Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique

The lateral geniculate nucleus (LGN) is a small, inhomogeneous structure that relays major sensory inputs from the retina to the visual cortex. LGN morphology has been intensively studied due to various retinal diseases, as well as in the context of normal brain development. However, many of the met...

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Autores principales:	Mikhail Lipin, Jean Bennett, Gui-Shuang Ying, Yinxi Yu, Manzar Ashtari
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	segmentation brain morphometry MRI noise immunity partial volume effect LGN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/d4d4b2e19bc342b4ba46d40ddbd0349a
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spelling	oai:doaj.org-article:d4d4b2e19bc342b4ba46d40ddbd0349a2021-12-03T04:59:43ZImproving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique1662-518810.3389/fncom.2021.708866https://doaj.org/article/d4d4b2e19bc342b4ba46d40ddbd0349a2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fncom.2021.708866/fullhttps://doaj.org/toc/1662-5188The lateral geniculate nucleus (LGN) is a small, inhomogeneous structure that relays major sensory inputs from the retina to the visual cortex. LGN morphology has been intensively studied due to various retinal diseases, as well as in the context of normal brain development. However, many of the methods used for LGN structural evaluations have not adequately addressed the challenges presented by the suboptimal routine MRI imaging of this structure. Here, we propose a novel method of edge enhancement that allows for high reliability and accuracy with regard to LGN morphometry, using routine 3D-MRI imaging protocols. This new algorithm is based on modeling a small brain structure as a polyhedron with its faces, edges, and vertices fitted with one plane, the intersection of two planes, and the intersection of three planes, respectively. This algorithm dramatically increases the contrast-to-noise ratio between the LGN and its surrounding structures as well as doubling the original spatial resolution. To show the algorithm efficacy, two raters (MA and ML) measured LGN volumes bilaterally in 19 subjects using the edge-enhanced LGN extracted areas from the 3D-T1 weighted images. The averages of the left and right LGN volumes from the two raters were 175 ± 8 and 174 ± 9 mm3, respectively. The intra-class correlations between raters were 0.74 for the left and 0.81 for the right LGN volumes. The high contrast edge-enhanced LGN images presented here, from a 7-min routine 3T-MRI acquisition, is qualitatively comparable to previously reported LGN images that were acquired using a proton density sequence with 30–40 averages and 1.5-h of acquisition time. The proposed edge-enhancement algorithm is not limited only to the LGN, but can significantly improve the contrast-to-noise ratio of any small deep-seated gray matter brain structure that is prone to high-levels of noise and partial volume effects, and can also increase their morphometric accuracy and reliability. An immensely useful feature of the proposed algorithm is that it can be used retrospectively on noisy and low contrast 3D brain images previously acquired as part of any routine clinical MRI visit.Mikhail LipinJean BennettGui-Shuang YingYinxi YuManzar AshtariFrontiers Media S.A.articlesegmentationbrain morphometryMRInoise immunitypartial volume effectLGNNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Computational Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	segmentation brain morphometry MRI noise immunity partial volume effect LGN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	segmentation brain morphometry MRI noise immunity partial volume effect LGN Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Mikhail Lipin Jean Bennett Gui-Shuang Ying Yinxi Yu Manzar Ashtari Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique
description	The lateral geniculate nucleus (LGN) is a small, inhomogeneous structure that relays major sensory inputs from the retina to the visual cortex. LGN morphology has been intensively studied due to various retinal diseases, as well as in the context of normal brain development. However, many of the methods used for LGN structural evaluations have not adequately addressed the challenges presented by the suboptimal routine MRI imaging of this structure. Here, we propose a novel method of edge enhancement that allows for high reliability and accuracy with regard to LGN morphometry, using routine 3D-MRI imaging protocols. This new algorithm is based on modeling a small brain structure as a polyhedron with its faces, edges, and vertices fitted with one plane, the intersection of two planes, and the intersection of three planes, respectively. This algorithm dramatically increases the contrast-to-noise ratio between the LGN and its surrounding structures as well as doubling the original spatial resolution. To show the algorithm efficacy, two raters (MA and ML) measured LGN volumes bilaterally in 19 subjects using the edge-enhanced LGN extracted areas from the 3D-T1 weighted images. The averages of the left and right LGN volumes from the two raters were 175 ± 8 and 174 ± 9 mm3, respectively. The intra-class correlations between raters were 0.74 for the left and 0.81 for the right LGN volumes. The high contrast edge-enhanced LGN images presented here, from a 7-min routine 3T-MRI acquisition, is qualitatively comparable to previously reported LGN images that were acquired using a proton density sequence with 30–40 averages and 1.5-h of acquisition time. The proposed edge-enhancement algorithm is not limited only to the LGN, but can significantly improve the contrast-to-noise ratio of any small deep-seated gray matter brain structure that is prone to high-levels of noise and partial volume effects, and can also increase their morphometric accuracy and reliability. An immensely useful feature of the proposed algorithm is that it can be used retrospectively on noisy and low contrast 3D brain images previously acquired as part of any routine clinical MRI visit.
format	article
author	Mikhail Lipin Jean Bennett Gui-Shuang Ying Yinxi Yu Manzar Ashtari
author_facet	Mikhail Lipin Jean Bennett Gui-Shuang Ying Yinxi Yu Manzar Ashtari
author_sort	Mikhail Lipin
title	Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique
title_short	Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique
title_full	Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique
title_fullStr	Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique
title_full_unstemmed	Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique
title_sort	improving the quantification of the lateral geniculate nucleus in magnetic resonance imaging using a novel 3d-edge enhancement technique
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/d4d4b2e19bc342b4ba46d40ddbd0349a
work_keys_str_mv	AT mikhaillipin improvingthequantificationofthelateralgeniculatenucleusinmagneticresonanceimagingusinganovel3dedgeenhancementtechnique AT jeanbennett improvingthequantificationofthelateralgeniculatenucleusinmagneticresonanceimagingusinganovel3dedgeenhancementtechnique AT guishuangying improvingthequantificationofthelateralgeniculatenucleusinmagneticresonanceimagingusinganovel3dedgeenhancementtechnique AT yinxiyu improvingthequantificationofthelateralgeniculatenucleusinmagneticresonanceimagingusinganovel3dedgeenhancementtechnique AT manzarashtari improvingthequantificationofthelateralgeniculatenucleusinmagneticresonanceimagingusinganovel3dedgeenhancementtechnique
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Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique

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