Paper Info
Title
Towards Optimal Linear Estimation Of Orientation Distribution Functions With Arbitrarily Sampled Diffusion Mri Data
Abstract
The estimation of orientation distribution functions (ODFs) from diffusion MRI data is an important step in diffusion tractography, but existing estimation methods often depend on signal modeling assumptions that are violated by real data, lack theoretical characterization, and/or are only applicable to a small range of q-space sampling patterns. As a result, existing ODF estimation methods may be suboptimal. In this work, we propose a novel ODF estimation approach that learns a linear ODF estimator from training data. The training set contains ideal data samples paired with corresponding ideal ODFs, and the learning procedure reduces to a simple linear least-squares problem. This approach can accommodate arbitrary q-space sampling schemes, can be characterized theoretically, and is theoretically demonstrated to generalize far beyond the training set. The proposed approach is evaluated with simulated and in vivo diffusion data, where it is demonstrated to outperform common alternatives.
Year
Venue
Field
2018
2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018)
Training set,Linear estimation,Diffusion MRI,Signal modeling,Pattern recognition,Computer science,Diffusion Tractography,Artificial intelligence,Sampling (statistics),Distribution function,Estimator
DocType
ISSN
Citations 
Conference
1945-7928
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Divya Varadarajan111.03
Justin P. Haldar235035.40