On the Averaging of Cardiac Diffusion Tensor MRI Data: The Effect of Distance Function Selection
File(s)
Accepted version
Author(s)
Giannakidis, A
Melkus, G
Yang, G
Gullberg, GT
Type
Journal Article
Abstract
Diffusion tensor magnetic resonance imaging (DT-MRI) allows a unique insight
into the microstructure of highly-directional tissues. The selection of the most
proper distance function for the space of diffusion tensors is crucial in enhancing
the clinical application of this imaging modality. Both linear and nonlinear
metrics have been proposed in the literature over the years. The debate on the
most appropriate DT-MRI distance function is still ongoing. In this paper, we presented
a framework to compare the Euclidean, affine-invariant Riemannian and
log-Euclidean metrics using actual high-resolution DT-MRI rat heart data. We employed
temporal averaging at the diffusion tensor level of three consecutive and
identically-acquired DT-MRI datasets from each of five rat hearts as a means to
rectify the background noise-induced loss of myocyte directional regularity. This
procedure is applied here for the first time in the context of tensor distance function
selection. When compared with previous studies that used a different concrete application
to juxtapose the various DT-MRI distance functions, this work is unique
in that it combined the following: (i) Metrics were judged by quantitative –rather
than qualitative– criteria, (ii) the comparison tools were non-biased, (iii) a longitudinal
comparison operation was used on a same-voxel basis. The statistical
analyses of the comparison showed that the three DT-MRI distance functions tend
to provide equivalent results. Hence, we came to the conclusion that the tensor
manifold for cardiac DT-MRI studies is a curved space of almost zero curvature. The signal to noise ratio dependence of the operations was investigated through
simulations. Finally, the “swelling effect” occurrence following Euclidean averaging
was found to be too unimportant to be worth consideration.
into the microstructure of highly-directional tissues. The selection of the most
proper distance function for the space of diffusion tensors is crucial in enhancing
the clinical application of this imaging modality. Both linear and nonlinear
metrics have been proposed in the literature over the years. The debate on the
most appropriate DT-MRI distance function is still ongoing. In this paper, we presented
a framework to compare the Euclidean, affine-invariant Riemannian and
log-Euclidean metrics using actual high-resolution DT-MRI rat heart data. We employed
temporal averaging at the diffusion tensor level of three consecutive and
identically-acquired DT-MRI datasets from each of five rat hearts as a means to
rectify the background noise-induced loss of myocyte directional regularity. This
procedure is applied here for the first time in the context of tensor distance function
selection. When compared with previous studies that used a different concrete application
to juxtapose the various DT-MRI distance functions, this work is unique
in that it combined the following: (i) Metrics were judged by quantitative –rather
than qualitative– criteria, (ii) the comparison tools were non-biased, (iii) a longitudinal
comparison operation was used on a same-voxel basis. The statistical
analyses of the comparison showed that the three DT-MRI distance functions tend
to provide equivalent results. Hence, we came to the conclusion that the tensor
manifold for cardiac DT-MRI studies is a curved space of almost zero curvature. The signal to noise ratio dependence of the operations was investigated through
simulations. Finally, the “swelling effect” occurrence following Euclidean averaging
was found to be too unimportant to be worth consideration.
Date Issued
2016-10-18
Date Acceptance
2016-09-19
Citation
Physics in Medicine and Biology, 2016, 61
ISSN
1361-6560
Publisher
IOP Publishing
Journal / Book Title
Physics in Medicine and Biology
Volume
61
Copyright Statement
©2016 IOP Publishing Ltd.
Subjects
Nuclear Medicine & Medical Imaging
0299 Other Physical Sciences
0903 Biomedical Engineering
Publication Status
Published
Article Number
7765