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Fraser JF, Pahwa S, Maniskas M, Michas C, Martinez M, Pennypacker KR, Dornbos D. Now that the door is open: an update on ischemic stroke pharmacotherapeutics for the neurointerventionalist. J Neurointerv Surg 2024; 16:425-428. [PMID: 37258227 DOI: 10.1136/jnis-2022-019293] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 05/22/2023] [Indexed: 06/02/2023]
Abstract
The last 10 years have seen a major shift in management of large vessel ischemic stroke with changes towards ever-expanding use of reperfusion therapies (intravenous thrombolysis and mechanical thrombectomy). These strategies 'open the door' to acute therapeutics for ischemic tissue, and we should investigate novel therapeutic approaches to enhance survival of recently reperfused brain. Key insights into new approaches have been provided through translational research models and preclinical paradigms, and through detailed research on ischemic mechanisms. Additional recent clinical trials offer exciting salvos into this new strategy of pairing reperfusion with neuroprotective therapy. This pairing strategy can be employed using drugs that have shown neuroprotective efficacy; neurointerventionalists can administer these during or immediately after reperfusion therapy. This represents a crucial moment when we emphasize reperfusion, and have the technological capability along with the clinical trial experience to lead the way in multiprong approaches to stroke treatment.
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Affiliation(s)
- Justin F Fraser
- Department of Neurological Surgery, University of Kentucky, Lexington, Kentucky, USA
- Department of Neurology, University of Kentucky, Lexington, Kentucky, USA
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Shivani Pahwa
- Department of Neurological Surgery, University of Kentucky, Lexington, Kentucky, USA
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Michael Maniskas
- Department of Neurology, The University of Texas Health Science Center at Houston John P and Katherine G McGovern Medical School, Houston, Texas, USA
| | - Christopher Michas
- Department of Neurological Surgery, University of Kentucky, Lexington, Kentucky, USA
| | - Mesha Martinez
- Department of Neurointerventional Radiology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Keith R Pennypacker
- Department of Neurology, University of Kentucky, Lexington, Kentucky, USA
- University of Kentucky, Lexington, Kentucky, USA
| | - David Dornbos
- Department of Neurological Surgery, University of Kentucky, Lexington, Kentucky, USA
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2
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Wen G, Shim V, Holdsworth SJ, Fernandez J, Qiao M, Kasabov N, Wang A. Machine Learning for Brain MRI Data Harmonisation: A Systematic Review. Bioengineering (Basel) 2023; 10:bioengineering10040397. [PMID: 37106584 PMCID: PMC10135601 DOI: 10.3390/bioengineering10040397] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/16/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. OBJECTIVE This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. METHOD This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. RESULTS a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). CONCLUSION Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.
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Affiliation(s)
- Grace Wen
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
| | - Samantha Jane Holdsworth
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
- Mātai Medical Research Institute, Tairāwhiti-Gisborne 4010, New Zealand
- Department of Anatomy & Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1142, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
| | - Miao Qiao
- Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
| | - Nikola Kasabov
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand
- Intelligent Systems Research Centre, Ulster University, Londonderry BT52 1SA, UK
- Institute for Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
| | - Alan Wang
- Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand
- Department of Anatomy & Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1142, New Zealand
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3
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Cho HM, Hong C, Lee C, Ding H, Kim T, Ahn B. LEGO-compatible modular mapping phantom for magnetic resonance imaging. Sci Rep 2020; 10:14755. [PMID: 32901056 PMCID: PMC7478958 DOI: 10.1038/s41598-020-71279-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 08/10/2020] [Indexed: 11/30/2022] Open
Abstract
Physical phantoms have been widely used for performance evaluation of magnetic resonance imaging (MRI). Although there are many kinds of physical phantoms, most MRI phantoms use fixed configurations with specific sizes that may fit one or a few different types of radio frequency (RF) coils. Therefore, it has limitations for various image quality assessments of scanning areas. In this article, we report a novel design for a truly customizable MRI phantom called the LEGO-compatible Modular Mapping (MOMA) phantom, which not only serves as a general quality assurance phantom for a wide range of RF coils, but also a flexible calibration phantom for quantitative imaging. The MOMA phantom has a modular architecture which includes individual assessment functionality of the modules and LEGO-type assembly compatibility. We demonstrated the feasibility of the MOMA phantom for quantitative evaluation of image quality using customized module assembly compatible with head, breast, spine, knee, and body coil features. This unique approach allows comprehensive image quality evaluation with wide versatility. In addition, we provide detailed MOMA phantom development and imaging characteristics of the modules.
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Affiliation(s)
- Hyo-Min Cho
- Safety Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon, 34113, Republic of Korea
| | - Cheolpyo Hong
- Department of Radiological Science, Daegu Catholic University, Gyeongsan-si, 38430, Gyeongbuk, Republic of Korea
| | - Changwoo Lee
- Safety Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon, 34113, Republic of Korea
| | - Huanjun Ding
- Department of Radiological Sciences, University of California, Irvine, CA, 92697, USA
| | - Taeho Kim
- Department of Radiation Oncology, Washington University, Saint Louis, MO, 63110, USA
| | - Bongyoung Ahn
- Safety Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon, 34113, Republic of Korea.
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4
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Koch S, Mueller S, Foddis M, Bienert T, von Elverfeldt D, Knab F, Farr TD, Bernard R, Dopatka M, Rex A, Dirnagl U, Harms C, Boehm-Sturm P. Atlas registration for edema-corrected MRI lesion volume in mouse stroke models. J Cereb Blood Flow Metab 2019; 39:313-323. [PMID: 28829217 PMCID: PMC6360485 DOI: 10.1177/0271678x17726635] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Lesion volume measurements with magnetic resonance imaging are widely used to assess outcome in rodent models of stroke. In this study, we improved a mathematical framework to correct lesion size for edema which is based on manual delineation of the lesion and hemispheres. Furthermore, a novel MATLAB toolbox to register mouse brain MR images to the Allen brain atlas is presented. Its capability to calculate edema-corrected lesion size was compared to the manual approach. Automated image registration performed equally well in in a mouse middle cerebral artery occlusion model (Pearson r = 0.976, p = 2.265e-11). Information encapsulated in the registration was used to generate maps of edema induced tissue volume changes. These showed discrepancies to simplified tissue models underlying the manual approach. The presented techniques provide biologically more meaningful, voxel-wise biomarkers of vasogenic edema after stroke.
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Affiliation(s)
- Stefan Koch
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
| | - Susanne Mueller
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
- Charité Core Facility 7T Experimental
MRIs,
Charité
University Medicine Berlin, Berlin,
Germany
| | - Marco Foddis
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
| | - Thomas Bienert
- Department of Radiology – Medical
Physics, and BrainLinks-BrainTools Excellence Cluster, Medical Center – University
of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Dominik von Elverfeldt
- Department of Radiology – Medical
Physics, and BrainLinks-BrainTools Excellence Cluster, Medical Center – University
of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Felix Knab
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
| | - Tracy D Farr
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
- School of Life Sciences, University of
Nottingham, Nottingham, UK
| | - René Bernard
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
| | - Monika Dopatka
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
| | - André Rex
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
| | - Ulrich Dirnagl
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
- German Center for Neurodegenerative
Diseases (DZNE), Berlin, Germany
- Berlin Institute of Health, Berlin,
Germany
| | - Christoph Harms
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
| | - Philipp Boehm-Sturm
- Department of Experimental Neurology,
Center for Stroke Research Berlin (CSB), and NeuroCure,
Charité
University Medicine Berlin, Berlin,
Germany
- Charité Core Facility 7T Experimental
MRIs,
Charité
University Medicine Berlin, Berlin,
Germany
- Philipp Boehm-Sturm, Department of
Experimental Neurology, Center for Stroke Research, Charitéplatz 1, Berlin
10117, Germany.
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5
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Zinnhardt B, Wiesmann M, Honold L, Barca C, Schäfers M, Kiliaan AJ, Jacobs AH. In vivo imaging biomarkers of neuroinflammation in the development and assessment of stroke therapies - towards clinical translation. Theranostics 2018; 8:2603-2620. [PMID: 29774062 PMCID: PMC5956996 DOI: 10.7150/thno.24128] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 01/31/2018] [Indexed: 01/01/2023] Open
Abstract
Modulation of the inflammatory microenvironment after stroke opens a new avenue for the development of novel neurorestorative therapies in stroke. Understanding the spatio-temporal profile of (neuro-)inflammatory imaging biomarkers in detail thereby represents a crucial factor in the development and application of immunomodulatory therapies. The early integration of quantitative molecular imaging biomarkers in stroke drug development may provide key information about (i) early diagnosis and follow-up, (ii) spatio-temporal drug-target engagement (pharmacodynamic biomarker), (iii) differentiation of responders and non-responders in the patient cohort (inclusion/exclusion criteria; predictive biomarkers), and (iv) the mechanism of action. The use of targeted imaging biomarkers for may thus allow clinicians to decipher the profile of patient-specific inflammatory activity and the development of patient-tailored strategies for immunomodulatory and neuro-restorative therapies in stroke. Here, we highlight the recent developments in preclinical and clinical molecular imaging biomarkers of neuroinflammation (endothelial markers, microglia, MMPs, cell labeling, future developments) in stroke and outline how imaging biomarkers can be used in overcoming current translational roadblocks and attrition in order to advance new immunomodulatory compounds within the clinical pipeline.
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Affiliation(s)
- Bastian Zinnhardt
- European Institute for Molecular Imaging (EIMI), Westfälische Wilhelms University Münster, Münster, Germany
- EU 7 th FP Programme “Imaging Inflammation in Neurodegenerative Diseases (INMiND)”
- Cells in Motion (CiM) Cluster of Excellence, University of Münster, Münster, Germany
- PET Imaging in Drug Design and Development (PET3D)
- Department of Nuclear Medicine, Universitätsklinikum Münster, Münster, Germany
| | - Maximilian Wiesmann
- Department of Anatomy, Radboud university medical center, Donders Institute for Brain, Cognition & Behaviour, Nijmegen, The Netherlands
| | - Lisa Honold
- European Institute for Molecular Imaging (EIMI), Westfälische Wilhelms University Münster, Münster, Germany
| | - Cristina Barca
- European Institute for Molecular Imaging (EIMI), Westfälische Wilhelms University Münster, Münster, Germany
- PET Imaging in Drug Design and Development (PET3D)
| | - Michael Schäfers
- European Institute for Molecular Imaging (EIMI), Westfälische Wilhelms University Münster, Münster, Germany
- Cells in Motion (CiM) Cluster of Excellence, University of Münster, Münster, Germany
- Department of Nuclear Medicine, Universitätsklinikum Münster, Münster, Germany
| | - Amanda J Kiliaan
- Department of Anatomy, Radboud university medical center, Donders Institute for Brain, Cognition & Behaviour, Nijmegen, The Netherlands
| | - Andreas H Jacobs
- European Institute for Molecular Imaging (EIMI), Westfälische Wilhelms University Münster, Münster, Germany
- EU 7 th FP Programme “Imaging Inflammation in Neurodegenerative Diseases (INMiND)”
- Cells in Motion (CiM) Cluster of Excellence, University of Münster, Münster, Germany
- PET Imaging in Drug Design and Development (PET3D)
- Department of Geriatrics, Johanniter Hospital, Evangelische Kliniken, Bonn, Germany
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