Linked Life Data

21129881

Source:http://linkedlifedata.com/resource/pubmed/id/21129881

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2011-2-7
pubmed:abstractText
Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane. We propose a novel approach to identify and reject outlier images automatically using local binary patterns (LBP) and 2D partial least square (2D-PLS) to estimate diffusion tensors robustly. This method uses an enhanced LBP algorithm to extract texture features from a local texture feature of the image matrix from the DWI data. Because the images have been transformed to local texture matrices, we are able to extract discriminating information that identifies outliers in the data set by extending a traditional one-dimensional PLS algorithm to a two-dimension operator. The class-membership matrix in this 2D-PLS algorithm is adapted to process samples that are image matrix, and the membership matrix thus represents varying degrees of importance of local information within the images. We also derive the analytic form of the generalized inverse of the class-membership matrix. We show that this method can effectively extract local features from brain images obtained from a large sample of human infants to identify images that are outliers in their textural features, permitting their exclusion from further processing when estimating tensors using the DWIs. This technique is shown to be superior in performance when compared with visual inspection and other common methods to address motion-related artifacts in DWI data. This technique is applicable to correct motion artifact in other magnetic resonance imaging (MRI) techniques (e.g., the bootstrapping estimation) that use univariate or multivariate regression methods to fit MRI data to a pre-specified model.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1873-5894
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
29
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
230-42
pubmed:meshHeading
pubmed-meshheading:21129881-Algorithms, pubmed-meshheading:21129881-Artifacts, pubmed-meshheading:21129881-Brain, pubmed-meshheading:21129881-Computer Simulation, pubmed-meshheading:21129881-Diffusion Magnetic Resonance Imaging, pubmed-meshheading:21129881-Female, pubmed-meshheading:21129881-Humans, pubmed-meshheading:21129881-Image Enhancement, pubmed-meshheading:21129881-Image Interpretation, Computer-Assisted, pubmed-meshheading:21129881-Infant, pubmed-meshheading:21129881-Least-Squares Analysis, pubmed-meshheading:21129881-Male, pubmed-meshheading:21129881-Models, Biological, pubmed-meshheading:21129881-Models, Statistical, pubmed-meshheading:21129881-Motion, pubmed-meshheading:21129881-Pattern Recognition, Automated, pubmed-meshheading:21129881-Pregnancy, pubmed-meshheading:21129881-Prenatal Diagnosis, pubmed-meshheading:21129881-Prenatal Exposure Delayed Effects, pubmed-meshheading:21129881-Reproducibility of Results, pubmed-meshheading:21129881-Sensitivity and Specificity
pubmed:year
2011
pubmed:articleTitle
Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares.
pubmed:affiliation
MRI Unit, Department of Psychiatry, Columbia University, New York, NY 10032, USA. zhouz@childpsych.columbia.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural