An enhanced multi-fiber reconstruction technique using adaptive gradient directions coupled with MoNCW model in diffusion MRI
(2021) In Journal of Magnetic Resonance 325.- Abstract
In this paper, we introduced a novel approach for generating unit gradient vectors named as adaptive gradient directions (AGD) for reconstructing single and decussating (crossing or kissing) white matter fibers in brain. The present study is focusing on reconstruction process of brain's white matter fibers but not dealing with data acquisition where scanning is performed. The gradient vectors used in the state-of-art methodologies for reconstruction are uniformly distributed vectors on a unit sphere but AGD, in contrary, are non-uniformly distributed points on a unit sphere. These points are uniformly distributed in some pattern on the surface of a unit sphere. For reconstruction, we have coupled the proposed AGD approach with mixture... (More)
In this paper, we introduced a novel approach for generating unit gradient vectors named as adaptive gradient directions (AGD) for reconstructing single and decussating (crossing or kissing) white matter fibers in brain. The present study is focusing on reconstruction process of brain's white matter fibers but not dealing with data acquisition where scanning is performed. The gradient vectors used in the state-of-art methodologies for reconstruction are uniformly distributed vectors on a unit sphere but AGD, in contrary, are non-uniformly distributed points on a unit sphere. These points are uniformly distributed in some pattern on the surface of a unit sphere. For reconstruction, we have coupled the proposed AGD approach with mixture of non-central Wishart (MoNCW) model. We uphold the proposed approach with different simulations including synthetic as well as real data experiments. Resistivity to different Rician noise levels (σ=0.02-0.1) is demonstrated in simulated data for single as well as two and three decussating fibers. Our approach of using AGD dissipates the limitations that are encountered by the state-of-art technique of uniformly distributed points over the surface of unit sphere and outperforms showing significant reduction in angular errors.
(Less)
- author
- Puri, Ashishi ; Shakya, Snehlata LU and Kumar, Sanjeev
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Decussating fibers, DT-MRI, Mixture Models, Orientational Heterogeneity, Rician noise
- in
- Journal of Magnetic Resonance
- volume
- 325
- article number
- 106931
- publisher
- Academic Press
- external identifiers
-
- scopus:85102040696
- pmid:33684888
- ISSN
- 1090-7807
- DOI
- 10.1016/j.jmr.2021.106931
- language
- English
- LU publication?
- yes
- id
- 805cbd88-4f2e-465b-aaed-44c19de8d30a
- date added to LUP
- 2021-03-16 14:47:28
- date last changed
- 2024-06-13 09:00:20
@article{805cbd88-4f2e-465b-aaed-44c19de8d30a,
abstract = {{<p>In this paper, we introduced a novel approach for generating unit gradient vectors named as adaptive gradient directions (AGD) for reconstructing single and decussating (crossing or kissing) white matter fibers in brain. The present study is focusing on reconstruction process of brain's white matter fibers but not dealing with data acquisition where scanning is performed. The gradient vectors used in the state-of-art methodologies for reconstruction are uniformly distributed vectors on a unit sphere but AGD, in contrary, are non-uniformly distributed points on a unit sphere. These points are uniformly distributed in some pattern on the surface of a unit sphere. For reconstruction, we have coupled the proposed AGD approach with mixture of non-central Wishart (MoNCW) model. We uphold the proposed approach with different simulations including synthetic as well as real data experiments. Resistivity to different Rician noise levels (σ=0.02-0.1) is demonstrated in simulated data for single as well as two and three decussating fibers. Our approach of using AGD dissipates the limitations that are encountered by the state-of-art technique of uniformly distributed points over the surface of unit sphere and outperforms showing significant reduction in angular errors.</p>}},
author = {{Puri, Ashishi and Shakya, Snehlata and Kumar, Sanjeev}},
issn = {{1090-7807}},
keywords = {{Decussating fibers; DT-MRI; Mixture Models; Orientational Heterogeneity; Rician noise}},
language = {{eng}},
publisher = {{Academic Press}},
series = {{Journal of Magnetic Resonance}},
title = {{An enhanced multi-fiber reconstruction technique using adaptive gradient directions coupled with MoNCW model in diffusion MRI}},
url = {{http://dx.doi.org/10.1016/j.jmr.2021.106931}},
doi = {{10.1016/j.jmr.2021.106931}},
volume = {{325}},
year = {{2021}},
}