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Zhao S, Zhang T, Zhang W, Pan T, Zhang G, Feng S, Zhang X, Nie B, Liu H, Shan B. Harmonizing T1-Weighted Images to Improve Consistency of Brain Morphology Among Different Scanner Manufacturers in Alzheimer's disease. J Magn Reson Imaging 2024; 59:1327-1340. [PMID: 37403942 DOI: 10.1002/jmri.28887] [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: 01/04/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND Brain MRI scanner variability can introduce bias in measurements. Harmonizing scanner variability is crucial. PURPOSE To develop a harmonization method aimed at removing scanner variability, and to evaluate the consistency of results in multicenter studies. STUDY TYPE Retrospective. POPULATION Multicenter data from 170 healthy participants (males/females = 98/72; age = 73.8 ± 7.3) and 170 Alzheimer's disease patients (males/females = 98/72; age = 76.2 ± 8.5) were compared with reference data from another 340 participants. FIELD STRENGTH/SEQUENCE 3-T, magnetization prepared rapid gradient echo and turbo field echo; 1.5-T, inversion recovery prepared fast spoiled gradient echo T1-weighted sequences. ASSESSMENT Gray matter (GM) brain images, obtained through segmentation of T1-weighted images, were utilized to evaluate the performance of the harmonization method using common orthogonal basis extraction (HCOBE) and four other methods (removal of artificial voxel effect by linear regression, RAVEL; Z_score; general linear model, GLM; ComBat). Linear discriminant analysis (LDA) was used to access the effectiveness of different methods in reducing scanner variability. The performance of harmonization methods in preserving GM volumes heterogeneity was evaluated by the similarity of the relationship between GM proportion and age in the reference and multicenter data. Furthermore, the consistency of the harmonized multicenter data with the reference data were evaluated based on classification results (train/test = 7/3) and brain atrophy. STATISTICAL TESTS Two-sample t-tests, area under the curve (AUC), and Dice coefficients were used to analyze the consistency of results from the reference and harmonized multicenter data. A P-value <0.01 was considered statistically significant. RESULTS HCOBE reduced the scanner variability from 0.09 before harmonization to 0.003 (ideal: 0, RAVEL/Z_score/GLM/ComBat = 0.087/0.003/0.006/0.013). GM volumes showed no significant difference (P = 0.52) between the reference and HCOBE-harmonized multicenter data. Consistency evaluation showed that AUC values of 0.95 for both reference and HCOBE-harmonized multicenter data (RAVEL/Z_score/GLM/ComBat = 0.86/0.86/0.84/0.89), and the Dice coefficient increased from 0.73 before harmonization to 0.82 (ideal: 1, RAVEL/Z_score/GLM/ComBat = 0.39/0.64/0.59/0.74). DATA CONCLUSION HCOBE may help to remove scanner variability and could improve the consistency of results in multicenter studies. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Shilun Zhao
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Tianhao Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Wei Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Tingting Pan
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| | - Ge Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Chemical Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Shuang Feng
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| | - Xiwan Zhang
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Hua Liu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Baoci Shan
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
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2
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Yao T, Rheault F, Cai LY, Nath V, Asad Z, Newlin N, Cui C, Deng R, Ramadass K, Shafer A, Resnick S, Schilling K, Landman BA, Huo Y. Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging. J Med Imaging (Bellingham) 2024; 11:014005. [PMID: 38188934 PMCID: PMC10768686 DOI: 10.1117/1.jmi.11.1.014005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/04/2023] [Accepted: 12/14/2023] [Indexed: 01/09/2024] Open
Abstract
Purpose Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies. Approach In this paper, we propose a data-driven deep CSD method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation. Results From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers. Conclusion We propose a deep CSD method to explicitly reduce the scan-rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging.
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Affiliation(s)
- Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Francois Rheault
- Université de Sherbrooke, Department of Computer Science, Sherbrooke, Québec, Canada
| | - Leon Y. Cai
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Vishwesh Nath
- NVIDIA Corporation, Bethesda, Maryland, United States
| | - Zuhayr Asad
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Nancy Newlin
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Can Cui
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Ruining Deng
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Karthik Ramadass
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Andrea Shafer
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Susan Resnick
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Kurt Schilling
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
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3
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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4
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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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5
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Chen Z, Pawar K, Ekanayake M, Pain C, Zhong S, Egan GF. Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges. J Digit Imaging 2023; 36:204-230. [PMID: 36323914 PMCID: PMC9984670 DOI: 10.1007/s10278-022-00721-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 09/09/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.
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Affiliation(s)
- Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia.
- Department of Data Science and AI, Monash University, Melbourne, VIC, Australia.
| | - Kamlesh Pawar
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
| | - Mevan Ekanayake
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
| | - Cameron Pain
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
| | - Shenjun Zhong
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- National Imaging Facility, Brisbane, QLD, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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6
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Huang P, Zhang M. Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull 2023; 39:99-112. [PMID: 35771383 PMCID: PMC9849544 DOI: 10.1007/s12264-022-00905-x] [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: 02/16/2022] [Accepted: 05/07/2022] [Indexed: 01/22/2023] Open
Abstract
Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
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7
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Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, Yang G. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2022; 82:99-122. [PMID: 35664012 PMCID: PMC8878813 DOI: 10.1016/j.inffus.2022.01.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 05/13/2023]
Abstract
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
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Affiliation(s)
- Yang Nan
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Javier Del Ser
- Department of Communications Engineering, University of the Basque Country UPV/EHU, Bilbao 48013, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), Derio 48160, Spain
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Carola Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
- Oncology R&D, AstraZeneca, Cambridge, Northern Ireland UK
| | - Ian Selby
- Department of Radiology, University of Cambridge, Cambridge, Northern Ireland UK
| | - Kit Howard
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - John Owen
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Jon Neville
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Julien Guiot
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | - Benoit Ernst
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | | | | | - Marion I. Menzel
- Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE Healthcare GmbH, Munich, Germany
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Nina Flerin
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Avishek Chatterjee
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Henry Woodruff
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Leonor Cerdá-Alberich
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Luis Martí-Bonmatí
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Francisco Herrera
- Department of Computer Sciences and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada, Granada, Spain
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, Northern Ireland UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, Northern Ireland UK
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8
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Tian D, Zeng Z, Sun X, Tong Q, Li H, He H, Gao JH, He Y, Xia M. A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset. Neuroimage 2022; 257:119297. [PMID: 35568346 DOI: 10.1016/j.neuroimage.2022.119297] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 03/31/2022] [Accepted: 05/09/2022] [Indexed: 12/12/2022] Open
Abstract
The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.
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Affiliation(s)
- Dezheng Tian
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xiaoyi Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Qiqi Tong
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou 311121, China
| | - Huanjie Li
- School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
| | - Jia-Hong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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Qiu J, Deng K, Wang P, Chen C, Luo Y, Yuan S, Wen J. Application of diffusion kurtosis imaging to the study of edema in solid and peritumoral areas of glioma. Magn Reson Imaging 2021; 86:10-16. [PMID: 34793876 DOI: 10.1016/j.mri.2021.11.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE When gliomas grow in an infiltrative form, high-grade malignant glioma tissue extends beyond the contrast-enhancing tumor boundary, and this diffuse non-enhancing tumor infiltration is not visible on conventional MRI. The purpose of this study was to evaluate the of diffusion kurtosis imaging (DKI)-derived parameters in a group of patients with pre-operative gliomas, evaluating changes in the solid tumor and peritumoral edema area, and investigating their use for evaluating the recurrence and prognosis of gliomas. METHODS In this retrospective study, 51 patients with gliomas who underwent biopsy or surgery underwent DKI scans before surgery. DKI scans were performed to generate DKI parameter maps of the solid tumor and peritumoral edema areas. In the solid tumor area, the kurtosis parameters showed the highest area under the curve (AUC), sensitivity, and specificity for distinguishing high- and low-grade gliomas (all P < 0.01). RESULTS In the peritumoral edema area, significant differences were found between groups with grade III and IV gliomas (P < 0.05). DKI parameters were found to correlate with clinical Ki-67 scores within the solid tumor area (MK: R2 = 0.288, P < 0.001; Kr: R2 = 0.270, P < 0.001; Ka: R2 = 0.274, P < 0.001; MD: R2 = 0.223, P < 0.001; FA: R2 = 0.098, P < 0.01). No significant correlations were found between Ki-67 and kurtosis parameters of peritumoral edema. CONCLUSIONS In this study, DKI showed potential utility for studying solid tumor and peritumoral edema of high grade gliomas.
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Affiliation(s)
- Jun Qiu
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Peng Wang
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Chuanyu Chen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yi Luo
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Shuya Yuan
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Jie Wen
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
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Chen L, Xia C, Sun H. Recent advances of deep learning in psychiatric disorders. PRECISION CLINICAL MEDICINE 2020; 3:202-213. [PMID: 35694413 PMCID: PMC8982596 DOI: 10.1093/pcmedi/pbaa029] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 08/24/2020] [Accepted: 08/25/2020] [Indexed: 02/05/2023] Open
Abstract
Deep learning (DL) is a recently proposed subset of machine learning methods that has gained extensive attention in the academic world, breaking benchmark records in areas such as visual recognition and natural language processing. Different from conventional machine learning algorithm, DL is able to learn useful representations and features directly from raw data through hierarchical nonlinear transformations. Because of its ability to detect abstract and complex patterns, DL has been used in neuroimaging studies of psychiatric disorders, which are characterized by subtle and diffuse alterations. Here, we provide a brief review of recent advances and associated challenges in neuroimaging studies of DL applied to psychiatric disorders. The results of these studies indicate that DL could be a powerful tool in assisting the diagnosis of psychiatric diseases. We conclude our review by clarifying the main promises and challenges of DL application in psychiatric disorders, and possible directions for future research.
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Affiliation(s)
- Lu Chen
- West China Medical Publishers, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Huaiqiang Sun
- Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
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