Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging

Estimation of Multiple Sclerosis lesion age on magnetic resonance imaging

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 tem...

Full description

Bibliographic Details
Main Authors:	Elizabeth M. Sweeney, Thanh D. Nguyen, Amy Kuceyeski, Sarah M. Ryan, Shun Zhang, Lily Zexter, Yi Wang, Susan A. Gauthier
Format:	Article
Language:	English
Published:	Elsevier 2021-01-01
Series:	NeuroImage
Online Access:	http://www.sciencedirect.com/science/article/pii/S1053811920309368

Similar Items

Dimethyl fumarate reduces inflammation in chronic active multiple sclerosis lesions
by: Zinger, Nicole, et al.
Published: (2023)

Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis
by: Ceren Tozlu, et al.
Published: (2021-10-01)

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis
by: Ceren Tozlu, et al.
Published: (2021-12-01)

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity
by: Ceren Tozlu, et al.
Published: (2023-01-01)

ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation
by: Hang Zhang, et al.
Published: (2021-01-01)

Magnetic resonance monitoring of lesion evolution in multiple sclerosis
by: Àlex Rovira, et al.
Published: (2013-09-01)

Diagnostic accuracy of semiautomatic lesion detection plus quantitative susceptibility mapping in the identification of new and enhancing multiple sclerosis lesions
by: Shun Zhang, et al.
Published: (2018-01-01)

QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps
by: Hang Zhang, et al.
Published: (2022-01-01)

Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions
by: T. Campbell Arnold, et al.
Published: (2022-01-01)

Magnetic resonance imaging in the diagnosis of non-tumorous multifocal brain lesions mimicking multiple sclerosis
by: I. A. Krotenkova, et al.
Published: (2021-03-01)

Distribution of multiple sclerosis lesions detected by brain magnetic resonance imaging in Erbil city
by: Dina NasihTawfeeq, et al.
Published: (2021-04-01)

A new method to segment the multiple sclerosis lesions on brain magnetic resonance images
by: Alireza Karimian, et al.
Published: (2015-01-01)

QSM is an imaging biomarker for chronic glial activation in multiple sclerosis lesions
by: Kelly M. Gillen, et al.
Published: (2021-04-01)

Vasomotor reactivity comparison in multiple sclerosis patients with white matter lesions and nonmultiple sclerosis subjects with white matter lesions in brain magnetic resonance imaging
by: Fariborz Khorvash, et al.
Published: (2016-01-01)

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging
by: Chris W. J. van der Weijden, et al.
Published: (2022-03-01)

Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195.
by: Yeona Kang, et al.
Published: (2018-01-01)

Neural Network-Based Learning Kernel for Automatic Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Images
by: Khastavaneh H., et al.
Published: (2017-06-01)

A comparison of brain magnetic resonance imaging lesions in multiple sclerosis by race with reference to disability progression
by: Yuri Nakamura, et al.
Published: (2018-09-01)

Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study
by: Tom Finck, et al.
Published: (2022-04-01)

Retracted: Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging
by: Advances in Materials Science and Engineering
Published: (2023-01-01)

Automated segmentation of changes in FLAIR-hyperintense white matter lesions in multiple sclerosis on serial magnetic resonance imaging
by: Paul Schmidt, et al.
Published: (2019-01-01)

Unsupervised Multimodal Magnetic Resonance Images Segmentation and Multiple Sclerosis Lesions Extraction based on Edge and Texture Features
by: Tannaz AKBARPOUR, et al.
Published: (2017-06-01)

Understanding Magnetic Resonance Imaging in Multiple Sclerosis (UMIMS): Development and Piloting of an Online Education Program About Magnetic Resonance Imaging for People With Multiple Sclerosis
by: Magalie Freund, et al.
Published: (2022-03-01)

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features
by: Adrian Ion-Mărgineanu, et al.
Published: (2017-07-01)

Serial cranial and spinal cord magnetic resonance imaging in multiple sclerosis.
by: Wiebe, S, et al.
Published: (1992)

Dual Inversion Recovery Magnetic Resonance Imaging's Diagnostic Value for Estimating the Overall Load of Multiple Sclerosis Lesions and its Anatomical Distribution
by: Mahmoud Mishaal Mohamed, et al.
Published: (2024-04-01)

Diagnostic value of 3D-FLAIR magnetic resonance sequence in detection of white matter brain lesions in multiple sclerosis
by: Ahmed Ibrahim Tawfik, et al.
Published: (2020-07-01)

Diffusion-Weighted Magnetic Resonance Imaging of the Brain in Multiple Sclerosis
by: Murat Terzi, et al.
Published: (2007-10-01)

FLAIR2 improves LesionTOADS automatic segmentation of multiple sclerosis lesions in non-homogenized, multi-center, 2D clinical magnetic resonance images
by: M. Le, et al.
Published: (2019-01-01)

Significance and In Vivo Detection of Iron-Laden Microglia in White Matter Multiple Sclerosis Lesions
by: Kelly M. Gillen, et al.
Published: (2018-02-01)

Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment.
by: Ani Eloyan, et al.
Published: (2014-01-01)

A Comparison of Magnetic Resonance Imaging Methods to Assess Multiple Sclerosis Lesions: Implications for Patient Characterization and Clinical Trial Design
by: Ewart Mark Haacke, et al.
Published: (2021-12-01)

Magnetic Resonance Imaging in the Differential Diagnosis of Multiple Sclerosis and Other Demyelinating Diseases
by: I. A. Krotenkova, et al.
Published: (2019-09-01)

Longitudinal multiple sclerosis lesion segmentation data resource
by: Aaron Carass, et al.
Published: (2017-06-01)

Longitudinal Magnetic Resonance Imaging of Cerebral Microbleeds in Multiple Sclerosis Patients
by: Karthikeyan Subramanian, et al.
Published: (2020-11-01)

Magnetic Resonance Venography Findings In A Group Of Patients With Multiple Sclerosis
by: Ali Qasim Al-Baghdady
Published: (2019-11-01)

Signal intensity of basal ganglions in T2 magnetic resonance imaging in multiple sclerosis
by: Farshchian N (MD), et al.
Published: (2012-12-01)

Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions
by: Elizabeth M. Sweeney, et al.
Published: (2016-01-01)

Association of brain tissue cerebrospinal fluid fraction with age in healthy cognitively normal adults
by: Liangdong Zhou, et al.
Published: (2023-06-01)

Applying Deep Learning to Accelerated Clinical Brain Magnetic Resonance Imaging for Multiple Sclerosis
by: Ashika Mani, et al.
Published: (2021-09-01)