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Zhao Q, Holt A, Spritzer CE, DeFrate LE, McNulty AL, Wang N. High angular resolution diffusion imaging (HARDI) of porcine menisci: a comparison of diffusion tensor imaging and generalized q-sampling imaging. Quant Imaging Med Surg 2024; 14:2738-2746. [PMID: 38617143 PMCID: PMC11007495 DOI: 10.21037/qims-23-1355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/19/2024] [Indexed: 04/16/2024]
Abstract
Background Diffusion magnetic resonance imaging (MRI) allows for the quantification of water diffusion properties in soft tissues. The goal of this study was to characterize the 3D collagen fiber network in the porcine meniscus using high angular resolution diffusion imaging (HARDI) acquisition with both diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI). Methods Porcine menisci (n=7) were scanned ex vivo using a three-dimensional (3D) HARDI spin-echo pulse sequence with an isotropic resolution of 500 µm at 7.0 Tesla. Both DTI and GQI reconstruction techniques were used to quantify the collagen fiber alignment and visualize the complex collagen network of the meniscus. The MRI findings were validated with conventional histology. Results DTI and GQI exhibited distinct fiber orientation maps in the meniscus using the same HARDI acquisition. We found that crossing fibers were only resolved with GQI, demonstrating the advantage of GQI over DTI to visualize the complex collagen fiber orientation in the meniscus. Furthermore, the MRI findings were consistent with conventional histology. Conclusions HARDI acquisition with GQI reconstruction more accurately resolves the complex 3D collagen architecture of the meniscus compared to DTI reconstruction. In the future, these technologies have the potential to nondestructively assess both normal and abnormal meniscal structure.
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Affiliation(s)
- Qi Zhao
- Physical Education Institute, Jimei University, Xiamen, China
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Abigail Holt
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
| | - Charles E. Spritzer
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Louis E. DeFrate
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Amy L. McNulty
- Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Pathology, Duke University School of Medicine, Durham, NC, USA
| | - Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, USA
- Indiana Center for Musculoskeletal Health, Indiana University, Indianapolis, IN, USA
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Shu H, Xu H, Pan Z, Liu Y, Deng W, Zhao R, Sun Y, Wang Z, Yang J, Gao H, Yao K, Zheng J, Yu Y, Li X. Early detection of myocardial involvement by non-contrast T1ρ mapping of cardiac magnetic resonance in type 2 diabetes mellitus. Front Endocrinol (Lausanne) 2024; 15:1335899. [PMID: 38510696 PMCID: PMC10952821 DOI: 10.3389/fendo.2024.1335899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 02/07/2024] [Indexed: 03/22/2024] Open
Abstract
Objective This study aims to determine the effectiveness of T1ρ in detecting myocardial fibrosis in type 2 diabetes mellitus (T2DM) patients by comparing with native T1 and extracellular volume (ECV) fraction. Methods T2DM patients (n = 35) and healthy controls (n = 30) underwent cardiac magnetic resonance. ECV, T1ρ, native T1, and global longitudinal strain (GLS) values were assessed. Diagnostic performance was analyzed using receiver operating curves. Results The global ECV and T1ρ of T2DM group (ECV = 32.1 ± 3.2%, T1ρ = 51.6 ± 3.8 msec) were significantly higher than those of controls (ECV = 26.2 ± 1.6%, T1ρ = 46.8 ± 2.0 msec) (all P < 0.001), whether there was no significant difference in native T1 between T2DM and controls (P = 0.264). The GLS decreased significantly in T2DM patients compared with controls (-16.5 ± 2.4% vs. -18.3 ± 2.6%, P = 0.015). The T1ρ and native T1 were associated with ECV (Pearson's r = 0.50 and 0.25, respectively, both P < 0.001); the native T1, T1ρ, and ECV were associated with hemoglobin A1c (Pearson's r = 0.41, 0.52, and 0.61, respectively, all P < 0.05); and the ECV was associated with diabetes duration (Pearson's r = 0.41, P = 0.016). The AUC of ECV, T1ρ, GLS, and native T1 were 0.869, 0.810, 0.659, and 0.524, respectively. Conclusion In T2DM patients, T1ρ may be a new non-contrast cardiac magnetic resonance technique for identifying myocardial diffuse fibrosis, and T1ρ may be more sensitive than native T1 in the detection of myocardial diffuse fibrosis.
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Affiliation(s)
- Hongmin Shu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Huimin Xu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Zixiang Pan
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Yan Liu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Wei Deng
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Ren Zhao
- Department of Cardiology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yan Sun
- Department of Geriatric Endocrinology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhen Wang
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Jinxiu Yang
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Hui Gao
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Kaixuan Yao
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Jie Zheng
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
| | - Yongqiang Yu
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
| | - Xiaohu Li
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, Anhui, China
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Menon RG, Raghavan P, Regatte RR. Pilot study quantifying muscle glycosaminoglycan using bi-exponential T 1ρ mapping in patients with muscle stiffness after stroke. Sci Rep 2021; 11:13951. [PMID: 34230600 PMCID: PMC8260636 DOI: 10.1038/s41598-021-93304-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/16/2021] [Indexed: 01/14/2023] Open
Abstract
Post stroke muscle stiffness is a common problem, which left untreated can lead to disabling muscle contractures. The purpose of this pilot study was to evaluate the feasibility of bi-exponential T1ρ mapping in patients with arm muscle stiffness after stroke and its ability to measure treatment related changes in muscle glycosaminoglycans (GAGs). Five patients with muscle stiffness after stroke and 5 healthy controls were recruited for imaging of the upper arm with 3D-T1ρ mapping. Patients were scanned before and after treatment with hyaluronidase injections, whereas the controls were scanned once. Wilcoxon Mann-Whitney tests compared patients vs. controls and patients pre-treatment vs. post-treatment. With bi-exponential modeling, the long component, T1ρl was significantly longer in the patients (biceps P = 0.01; triceps P = 0.004) compared to controls. There was also a significant difference in the signal fractions of the long and short components (biceps P = 0.03, triceps P = 0.04). The results suggest that muscle stiffness is characterized by increased muscle free water and GAG content. Post-treatment, the T1ρ parameters shifted toward control values. This pilot study demonstrates the application of bi-exponential T1ρ mapping as a marker for GAG content in muscle and as a potential treatment monitoring tool for patients with muscle stiffness after stroke.
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Affiliation(s)
- Rajiv G Menon
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA.
| | - Preeti Raghavan
- Deparments of Physical Medicine and Rehabilitation and Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Ravinder R Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 1st Ave, 4th Floor, New York, NY, 10016, USA
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Abstract
PURPOSE OF REVIEW Osteoarthritis is a major source of disability, pain and socioeconomic cost worldwide. The epidemiology of the disorder is multifactorial including genetic, biological and biomechanical components, some of them detectable by MRI. This review provides the most recent update on MRI biomarkers which can provide functional information of the joint structures for diagnosis, prognosis and treatment response monitoring in osteoarthritis trials. RECENT FINDINGS Compositional or functional MRI can provide clinicians with valuable information on glycosaminoglycan content (chemical exchange saturation transfer, sodium MRI, T1ρ) and collagen organization (T2, T2, apparent diffusion coefficient, magnetization transfer) in joint structures. Other parameters may also provide useful information, such as volumetric measurements of joint structures or advanced image data postprocessing and analysis. Automated tools seem to have a great potential to be included in these efforts providing standardization and acceleration of the image data analysis process. SUMMARY Functional or compositional MRI has great potential to provide noninvasive imaging biomarkers for osteoarthritis. Osteoarthritis as a whole joint condition needs to be diagnosed in early stages to facilitate selection of patients into clinical trials and/or to measure treatment effectiveness. Advanced evaluation including machine learning, neural networks and multidimensional data analysis allow for wall-to-wall understanding of parameter interactions and their role in clinical evaluation of osteoarthritis.
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