneous imaging techniques (Lopez Kolkovsky et al., 2022) [15]. In addition, 23Na MRI topics have been covered in review articles with broader topics such as multinuclear MRI or ultra-high-field MRI (Niesporek et al., 2019, Hu et al., 2019, Ladd et al., 2018) [16-18]. During the past decade, various research groups have continued working on technical improvements to sodium MRI and have investigated its potential to serve as a diagnostic and prognostic tool. Clinical research applications of 23Na MRI have covered a broad spectrum of diseases, mainly focusing on the brain, cartilage, and skeletal muscle (see Fig. 1). In this article, we aim to provide a comprehensive summary of methodological and hardware developments, as well as a review of various clinical research applications of sodium (23Na) MRI in the last decade (i.e., published from the beginning of 2013 to the end of 2022).
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Affiliation(s)
- Lena V Gast
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - Tanja Platt
- Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Teresa Gerhalter
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, 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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