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Xie Y, Zhang S, Wu D, Yao Y, Cho J, Lu J, Zhu H, Wang Y, Zhang Y, Zhu W. The changes of oxygen extraction fraction in different types of lesions in relapsing-remitting multiple sclerosis: A cross-sectional and longitudinal study. Neurol Sci 2024; 45:3939-3949. [PMID: 38492126 DOI: 10.1007/s10072-024-07463-2] [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: 12/17/2023] [Accepted: 03/11/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVES To explore the oxygen metabolism level of different types of lesions in relapsing-remitting multiple sclerosis (RRMS) patients by oxygen extraction fraction (OEF) both cross-sectionally and longitudinally. METHODS Forty-six RRMS patients and forty-one healthy controls (HC) went MRI examination. The quantitative susceptibility mapping (QSM) and OEF map were reconstructed from a 3D multi-echo gradient echo sequence. MS lesions in white matter were classified as contrast-enhancing lesions (CELs) on post-gadolinium T1-weighted sequence, paramagnetic rim lesions (PRLs), hyperintense lesions and non-hyperintense lesions on QSM, respectively. The susceptibility and OEF of different types of lesions were compared. The susceptibility and OEF values were measured and compared among different types of lesions. Among these RRMS patients, seventeen had follow-up MRI and 232 lesions, and baseline to follow-up longitudinal changes in susceptibility and OEF were measured. RESULTS PRLs had higher susceptibility and lower OEF than CELs, hyperintense lesions, and non-hyperintense lesions. The hyperintense lesions had higher susceptibility and lower OEF than non-hyperintense lesions. In longitudinal changes, PRLs had susceptibility increased (P < 0.001) and OEF decreased (P < 0.001). The hyperintense lesions showed significant decreases in susceptibility (P = 0.020), and non-hyperintense lesions showed significant increases in OEF during follow-up (P = 0.005). Notably, hyperintense lesions may convert to PRLs or non-hyperintense lesions as time progresses, accompanied by changes of OEF and susceptibility in the lesions. CONCLUSION This study revealed tissue damage and oxygen metabolism level in different types of MS lesions. The OEF may contribute to further understanding the evolution of MS lesions.
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Affiliation(s)
- Yan Xie
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Di Wu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Yihao Yao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Junghun Cho
- Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, 14260, USA
| | - Jun Lu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Yan Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, People's Republic of China.
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Homssi M, Sweeney EM, Demmon E, Mannheim W, Sakirsky M, Wang Y, Gauthier SA, Gupta A, Nguyen TD. Evaluation of the Statistical Detection of Change Algorithm for Screening Patients with MS with New Lesion Activity on Longitudinal Brain MRI. AJNR Am J Neuroradiol 2023; 44:649-655. [PMID: 37142431 PMCID: PMC10249703 DOI: 10.3174/ajnr.a7858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/03/2023] [Indexed: 05/06/2023]
Abstract
BACKGROUND AND PURPOSE Identification of new MS lesions on longitudinal MR imaging by human readers is time-consuming and prone to error. Our objective was to evaluate the improvement in the performance of subject-level detection by readers when assisted by the automated statistical detection of change algorithm. MATERIALS AND METHODS A total of 200 patients with MS with a mean interscan interval of 13.2 (SD, 2.4) months were included. Statistical detection of change was applied to the baseline and follow-up FLAIR images to detect potential new lesions for confirmation by readers (Reader + statistical detection of change method). This method was compared with readers operating in the clinical workflow (Reader method) for a subject-level detection of new lesions. RESULTS Reader + statistical detection of change found 30 subjects (15.0%) with at least 1 new lesion, while Reader detected 16 subjects (8.0%). As a subject-level screening tool, statistical detection of change achieved a perfect sensitivity of 1.00 (95% CI, 0.88-1.00) and a moderate specificity of 0.67 (95% CI, 0.59-0.74). The agreement on a subject level was 0.91 (95% CI, 0.87-0.95) between Reader + statistical detection of change and Reader, and 0.72 (95% CI, 0.66-0.78) between Reader + statistical detection of change and statistical detection of change. CONCLUSIONS The statistical detection of change algorithm can serve as a time-saving screening tool to assist human readers in verifying 3D FLAIR images of patients with MS with suspected new lesions. Our promising results warrant further evaluation of statistical detection of change in prospective multireader clinical studies.
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Affiliation(s)
- M Homssi
- From the Department of Radiology (M.H., Y.W., A.G., T.D.N.)
| | - E M Sweeney
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics (E.M.S.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - E Demmon
- Department of Neurology (E.D., W.M., M.S., S.A.G.)
| | - W Mannheim
- Department of Neurology (E.D., W.M., M.S., S.A.G.)
| | - M Sakirsky
- Department of Neurology (E.D., W.M., M.S., S.A.G.)
| | - Y Wang
- From the Department of Radiology (M.H., Y.W., A.G., T.D.N.)
| | - S A Gauthier
- Department of Neurology (E.D., W.M., M.S., S.A.G.)
- The Feil Family Brain & Mind Institute (S.A.G.), Weill Cornell Medicine, New York, New York
| | - A Gupta
- From the Department of Radiology (M.H., Y.W., A.G., T.D.N.)
| | - T D Nguyen
- From the Department of Radiology (M.H., Y.W., A.G., T.D.N.)
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Jung W, Bollmann S, Lee J. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR IN BIOMEDICINE 2022; 35:e4292. [PMID: 32207195 DOI: 10.1002/nbm.4292] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/04/2020] [Accepted: 02/25/2020] [Indexed: 06/10/2023]
Abstract
Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.
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Affiliation(s)
- Woojin Jung
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
| | - Steffen Bollmann
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Jongho Lee
- Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea
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Kim J, Nguyen TD, Zhang J, Gauthier SA, Marcille M, Zhang H, Cho J, Spincemaille P, Wang Y. Subsecond accurate myelin water fraction reconstruction from FAST-T 2 data with 3D UNET. Magn Reson Med 2022; 87:2979-2988. [PMID: 35092094 DOI: 10.1002/mrm.29176] [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: 08/28/2021] [Revised: 12/31/2021] [Accepted: 01/08/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE To develop a 3D UNET convolutional neural network for rapid extraction of myelin water fraction (MWF) maps from six-echo fast acquisition with spiral trajectory and T2 -prep data and to evaluate its accuracy in comparison with multilayer perceptron (MLP) network. METHODS The MWF maps were extracted from 138 patients with multiple sclerosis using an iterative three-pool nonlinear least-squares algorithm (NLLS) without and with spatial regularization (srNLLS), which were used as ground-truth labels to train, validate, and test UNET and MLP networks as a means to accelerate data fitting. Network testing was performed in 63 patients with multiple sclerosis and a numerically simulated brain phantom at SNR of 200, 100 and 50. RESULTS Simulations showed that UNET reduced the MWF mean absolute error by 30.1% to 56.4% and 16.8% to 53.6% over the whole brain and by 41.2% to 54.4% and 21.4% to 49.4% over the lesions for predicting srNLLS and NLLS MWF, respectively, compared to MLP, with better performance at lower SNRs. UNET also outperformed MLP for predicting srNLLS MWF in the in vivo multiple-sclerosis brain data, reducing mean absolute error over the whole brain by 61.9% and over the lesions by 67.5%. However, MLP yielded 41.1% and 51.7% lower mean absolute error for predicting in vivo NLLS MWF over the whole brain and the lesions, respectively, compared with UNET. The whole-brain MWF processing time using a GPU was 0.64 seconds for UNET and 0.74 seconds for MLP. CONCLUSION Subsecond whole-brain MWF extraction from fast acquisition with spiral trajectory and T2 -prep data using UNET is feasible and provides better accuracy than MLP for predicting MWF output of srNLLS algorithm.
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Affiliation(s)
- Jeremy Kim
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jinwei Zhang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Susan A Gauthier
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Melanie Marcille
- Department of Neurology, Weill Cornell Medicine, New York, New York, USA
| | - Hang Zhang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | - Junghun Cho
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | | | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA
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Meca-Lallana J, García-Merino JA, Martínez-Yélamos S, Vidal-Jordana A, Costa L, Eichau S, Rovira À, Brieva L, Agüera E, Zarranz ARA. Identification of patients with relapsing multiple sclerosis eligible for high-efficacy therapies. Neurodegener Dis Manag 2021; 11:251-261. [PMID: 33966475 DOI: 10.2217/nmt-2020-0049] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Relapsing multiple sclerosis (RMS) presents a highly variable clinical evolution among patients, and its management should be personalized. Although there is no cure at present, effective disease-modifying therapies (DMTs) are available. Selection of the most appropriate DMT for each patient is influenced by several clinical, radiological and demographic aspects as well as personal preferences that, at times, are not covered in the regulatory criteria. This may be a source of difficulty, especially in certain situations where so-called 'high-efficacy DMTs' (usually considered second-line) could be of greater benefit to the patient. In this narrative review, we discuss evidence and experience, and propose a pragmatic guidance on decision-making with respect to the indication and management of high-efficacy DMT in adult patients with RMS based on expert opinion.
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Affiliation(s)
- José Meca-Lallana
- Multiple Sclerosis CSUR, Neurology Department, Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, 30120, Spain
| | | | - Sergio Martínez-Yélamos
- Neurology Department, Hospital Universitario de Bellvitge, L'Hospitalet de Llobregat, 08907, Spain
| | - Angela Vidal-Jordana
- Neurology-Neuroimmunology Department, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitario Vall d'Hebron, Barcelona, 08035, Spain
| | - Lucienne Costa
- CSUR de Esclerosis Múltiple, Neurology Department, Fundación para la Investigación Biomédica IRyCIS, Hospital Universitario Ramón y Cajal, Madrid, 28034, Spain
| | - Sara Eichau
- EM Unit, Neurology Department, Hospital Universitario Virgen de la Macarena, Seville, 41009, Spain
| | - Àlex Rovira
- Neuroradiology Section, Radiology Department, Hospital Universitario Vall d'Hebron, Barcelona, 08035, Spain
| | - Luis Brieva
- Neurology Section, Hospital Universitario Arnau de Vilanova, IRB Lleida, Lleida, 25198, Spain
| | - Eduardo Agüera
- Neurology department, Hospital Universitario Reina Sofía, Cordoba, 14004, Spain
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Thaler C, Kyselyova AA, Faizy TD, Nawka MT, Jespersen S, Hansen B, Stellmann JP, Heesen C, Stürner KH, Stark M, Fiehler J, Bester M, Gellißen S. Heterogeneity of multiple sclerosis lesions in fast diffusional kurtosis imaging. PLoS One 2021; 16:e0245844. [PMID: 33539364 PMCID: PMC7861404 DOI: 10.1371/journal.pone.0245844] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 01/09/2021] [Indexed: 12/14/2022] Open
Abstract
Background Mean kurtosis (MK), one of the parameters derived from diffusion kurtosis imaging (DKI), has shown increased sensitivity to tissue microstructure damage in several neurological disorders. Methods Thirty-seven patients with relapsing-remitting MS and eleven healthy controls (HC) received brain imaging on a 3T MR scanner, including a fast DKI sequence. MK and mean diffusivity (MD) were measured in the white matter of HC, normal-appearing white matter (NAWM) of MS patients, contrast-enhancing lesions (CE-L), FLAIR lesions (FLAIR-L) and black holes (BH). Results Overall 1529 lesions were analyzed, including 30 CE-L, 832 FLAIR-L and 667 BH. Highest MK values were obtained in the white matter of HC (0.814 ± 0.129), followed by NAWM (0.724 ± 0.137), CE-L (0.619 ± 0.096), FLAIR-L (0.565 ± 0.123) and BH (0.549 ± 0.12). Lowest MD values were obtained in the white matter of HC (0.747 ± 0.068 10−3mm2/sec), followed by NAWM (0.808 ± 0.163 10−3mm2/sec), CE-L (0.853 ± 0.211 10−3mm2/sec), BH (0.957 ± 0.304 10−3mm2/sec) and FLAIR-L (0.976 ± 0.35 10−3mm2/sec). While MK differed significantly between CE-L and non-enhancing lesions, MD did not. Conclusion MK adds predictive value to differentiate between MS lesions and might provide further information about diffuse white matter injury and lesion microstructure.
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Affiliation(s)
- Christian Thaler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- * E-mail:
| | - Anna A. Kyselyova
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias D. Faizy
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marie T. Nawka
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sune Jespersen
- Department of Clinical Medicine - Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Brian Hansen
- Department of Clinical Medicine - Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Jan-Patrick Stellmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Neuroimmunology and Clinical MS Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- APHM, Hospital de la Timone, CEMEREM, Marseille, France
- Aix Marseille University, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Christoph Heesen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Neuroimmunology and Clinical MS Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Klarissa H. Stürner
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Neuroimmunology and Clinical MS Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Maria Stark
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Maxim Bester
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Susanne Gellißen
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Vinayagamani S, Sabarish S, Nair SS, Tandon V, Kesavadas C, Thomas B. Quantitative susceptibility-weighted imaging in predicting disease activity in multiple sclerosis. Neuroradiology 2021; 63:1061-1069. [PMID: 33403447 DOI: 10.1007/s00234-020-02605-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 11/10/2020] [Indexed: 10/22/2022]
Abstract
PURPOSE Repeated use of Gadolinium (Gd) contrast for multiple sclerosis (MS) imaging leads to Gd deposition in brain. We aimed to study the utility of phase values by susceptibility weighted imaging (SWI) to assess the iron content in MS lesions to differentiate active and inactive lesions. METHODS MS persons who underwent MRI were grouped into group 1 with active lesions and group 2 with inactive lesions based on the presence or absence of contrast enhancing lesions. Phase values of lesions (PL) and contralateral normal white matter (PN) were calculated using the SPIN software by drawing ROI. Subtracted phase values (PS = PL - PN) and iron content (PS/3) of the lesions were calculated in both groups. RESULTS We analyzed 69 enhancing lesions from 22 patients (group 1) and 84 non-enhancing lesions from 29 patients (group 2). Mean-subtracted phase values and iron content corrected for voxels in ROI were significantly lower in enhancing lesions compared to non-enhancing lesions (p < 0.001). A cut-off value 2.8 μg/g for iron content showed area under the curve of 0.909 with good sensitivity. CONCLUSION Quantification of iron content using SWI phase values holds promise as a biomarker to differentiate active from inactive lesions of MS.
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Affiliation(s)
- Selvadasan Vinayagamani
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - Sekar Sabarish
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - Sruthi S Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - Vaibhav Tandon
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - Chandrasekharan Kesavadas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India
| | - Bejoy Thomas
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, 695011, India.
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Afzal HMR, Luo S, Ramadan S, Lechner-Scott J. The emerging role of artificial intelligence in multiple sclerosis imaging. Mult Scler 2020; 28:849-858. [PMID: 33112207 DOI: 10.1177/1352458520966298] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Computer-aided diagnosis can facilitate the early detection and diagnosis of multiple sclerosis (MS) thus enabling earlier interventions and a reduction in long-term MS-related disability. Recent advancements in the field of artificial intelligence (AI) have led to the improvements in the classification, quantification and identification of diagnostic patterns in medical images for a range of diseases, in particular, for MS. Importantly, data generated using AI techniques are analyzed automatically, which compares favourably with labour-intensive and time-consuming manual methods. OBJECTIVE The aim of this review is to assist MS researchers to understand current and future developments in the AI-based diagnosis and prognosis of MS. METHODS We will investigate a variety of AI approaches and various classifiers and compare the current state-of-the-art techniques in relation to lesion segmentation/detection and prognosis of disease. After briefly describing the magnetic resonance imaging (MRI) techniques commonly used, we will describe AI techniques used for the detection of lesions and MS prognosis. RESULTS We then evaluate the clinical maturity of these AI techniques in relation to MS. CONCLUSION Finally, future research challenges are identified in a bid to encourage further improvements of the methods.
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Affiliation(s)
- H M Rehan Afzal
- School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia/Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Suhuai Luo
- School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia
| | - Saadallah Ramadan
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia/School of Medicine and Public Health, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW, Australia
| | - Jeannette Lechner-Scott
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia/School of Medicine and Public Health, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW, Australia/Department of Neurology, John Hunter Hospital, New Lambton Heights, NSW, Australia
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9
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Sweeney EM, Nguyen TD, Kuceyeski A, Ryan SM, Zhang S, Zexter L, Wang Y, Gauthier SA. Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging. Neuroimage 2020; 225:117451. [PMID: 33069865 PMCID: PMC7805079 DOI: 10.1016/j.neuroimage.2020.117451] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 10/02/2020] [Accepted: 10/08/2020] [Indexed: 01/17/2023] Open
Abstract
We introduce the first-ever statistical framework for estimating the age of Multiple Sclerosis (MS) lesions from magnetic resonance imaging (MRI). Estimating lesion age is an important step when studying the longitudinal behavior of MS lesions and can be used in applications such as studying the temporal dynamics of chronic active MS lesions. Our lesion age estimation models use first order radiomic features over a lesion derived from conventional T1 (T1w) and T2 weighted (T2w) and fluid attenuated inversion recovery (FLAIR), T1w with gadolinium contrast (T1w+c), and Quantitative Susceptibility Mapping (QSM) MRI sequences as well as demographic information. For this analysis, we have a total of 32 patients with 53 new lesions observed at 244 time points. A one or two step random forest model for lesion age is fit on a training set using a lesion volume cutoff of 15 mm3 or 50 mm3 . We explore the performance of nine different modeling scenarios that included various combinations of the MRI sequences and demographic information and a one or two step random forest models, as well as simpler models that only uses the mean radiomic feature from each MRI sequence. The best performing model on a validation set is a model that uses a two-step random forest model on the radiomic features from all of the MRI sequences with demographic information using a lesion volume cutoff of 50 mm3 . This model has a mean absolute error of 7.23 months (95% CI: [6.98, 13.43]) and a median absolute error of 5.98 months (95% CI: [5.26, 13.25]) in the validation set. For this model, the predicted age and actual age have a statistically significant association (p-value <0.001) in the validation set.
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Affiliation(s)
- Elizabeth M Sweeney
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, United States.
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States; Brain and Mind Institute, Weill Cornell Medical College, New York, NY, United States
| | - Sarah M Ryan
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Wuhan, China
| | - Lily Zexter
- Department of Neurology, Weill Cornell Medical College, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States
| | - Susan A Gauthier
- Department of Radiology, Weill Cornell Medical College, New York, NY, United States; Brain and Mind Institute, Weill Cornell Medical College, New York, NY, United States; Department of Neurology, Weill Cornell Medical College, New York, NY, United States
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10
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Vinayagamani S, Sheelakumari R, Sabarish S, Senthilvelan S, Ros R, Thomas B, Kesavadas C. Quantitative Susceptibility Mapping: Technical Considerations and Clinical Applications in Neuroimaging. J Magn Reson Imaging 2020; 53:23-37. [DOI: 10.1002/jmri.27058] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/28/2019] [Accepted: 12/30/2019] [Indexed: 12/12/2022] Open
Affiliation(s)
| | - R Sheelakumari
- Department of Imaging Sciences and Interventional Radiology Trivandrum India
| | - Sekar Sabarish
- Department of Imaging Sciences and Interventional Radiology Trivandrum India
| | | | - Roopa Ros
- Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology Trivandrum India
| | - Bejoy Thomas
- Department of Imaging Sciences and Interventional Radiology Trivandrum India
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Spincemaille P, Liu Z, Zhang S, Kovanlikaya I, Ippoliti M, Makowski M, Watts R, de Rochefort L, Venkatraman V, Desmond P, Santin MD, Lehéricy S, Kopell BH, Péran P, Wang Y. Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi-Site Reproducibility and Single-Site Robustness. J Neuroimaging 2019; 29:689-698. [PMID: 31379055 DOI: 10.1111/jon.12658] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 07/11/2019] [Accepted: 07/21/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND AND PURPOSE Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi-site, multi-vendor reproducibility study and a large, single-site, multi-scanner image quality review study in a clinical environment. METHODS A single healthy subject was scanned with a 3D multi-echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high-resolution (HiRes, .5 × .5 × 1 mm3 reconstructed) and standard-resolution (StdRes, .5 × .5 × 3 mm3 ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi-scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month. RESULTS Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients. CONCLUSION Online QSM reconstruction for a variety of sites and scanner platforms with low cross-site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients.
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Affiliation(s)
- Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Zhe Liu
- Department of Radiology, Weill Medical College of Cornell University, New York, NY.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
| | - Shun Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY.,Department of Radiology, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ilhami Kovanlikaya
- Department of Radiology, Weill Medical College of Cornell University, New York, NY
| | - Matteo Ippoliti
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Marcus Makowski
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Richard Watts
- Department of Psychology, Yale University, New Haven, CT
| | | | - Vijay Venkatraman
- Department of Medicine and Radiology, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Patricia Desmond
- Department of Medicine and Radiology, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Mathieu D Santin
- Inserm U 1127, CNRS UMR 7225, Centre for NeuroImaging Research, ICM (Brain & Spine Institute), Sorbonne University, Paris, France
| | - Stéphane Lehéricy
- Inserm U 1127, CNRS UMR 7225, Centre for NeuroImaging Research, ICM (Brain & Spine Institute), Sorbonne University, Paris, France.,Neuroradiology, Hôpital Pitié-Salpêtrière, Paris, France
| | - Brian H Kopell
- Division of Movement Disorders, Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Patrice Péran
- Toulouse NeuroImaging Center, Université de Toulouse Inserm, Toulouse, France
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY
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12
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Zhang S, Nguyen TD, Hurtado Rúa SM, Kaunzner UW, Pandya S, Kovanlikaya I, Spincemaille P, Wang Y, Gauthier SA. Quantitative Susceptibility Mapping of Time-Dependent Susceptibility Changes in Multiple Sclerosis Lesions. AJNR. AMERICAN JOURNAL OF NEURORADIOLOGY 2019; 40:987-993. [PMID: 31097429 DOI: 10.3174/ajnr.a6071] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 04/17/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE MR imaging studies have demonstrated that magnetic susceptibility in multiple sclerosis lesions is dependent on lesion age. The objective of this study was to use quantitative susceptibility mapping to determine whether lesions with a hyperintense rim, indicative of iron-laden inflammatory cells (rim+), follow a unique time-dependent trajectory of susceptibility change compared with those without (rim-). MATERIALS AND METHODS We studied patients with MS with at least 1 new gadolinium-enhancing lesion and at least 3 longitudinal quantitative susceptibility mapping scans obtained between 1.1 and 6.1 years. Lesions were classified as rim+ if a hyperintense rim appeared on quantitative susceptibility mapping at any time. A multilevel growth curve model compared longitudinal susceptibility among rim+ and rim- lesions. RESULTS Thirty-two new gadolinium-enhancing lesions from 19 patients with MS were included, and 16 lesions (50%) were identified as rim+. Quantitative susceptibility mapping rim+ lesions were larger than rim- lesions with gadolinium enhancement (P < .001). Among all lesions, susceptibility increased sharply after enhancement to a peak between 1 and 2 years followed by a decrease. The overall susceptibility curve height for rim- lesions was 4.27 parts per billion lower than that for rim+ lesions (P = .01). Rim- lesions demonstrated a higher linear slope relative to rim+ lesions (P = .023) but faster cubic decay relative to rim+ lesions (P = .005). Rim- lesions started decaying approximately 2 years earlier compared with rim+ lesions. CONCLUSIONS There was a marked difference in the susceptibility temporal trajectory between rim+ and rim- lesions during the first 6 years of lesion formation. Most rim+ lesions retain iron for years after the initial lesion appearance.
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Affiliation(s)
- S Zhang
- From the Departments of Radiology (S.Z., T.D.N., S.P., I.K., P.S., Y.W., S.A.G.).,Department of Radiology (S.Z.), Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - T D Nguyen
- From the Departments of Radiology (S.Z., T.D.N., S.P., I.K., P.S., Y.W., S.A.G.)
| | - S M Hurtado Rúa
- Department of Mathematics and Statistics (S.M.H.R.), College of Science and Health Professions, Cleveland State University, Cleveland, Ohio
| | | | - S Pandya
- From the Departments of Radiology (S.Z., T.D.N., S.P., I.K., P.S., Y.W., S.A.G.)
| | - I Kovanlikaya
- From the Departments of Radiology (S.Z., T.D.N., S.P., I.K., P.S., Y.W., S.A.G.)
| | - P Spincemaille
- From the Departments of Radiology (S.Z., T.D.N., S.P., I.K., P.S., Y.W., S.A.G.)
| | - Y Wang
- From the Departments of Radiology (S.Z., T.D.N., S.P., I.K., P.S., Y.W., S.A.G.).,Department of Biomedical Engineering (Y.W.), Cornell University, Ithaca, New York
| | - S A Gauthier
- From the Departments of Radiology (S.Z., T.D.N., S.P., I.K., P.S., Y.W., S.A.G.) .,Neurology (U.W.K., S.A.G.).,Feil Family Brain and Mind Research Institute (S.A.G.), Weill Cornell Medicine, New York, New York
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Zhang S, Liu Z, Nguyen TD, Yao Y, Gillen KM, Spincemaille P, Kovanlikaya I, Gupta A, Wang Y. Clinical feasibility of brain quantitative susceptibility mapping. Magn Reson Imaging 2019; 60:44-51. [PMID: 30954651 DOI: 10.1016/j.mri.2019.04.003] [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] [Received: 01/07/2019] [Revised: 03/31/2019] [Accepted: 04/02/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To evaluate the quality of brain quantitative susceptibility mapping (QSM) that is fully automatically reconstructed in clinical MRI of various neurological diseases. METHODS 393 consecutive patients in one month were recruited for this evaluation study. QSM was reconstructed using Morphology Enabled Dipole Inversion without zero reference regularization (MEDI) and using MEDI with cerebrospinal fluid automatic zero-reference regularization to generate susceptibility values (MEDI+0). Two neuroradiologists independently assessed the image quality of MEDI+0 and MEDI and image concordance between them. Lesion susceptibility values were measured in 20 cases of glioma, 21 cases of ischemic stroke and 43 multiple sclerosis (MS) cases on both MEDI+0 and MEDI images. RESULTS The two neuroradiologists rated the MEDI+0 image qualities of the 393 cases as 351 (89.3%) and 362 (92.1%) excellent, 29 (7.4%) and 24 (6.1%) diagnostic, and 13 (3.3%) and 7 (1.8%) poor, and scored the concordances between MEDI+0 and MEDI as 364 (92.6%) and 351 (89.3%) excellent, 13 (3.3%) and 31 (7.9%) good, 14 (3.6%) and 9 (2.3%) intermediate, 2 (0.5%) and 2 (0.5%) poor, and 0 (0%) and 0 (0%) none. There was good correlation between MEDI+0 and MEDI in lesion susceptibility contrast of glioma, ischemic stroke, and MS cases (all p < 0.05). The MS lesion susceptibility time course from this patient cohort was found to be similar to the reported pattern: isointense initially for acute enhancing lesions, and hyperintense over the following years for active chronic lesions. CONCLUSION Brain QSM images of various neurological diseases have reliable diagnostic quality in clinical MRI, with MEDI+0 providing susceptibility values automatically referenced to CSF in longitudinal and cross-center studies.
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Affiliation(s)
- Shun Zhang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhe Liu
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Yihao Yao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kelly M Gillen
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | | | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA; Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
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Lin F, Prince MR, Spincemaille P, Wang Y. Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism. Recent Pat Biotechnol 2018; 13:90-113. [PMID: 30556508 DOI: 10.2174/1872208313666181217112745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 12/04/2018] [Accepted: 12/11/2018] [Indexed: 01/06/2023]
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice. OBJECTIVE To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community. METHOD We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility. RESULTS We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase. CONCLUSION Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.
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Affiliation(s)
- Feng Lin
- School of Law, City University of Hong Kong, Hong Kong, China
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States.,Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States
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