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Multiple Sclerosis Medical Image Analysis and Information Management
Author(s) -
Liu Lifeng,
Meier Dominik,
PolgarTurcsanyi Mariann,
Karkocha Pawel,
Bakshi Rohit,
Guttmann Charles R. G.
Publication year - 2005
Publication title -
journal of neuroimaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.822
H-Index - 64
eISSN - 1552-6569
pISSN - 1051-2284
DOI - 10.1177/1051228405282864
Subject(s) - workflow , computer science , image processing , visualization , data management , medicine , data mining , database , artificial intelligence , image (mathematics)
Magnetic resonance imaging (MRI) has become a central tool for patient management, as well as research, in multiple sclerosis (MS). Measurements of disease burden and activity derived from MRI through quantitative image analysis techniques are increasingly being used. There are many complexities and challenges in building computerized processing pipelines to ensure efficiency, reproducibility, and quality control for MRI scans from MS patients. Such paradigms require advanced image processing and analysis technologies, as well as integrated database management systems to ensure the most utility for clinical and research purposes. This article reviews pipelines available for quantitative clinical MRI research in MS, including image segmentation, registration, time‐series analysis, performance validation, visualization techniques, and advanced medical imaging software packages. To address the complex demands of the sequential processes, the authors developed a workflow management system that uses a centralized database and distributed computing system for image processing and analysis. The implementation of their system includes a web‐form‐based Oracle ® database application for information management and event dispatching, and multiple modules for image processing and analysis. The seamless integration of processing pipelines with the database makes it more efficient for users to navigate complex, multistep analysis protocols, reduces the user's learning curve, reduces the time needed for combining and activating different computing modules, and allows for close monitoring for quality‐control purposes. The authors' system can be extended to general applications in clinical trials and to routine processing for image‐based clinical research.