期刊
MAGNETIC RESONANCE IMAGING
卷 62, 期 -, 页码 59-69出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2019.05.001
关键词
Brain; Fluid-attenuated inversion recovery; Intensity standardization; White matter lesions; Alzheimer's disease; Vascular disease; Segmentation
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Canadian Institutes of Health Research Team Grant for Clinical Research Initiatives [CIHR- CRI 88057]
- Canada Foundation for Innovation [CFI CAIN 20099]
- government of Alberta
- government of Ontario
- government of Quebec
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defence) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- Eurolmmun
- F. Hoffmann-La Roche Ltd.
- Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Lumosity
- Lundbeck
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- NeuroRx Research
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- Canadian Institutes of Health Research
- Neurotrack Technologies
Fluid-Attenuated Inversion Recovery (FLAIR) MRI are used by physicians to analyze white matter lesions (WML) of the brain, which are related to neurodegenerative diseases such as dementia and vascular disease. To study the causes and progression of these diseases, multi-centre (MC) studies are conducted, with images acquired and analyzed from multiple institutions. Due to differences in acquisition software and hardware, there is variability in image properties, which creates challenges for automated algorithms. This work explores this variability, known as the MC effect, by analyzing nearly 5000 MC FLAIR volumes and proposes an intensity standardization framework to normalize intensity non-standardness in FLAIR MRI, while ensuring the appearance of WML. Results show that original image characteristics varied significantly between scanner vendors and centres, and that this variability was reduced with standardization. To further highlight the utility of intensity standardization, a threshold-based brain extraction algorithm is implemented and compared with a classifier-based approach. A competitive Dice Similarity Coefficient of 81% was achieved on 183 volumes, demonstrating that optimized pre-processing can effectively reduce the variability in MC studies, allowing for simplified algorithms to be applied on large datasets robustly.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据