Constrained free form deformation based algorithm for geometric distortion correction of echo planar diffusion tensor images

Author(s):  
S. Ardekani ◽  
U. Sinha
2009 ◽  
Vol 61 (4) ◽  
pp. 994-1000 ◽  
Author(s):  
Iulius Dragonu ◽  
Baudouin Denis de Senneville ◽  
Bruno Quesson ◽  
Chrit Moonen ◽  
Mario Ries

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Ruth P. Lim ◽  
Jeremy C. Lim ◽  
Jose R. Teruel ◽  
Elissa Botterill ◽  
Jas-mine Seah ◽  
...  

2020 ◽  
Author(s):  
Vinai Roopchansingh ◽  
Jerry J. French ◽  
Dylan M. Nielson ◽  
Richard C. Reynolds ◽  
Daniel R. Glen ◽  
...  

AbstractTask, resting state, and diffusion MRI data are usually acquired from subjects using echo-planar based imaging techniques. These techniques are highly susceptible to B0 homogeneity effects that result in geometric distortions in the reconstructed images. As researchers work to link the information from these scans back to various developmental stages, or to conditions and diseases in specific regions or structures of the brain, it becomes critical to have accurate correspondence between more geometrically distorted echo-planar images and less geometrically distorted anatomical images. A variety of techniques and tools have been developed to improve this correspondence. The basic premise behind most techniques used to mitigate geometric distortion is to acquire enough information to inform software tools how echo-planar images are warped, then have them undo that warping. Here, we investigate the application of two common methods: B0 correction, and reverse-polarity phase-encoding (or reverse blip) correction. We implement each of these in two separate, widely used software packages in the field: AFNI and FSL. We find that using either technique in either software package results in reduced geometric distortions in the EPI images. We discuss the practical implementations of these methods (e.g., increased scan and processing time). In general, however, both methods possess readily available data acquisition schemes, and are highly efficient to include in processing streams. Due to the overall data improvement, we strongly recommend that researchers include one of these methods in their standard protocols.


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bunheang Tay ◽  
Jung Keun Hyun ◽  
Sejong Oh

Diffusion Tensor Imaging (DTI) uses in vivo images that describe extracellular structures by measuring the diffusion of water molecules. These images capture axonal movement and orientation using echo-planar imaging and provide critical information for evaluating lesions and structural damage in the central nervous system. This information can be used for prediction of Spinal Cord Injuries (SCIs) and for assessment of patients who are recovering from such injuries. In this paper, we propose a classification scheme for identifying healthy individuals and patients. In the proposed scheme, a dataset is first constructed from DTI images, after which the constructed dataset undergoes feature selection and classification. The experiment results show that the proposed scheme aids in the diagnosis of SCIs.


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