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Chekhonin IV, Cohen O, Otazo R, Young RJ, Holodny AI, Pronin IN. Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review. Neuroradiol J 2024; 37:267-275. [PMID: 37133228 PMCID: PMC11138331 DOI: 10.1177/19714009231173100] [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] [Indexed: 05/04/2023] Open
Abstract
Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.
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Affiliation(s)
- Ivan V Chekhonin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
- Federal State Budgetary Institution V.P. Serbsky National Medical Research Centre for Psychiatry and Narcology of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY, USA
| | - Igor N Pronin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
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Grazzini I, Venezia D, Roscio DD, Chiarotti I, Mazzei MA, Cerase A. Morphological and functional neuroradiology of brain metastases. Semin Ultrasound CT MR 2023; 44:170-193. [DOI: 10.1053/j.sult.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
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Liu S, Li H, Liu Y, Cheng G, Yang G, Wang H, Zheng H, Liang D, Zhu Y. Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac8c81] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/24/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Introduction. To propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping by undersampling k-space and reducing the acquired contrast number simultaneously. Methods. The proposed framework consists of a reconstruction module and a generative module. The reconstruction module reconstructs MR images from the acquired few undersampled k-space data with the help of a data prior. The generative module then synthesizes the remaining multi-contrast images from the reconstructed images, where the exponential model is implicitly incorporated into the image generation through the supervision of fully sampled labels. The RG-Net was trained and tested on the T1ρ
mapping data from 8 volunteers at net acceleration rates of 17, respectively. Regional T1ρ
analysis for cartilage and the brain was performed to assess the performance of RG-Net. Results. RG-Net yields a high-quality T1ρ
map at a high acceleration rate of 17. Compared with the competing methods that only undersample k-space, our framework achieves better performance in T1ρ
value analysis. Conclusion. The proposed RG-Net can achieve a high acceleration rate while maintaining good reconstruction quality by undersampling k-space and reducing the contrast number simultaneously for fast MR parametric mapping. The generative module of our framework can also be used as an insertable module in other fast MR parametric mapping methods.
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Hou X, Chen S, Zhang P, Guo D, Wang B. Targeted Arginine Metabolism Therapy: A Dilemma in Glioma Treatment. Front Oncol 2022; 12:938847. [PMID: 35898872 PMCID: PMC9313538 DOI: 10.3389/fonc.2022.938847] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/20/2022] [Indexed: 11/29/2022] Open
Abstract
Efforts in the treatment of glioma which is the most common primary malignant tumor of the central nervous system, have not shown satisfactory results despite a comprehensive treatment model that combines various treatment methods, including immunotherapy. Cellular metabolism is a determinant of the viability and function of cancer cells as well as immune cells, and the interplay of immune regulation and metabolic reprogramming in tumors has become an active area of research in recent years. From the perspective of metabolism and immunity in the glioma microenvironment, we elaborated on arginine metabolic reprogramming in glioma cells, which leads to a decrease in arginine levels in the tumor microenvironment. Reduced arginine availability significantly inhibits the proliferation, activation, and function of T cells, thereby promoting the establishment of an immunosuppressive microenvironment. Therefore, replenishment of arginine levels to enhance the anti-tumor activity of T cells is a promising strategy for the treatment of glioma. However, due to the lack of expression of argininosuccinate synthase, gliomas are unable to synthesize arginine; thus, they are highly dependent on the availability of arginine in the extracellular environment. This metabolic weakness of glioma has been utilized by researchers to develop arginine deprivation therapy, which ‘starves’ tumor cells by consuming large amounts of arginine in circulation. Although it has shown good results, this treatment modality that targets arginine metabolism in glioma is controversial. Exploiting a suitable strategy that can not only enhance the antitumor immune response, but also “starve” tumor cells by regulating arginine metabolism to cure glioma will be promising.
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Saito S. [5. Advanced Imaging Technology-T1rho-CEST Imaging]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:95-100. [PMID: 35046227 DOI: 10.6009/jjrt.780111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Shigeyoshi Saito
- Laboratory of Advanced Imaging Technology, Department of Medical Physics and Engineering, Division of Health Sciences, Osaka University Graduate School of Medicine.,Department of Advanced Medical Technology, National Cardiovascular and Cerebral Research Center
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Velasco C, Cruz G, Lavin B, Hua A, Fotaki A, Botnar RM, Prieto C. Simultaneous T 1 , T 2 , and T 1ρ cardiac magnetic resonance fingerprinting for contrast agent-free myocardial tissue characterization. Magn Reson Med 2021; 87:1992-2002. [PMID: 34799854 DOI: 10.1002/mrm.29091] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop a simultaneous T1 , T2 , and T1ρ cardiac magnetic resonance fingerprinting (MRF) approach to enable comprehensive contrast agent-free myocardial tissue characterization in a single breath-hold scan. METHODS A 2D gradient-echo electrocardiogram-triggered cardiac MRF sequence with low flip angles, varying magnetization preparation, and spiral trajectory was acquired at 1.5 T to encode T1 , T2 , and T1⍴ simultaneously. The MRF images were reconstructed using low-rank inversion, regularized with a multicontrast patch-based higher-order reconstruction. Parametric maps were generated and matched in the singular value domain to extended phase graph-based dictionaries. The proposed approach was tested in phantoms and 10 healthy subjects and compared against conventional methods in terms of coefficients of determination and best fits for the phantom study, and in terms of Bland-Altman agreement, average values and coefficient of variation of T1 , T2 , and T1⍴ for the healthy subjects study. RESULTS The T1 , T2 , and T1⍴ MRF values showed excellent correlation with conventional spin-echo and clinical mapping methods in phantom studies (r2 > 0.97). Measured MRF values in myocardial tissue (mean ± SD) were 1133 ± 33 ms, 38.8 ± 3.5 ms, and 52.0 ± 4.0 ms for T1 , T2 and T1⍴ , respectively, against 1053 ± 47 ms, 50.4 ± 3.9 ms, and 55.9 ± 3.3 ms for T1 modified Look-Locker inversion imaging, T2 gradient and spin echo, and T1⍴ turbo field echo, respectively. CONCLUSION A cardiac MRF approach for simultaneous quantification of myocardial T1 , T2 , and T1ρ in a single breath-hold MR scan of about 16 seconds has been proposed. The approach has been investigated in phantoms and healthy subjects showing good agreement with reference spin echo measurements and conventional clinical maps.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University of Madrid, Madrid, Spain
| | - Alina Hua
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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Zhu Y, Liu Y, Ying L, Qiu Z, Liu Q, Jia S, Wang H, Peng X, Liu X, Zheng H, Liang D. A 4-minute solution for submillimeter whole-brain T 1ρ quantification. Magn Reson Med 2021; 85:3299-3307. [PMID: 33421224 DOI: 10.1002/mrm.28656] [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: 08/14/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 12/12/2022]
Abstract
PURPOSE To develop a robust, accurate, and accelerated T1ρ quantification solution for submillimeter in vivo whole-brain imaging. METHODS A multislice T1ρ mapping solution (MS-T1ρ ) was developed based on a two-acquisition scheme using turbo spin echo with RF cycling to allow for whole-brain coverage with 0.8-mm in-plane resolution. A compressed sensing-based fast imaging method, SCOPE, was used to accelerate the MS-T1ρ acquisition time to a total scan time of 3 minutes 31 seconds. A phantom experiment was conducted to assess the accuracy of MS-T1ρ by comparing the T1ρ value obtained using MS-T1ρ with the reference value obtained using the standard single-slice T1ρ mapping method. In vivo scans of 13 volunteers were acquired prospectively to validate the robustness of MS-T1ρ . RESULTS In the phantom study, the T1ρ values obtained with MS-T1ρ were in good agreement with the reference T1ρ values (R2 = 0.9991) and showed high consistency throughout all slices (coefficient of variation = 2.2 ± 2.43%). In the in vivo experiments, T1ρ maps were successfully acquired for all volunteers with no visually noticeable artifacts. There was no significant difference in T1ρ values between MS-T1ρ acquisitions and fully sampled acquisitions for all brain tissues (p-value > .05). In the intraclass correlation coefficient and Bland-Altman analyses, the accelerated T1ρ measurements show moderate to good agreement to the fully sampled reference values. CONCLUSION The proposed MS-T1ρ solution allows for high-resolution whole-brain T1ρ mapping within 4 minutes and may provide a potential tool for investigating neural diseases.
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Affiliation(s)
- Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Yuanyuan Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Leslie Ying
- Department of Biomedical Engineering and Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, 14260, USA
| | - Zhilang Qiu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, Jiangxi, 330031, China
| | - Sen Jia
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Haifeng Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Xi Peng
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Xin Liu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
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8
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Ai QYH, Zhang H, Jiang B, So TY, Mo FKF, Qamar S, Chen W, King AD. Test-retest repeatability of T1rho (T1ρ) MR imaging in the head and neck. Eur J Radiol 2020; 135:109489. [PMID: 33395595 DOI: 10.1016/j.ejrad.2020.109489] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/15/2020] [Accepted: 12/18/2020] [Indexed: 12/27/2022]
Abstract
PURPOSE T1rho imaging is a new quantitative MRI sequence for head and neck cancer and the repeatability for this region is unknown. This study aimed to evaluate the repeatability of quantitative T1rho imaging in the head and neck. MATERIALS AND METHODS T1rho imaging of the head and neck was prospectively performed in 15 healthy participants on three occasions. Scan 1 and 2 were performed with a time interval of 30 minutes (intra-session) and scan 3 was performed 14 days later (inter-session). T1rho values for normal tissues (parotid glands, palatine tonsils, pterygoid muscles, and tongue) were obtained on each scan. Intra-class coefficients (ICCs), within-subject coefficient of variances (wCoVs), and repeatability coefficient (RCs) of the intra-session scan (scan 1 vs 2) and inter-session scan (scan 1 vs 3) for the normal tissues were calculated. RESULTS The ICCs of T1rho values for normal tissues were almost perfect (0.83-0.97) for intra-session scans and were substantial (0.71-0.80) for inter-session scans. The wCoVs showed a small range (2.46%-3.30%) for intra-session scans, and slightly greater range (3.27%-6.51%) for inter-session scan. The greatest and lowest wCoVs of T1rho were found in the parotid gland and muscles, respectively. The T1rho RCs varied for all tissues between intra- and inter- sessions, and the greatest RC of 10.07 msec was observed for parotid gland on inter-session scan. CONCLUSION T1rho imaging is a repeatable quantitative MRI sequence in the head and neck but variances of T1rho values among tissues should be take into account during analysis.
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Affiliation(s)
- Qi Yong H Ai
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong.
| | - Huimin Zhang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Baiyan Jiang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Tiffany Y So
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Frankie K F Mo
- Department of Clinical Oncology, State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Sahrish Qamar
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Weitian Chen
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
| | - Ann D King
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong
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9
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Ai QYH, Chen W, So TY, Lam WKJ, Jiang B, Poon DMC, Qamar S, Mo FKF, Blu T, Chan Q, Ma BBY, Hui EP, Chan KCA, King AD. Quantitative T1ρ MRI of the Head and Neck Discriminates Carcinoma and Benign Hyperplasia in the Nasopharynx. AJNR Am J Neuroradiol 2020; 41:2339-2344. [PMID: 33122214 DOI: 10.3174/ajnr.a6828] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 08/07/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE T1ρ imaging is a new quantitative MR imaging pulse sequence with the potential to discriminate between malignant and benign tissue. In this study, we evaluated the capability of T1ρ imaging to characterize tissue by applying T1ρ imaging to malignant and benign tissue in the nasopharynx and to normal tissue in the head and neck. MATERIALS AND METHODS Participants with undifferentiated nasopharyngeal carcinoma and benign hyperplasia of the nasopharynx prospectively underwent T1ρ imaging. T1ρ measurements obtained from the histogram analysis for nasopharyngeal carcinoma in 43 participants were compared with those for benign hyperplasia and for normal tissue (brain, muscle, and parotid glands) in 41 participants using the Mann-Whitney U test. The area under the curve of significant T1ρ measurements was calculated and compared using receiver operating characteristic analysis and the Delong test, respectively. A P < . 05 indicated statistical significance. RESULTS There were significant differences in T1ρ measurements between nasopharyngeal carcinoma and benign hyperplasia and between nasopharyngeal carcinoma and normal tissue (all, P < . 05). Compared with benign hyperplasia, nasopharyngeal carcinoma showed a lower T1ρ mean (62.14 versus 65.45 × ms), SD (12.60 versus 17.73 × ms), and skewness (0.61 versus 0.76) (all P < .05), but no difference in kurtosis (P = . 18). The T1ρ SD showed the highest area under the curve of 0.95 compared with the T1ρ mean (area under the curve = 0.72) and T1ρ skewness (area under the curve = 0.72) for discriminating nasopharyngeal carcinoma and benign hyperplasia (all, P < .05). CONCLUSIONS Quantitative T1ρ imaging has the potential to discriminate malignant from benign and normal tissue in the head and neck.
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Affiliation(s)
- Q Y H Ai
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - W Chen
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - T Y So
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - W K J Lam
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Chemical Pathology (W.K.J.L., K.C.A.C.), State Key Laboratory in Oncology in South China, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR
| | - B Jiang
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - D M C Poon
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Clinical Oncology (D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H.), State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, SAR.,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.)
| | - S Qamar
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
| | - F K F Mo
- Department of Clinical Oncology (D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H.), State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, SAR.,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.)
| | - T Blu
- Department of Electrical Engineering (T.B.), The Chinese University of Hong Kong, Hong Kong, SAR
| | - Q Chan
- Philips Healthcare (Q.C.), Hong Kong, SAR
| | - B B Y Ma
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Clinical Oncology (D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H.), State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, SAR.,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.)
| | - E P Hui
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Clinical Oncology (D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H.), State Key Laboratory in Oncology in South China, Sir Y.K. Pao Centre for Cancer, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, SAR.,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.)
| | - K C A Chan
- Li Ka Shing Institute of Health Sciences (W.K.J.L., D.M.C.P., B.B.Y.M., E.P.H., K.C.A.C.).,State Key Laboratory of Translational Oncology (W.K.J.L., D.M.C.P., F.K.F.M., B.B.Y.M., E.P.H., K.C.A.C.).,Department of Chemical Pathology (W.K.J.L., K.C.A.C.), State Key Laboratory in Oncology in South China, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, SAR
| | - A D King
- From the Department of Imaging and Interventional Radiology (Q.Y.H.A., W.C., T.Y.S., B.J., S.Q., A.D.K.)
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10
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Wyatt CR, Barbara TM, Guimaraes AR. T 1ρ magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2020; 33:e4284. [PMID: 32125050 PMCID: PMC8818303 DOI: 10.1002/nbm.4284] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 02/03/2020] [Accepted: 02/05/2020] [Indexed: 05/15/2023]
Abstract
T1ρ relaxation imaging is a quantitative imaging technique that has been used to assess cartilage integrity, liver fibrosis, tumors, cardiac infarction, and Alzheimer's disease. T1 , T2 , and T1ρ relaxation time constants have each demonstrated different degrees of sensitivity to several markers of fibrosis and inflammation, allowing for a potential multi-parametric approach to tissue quantification. Traditional magnetic resonance fingerprinting (MRF) has been shown to provide quick, quantitative mapping of T1 and T2 relaxation time constants. In this study, T1ρ relaxation is added to the MRF framework using spin lock preparations. An MRF sequence involving an RF-spoiled sequence with TR , flip angle, T1ρ , and T2 preparation variation is described. The sequence is then calibrated against conventional T1 , T2 , and T1ρ relaxation mapping techniques in agar phantoms and the abdomens of four healthy volunteers. Strong intraclass correlation coefficients (ICC > 0.9) were found between conventional and MRF sequences in phantoms and also in healthy volunteers (ICC > 0.8). The highest ICC correlation values were seen in T1 , followed by T1ρ and then T2 . In this study, T1ρ relaxation has been incorporated into the MRF framework by using spin lock preparations, while still fitting for T1 and T2 relaxation time constants. The acquisition of these parameters within a single breath hold in the abdomen alleviates the issues of movement between breath holds in conventional techniques.
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Affiliation(s)
- Cory R. Wyatt
- Advanced Imaging Research Center, Oregon Health & Sciences University, Portland, OR 97239
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, OR 97239
| | - Thomas M. Barbara
- Advanced Imaging Research Center, Oregon Health & Sciences University, Portland, OR 97239
| | - Alexander R. Guimaraes
- Advanced Imaging Research Center, Oregon Health & Sciences University, Portland, OR 97239
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, OR 97239
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11
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Zhu Y, Liu Y, Ying L, Liu X, Zheng H, Liang D. Bio-SCOPE: fast biexponential T 1ρ mapping of the brain using signal-compensated low-rank plus sparse matrix decomposition. Magn Reson Med 2019; 83:2092-2106. [PMID: 31762102 DOI: 10.1002/mrm.28067] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 09/28/2019] [Accepted: 10/14/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop and evaluate a fast imaging method based on signal-compensated low-rank plus sparse matrix decomposition to accelerate data acquisition for biexponential brain T1ρ mapping (Bio-SCOPE). METHODS Two novel strategies were proposed to improve reconstruction performance. A variable-rate undersampling scheme was used with a varied acceleration factor for each k-space along the spin-lock time direction, and a modified nonlinear thresholding scheme combined with a feature descriptor was used for Bio-SCOPE reconstruction. In vivo brain T1ρ mappings were acquired from 4 volunteers. The fully sampled k-space data acquired from 3 volunteers were retrospectively undersampled by net acceleration rates (R) of 4.6 and 6.1. Reference values were obtained from the fully sampled data. The agreement between the accelerated T1ρ measurements and reference values was assessed with Bland-Altman analyses. Prospectively undersampled data with R = 4.6 and R = 6.1 were acquired from 1 volunteer. RESULTS T1ρ -weighted images were successfully reconstructed using Bio-SCOPE for R = 4.6 and 6.1 with signal-to-noise ratio variations <1 dB and normalized root mean square errors <4%. Accelerated and reference T1ρ measurements were in good agreement for R = 4.6 (T1ρ s : 18.6651 ± 1.7786 ms; T1ρ l : 88.9603 ± 1.7331 ms) and R = 6.1 (T1ρ s : 17.8403 ± 3.3302 ms; T1ρ l : 88.0275 ± 4.9606 ms) in the Bland-Altman analyses. T1ρ parameter maps from prospectively undersampled data also show reasonable image quality using the Bio-SCOPE method. CONCLUSION Bio-SCOPE achieves a high net acceleration rate for biexponential T1ρ mapping and improves reconstruction quality by using a variable-rate undersampling data acquisition scheme and a modified soft-thresholding algorithm in image reconstruction.
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Affiliation(s)
- Yanjie Zhu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yuanyuan Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,University of Chinese Academy of Sciences, Beijing, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Leslie Ying
- Department of Biomedical Engineering and Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, New York
| | - Xin Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Dong Liang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Qin J, Li Y, Liang D, Zhang Y, Yao W. Histogram analysis of absolute cerebral blood volume map can distinguish glioblastoma from solitary brain metastasis. Medicine (Baltimore) 2019; 98:e17515. [PMID: 31626111 PMCID: PMC6824738 DOI: 10.1097/md.0000000000017515] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Glioblastoma multiforme (GBM) is difficult to be separated from solitary brain metastasis (sBM) in clinical practice. This study aimed to distinguish two entities by the histogram analysis of absolute cerebral blood volume (CBV) map.From March 2016 to June 2018, 24 patients with GBM and 18 patients with sBM were included in this retrospective study. The enhancing area was first segmented on the post-contrast T1WI, then the segmentation was copied to the absolute CBV map and histogram analysis was finally performed. Unpaired t test was used to select the features that could separate two entities and receiving operating curve was used to test the diagnostic performance. Finally, a machine learning method was used to test the diagnostic performance combing all the selected features.Six of 19 features were feasible to distinguish GBM from sBM (all P < .001), among which energy had the highest diagnostic performance (area under curve, 0.84; accuracy, 88%), while a machine learning method could improve the diagnostic performance (area under curve, 0.94; accuracy, 95%).Histogram analysis of the absolute CBV in the enhancing area could help us distinguish GBM from sBM, in addition, a machine learning method with combined features is preferable. It is quite helpful in the condition that the biological nature of peritumoral edema could not separate these two entities.
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Affiliation(s)
- Jianhua Qin
- School of Medicine, Qingdao University, Qingdao
- Department of Radiology, Rizhao Central Hospital, Rizhao, P. R. China
| | | | - Donghai Liang
- Department of Radiology, Rizhao Central Hospital, Rizhao, P. R. China
| | - Yuanna Zhang
- Department of Radiology, Rizhao Central Hospital, Rizhao, P. R. China
| | - Weicheng Yao
- Department of Neurosurgery, The Affiliated Hospital of Qingdao University, China
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Zhang X, Lu X, Liu Z, Guan R, Wang J, Kong X, Chen L, Bo C, Tian K, Xu S, Bai M, Zhang H, Li J, Wang L, Shen J, Guo M. Integrating multiple-level molecular data to infer the distinctions between glioblastoma and lower-grade glioma. Int J Cancer 2019; 145:952-961. [PMID: 30694558 DOI: 10.1002/ijc.32174] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/28/2018] [Accepted: 01/08/2019] [Indexed: 01/25/2023]
Abstract
Glioblastomas (GBMs) and lower-grade gliomas (LGGs) are the most common malignant brain tumors. Despite extensive studies that have suggested that there are differences between the two in terms of clinical profile and treatment, their distinctions on a molecular level had not been systematically analyzed. Here, we investigated the distinctions between GBM and LGG based on multidimensional data, including somatic mutations, somatic copy number variants (SCNVs), gene expression, lncRNA expression and DNA methylation levels. We found that GBM patients had a higher mutation frequency and SCNVs than LGG patients. Differential mRNAs and lncRNAs between GBM and LGG were identified and a differential mRNA-lncRNA network was constructed and analyzed. We also discovered some differential DNA methylation sites could distinguish between GBM and LGG samples. Finally, we identified some key GBM- and LGG-specific genes featuring multiple-level molecular alterations. These specific genes participate in diverse functions; moreover, GBM-specific genes are enriched in the glioma pathway. Overall, our studies explored the distinctions between GMB and LGG using a comprehensive genomics approach that may provide novel insights into studying the mechanism and treatment of brain tumors.
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Affiliation(s)
- Xiaoming Zhang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xiaoyu Lu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Zhaojun Liu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Ruoyu Guan
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Jianjian Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Xiaotong Kong
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Lixia Chen
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Chunrui Bo
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Kuo Tian
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Si Xu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Ming Bai
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Huixue Zhang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Jie Li
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Lihua Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Jia Shen
- Division of Growth and Development and Section of Orthodontics, School of Dentistry, University of California, Los Angeles, CA
| | - Mian Guo
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
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Brain T1ρ mapping for grading and IDH1 gene mutation detection of gliomas: a preliminary study. J Neurooncol 2018; 141:245-252. [PMID: 30414094 DOI: 10.1007/s11060-018-03033-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 10/15/2018] [Indexed: 01/08/2023]
Abstract
INTRODUCTION The longitudinal relaxation time in the rotating frame (T1ρ) has proved to be sensitive to metabolism and useful in application to neurodegenerative diseases. However, few literature exists on its utility in gliomas. Thus, this study was conducted to explore the performance of T1ρ mapping in tumor grading and characterization of isocitrate dehydrogenase 1 (IDH1) gene mutation status of gliomas. METHODS Fifty-seven patients with gliomas underwent brain MRI and quantitative measurements of T1ρ and apparent diffusion coefficient (ADC) were recorded. Parameters were compared between high-grade gliomas (HGG) and low-grade gliomas (LGG) and between IDH1 mutant and wildtype groups. RESULTS HGG showed significantly higher T1ρ values in both the solid and peritumoral edema areas compared with LGG (P < 0.001 and P = 0.005, respectively), whereas no significant differences in the two areas were found for ADC (both P > 0.05). Receiver operating characteristic (ROC) curve analysis showed that T1ρ value in the solid area achieved the highest area under the ROC curve (AUC, 0.841) in grading with a sensitivity of 80.6% and a specificity of 81.0%. In the grade II/III glioma group, multivariate logistic regression showed that both tumor frontal lobe location (odds ratio [OR] 526.608; P = 0.045) and T1ρ value of the peritumoral edema area (OR 0.863; P = 0.037) were significant predictors of IDH1 mutation. Using the combination, the diagnostic sensitivity and specificity for IDH1 mutated gliomas were 93.3% and 88.9%, respectively. CONCLUSIONS Our study shows the feasibility of applying T1ρ mapping in assessing the histologic grade and IDH1 mutation status of gliomas.
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