Joint RElaxation-Diffusion Imaging Moments to Probe Neurite Microstructure
Ning, Lipeng ; Gagoski, Borjan ; Szczepankiewicz, Filip LU
; Westin, Carl-Fredrik
and Rathi, Yogesh
(2020)
In IEEE Transactions on Medical Imaging
39(3).
p.668-677
- Abstract
Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use... (More)
Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.
(Less)
- author
- Ning, Lipeng
; Gagoski, Borjan
; Szczepankiewicz, Filip
LU
; Westin, Carl-Fredrik
and Rathi, Yogesh
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- keywords
- Algorithms, Brain/diagnostic imaging, Diffusion Magnetic Resonance Imaging/methods, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted/methods, Neurites/ultrastructure
- in
- IEEE Transactions on Medical Imaging
- volume
- 39
- issue
- 3
- pages
- 668 - 677
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85080909264
- pmid:31398113
- ISSN
- 1558-254X
- DOI
- 10.1109/TMI.2019.2933982
- language
- English
- LU publication?
- no
- id
- 12330064-2ae7-4506-bb8f-e14154590278
- date added to LUP
- 2022-04-04 12:35:19
- date last changed
- 2024-04-08 09:36:07
@article{12330064-2ae7-4506-bb8f-e14154590278,
abstract = {{<p>Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.</p>}},
author = {{Ning, Lipeng and Gagoski, Borjan and Szczepankiewicz, Filip and Westin, Carl-Fredrik and Rathi, Yogesh}},
issn = {{1558-254X}},
keywords = {{Algorithms; Brain/diagnostic imaging; Diffusion Magnetic Resonance Imaging/methods; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted/methods; Neurites/ultrastructure}},
language = {{eng}},
number = {{3}},
pages = {{668--677}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Medical Imaging}},
title = {{Joint RElaxation-Diffusion Imaging Moments to Probe Neurite Microstructure}},
url = {{http://dx.doi.org/10.1109/TMI.2019.2933982}},
doi = {{10.1109/TMI.2019.2933982}},
volume = {{39}},
year = {{2020}},
}