Eo-Jin Hwang1, Hyug-Gi Kim2, Danbi Kim1, Hak Young Rhee3, Chang-Woo Ryu1, Tian Liu4, Yi Wang4, Geon-Ho Jahng1. 1. Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, #892 Dongnam-ro, Gangdong-Gu, Seoul 05278, South Korea. 2. Department of Biomedical Engineering, Graduate School, Kyung Hee University, #1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, South Korea. 3. Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, #892 Dongnam-ro, Gangdong-Gu, Seoul 05278, South Korea. 4. Department of Biomedical Engineering and Radiology, Cornell University, #515 E 71st Street, Suite 102, New York, New York 10021.
Abstract
PURPOSE: Although a number of studies have focused on finding anatomical regions in which iron concentrations are high, no study has been conducted to examine the overall variations in susceptibility maps of Alzheimer's disease (AD). The objective of this study, therefore, was to differentiate AD from cognitive normal (CN) and mild cognitive impairment (MCI) using a texture analysis of quantitative susceptibility maps (QSMs). METHODS: The study was approved by the local institutional review board, and informed consent was obtained from all subjects. In each participant group-CN, MCI, and AD-18 elderly subjects were enrolled. A fully first-order flow-compensated 3D gradient-echo sequence was run to obtain axial magnitudes and phase images and to produce QSM data. Sagittal structural 3D T1-weighted (3DT1W) images were also obtained with the magnetization-prepared rapid acquisition of gradient-echo sequence to obtain brain tissue images. The first- and second-order texture parameters of the QSMs and 3DT1W images were obtained to evaluate group differences using a one-way analysis of covariance. RESULTS: For the first-order QSM analysis, mean, standard deviation, and covariance of signal intensity separated the subject groups (F = 5.191, p = 0.009). For the second-order analysis, angular second moment, contrast, and correlation separated the subject groups (F = 6.896, p = 0.002). Finally, a receiver operating characteristic curve analysis differentiated MCI from CN in white matter on the QSMs (z = 3.092, p = 0.0020). CONCLUSIONS: This was the first study to evaluate the textures of QSM in AD, which overcame the limitations of voxel-based analyses. The QSM texture analysis successfully distinguished both AD and MCI from CN and outperformed the voxel-based analysis using 3DT1-weighed images in separating MCI from CN. The first-order textures were more efficient in differentiating MCI from CN than did the second-order.
PURPOSE: Although a number of studies have focused on finding anatomical regions in which iron concentrations are high, no study has been conducted to examine the overall variations in susceptibility maps of Alzheimer's disease (AD). The objective of this study, therefore, was to differentiate AD from cognitive normal (CN) and mild cognitive impairment (MCI) using a texture analysis of quantitative susceptibility maps (QSMs). METHODS: The study was approved by the local institutional review board, and informed consent was obtained from all subjects. In each participant group-CN, MCI, and AD-18 elderly subjects were enrolled. A fully first-order flow-compensated 3D gradient-echo sequence was run to obtain axial magnitudes and phase images and to produce QSM data. Sagittal structural 3D T1-weighted (3DT1W) images were also obtained with the magnetization-prepared rapid acquisition of gradient-echo sequence to obtain brain tissue images. The first- and second-order texture parameters of the QSMs and 3DT1W images were obtained to evaluate group differences using a one-way analysis of covariance. RESULTS: For the first-order QSM analysis, mean, standard deviation, and covariance of signal intensity separated the subject groups (F = 5.191, p = 0.009). For the second-order analysis, angular second moment, contrast, and correlation separated the subject groups (F = 6.896, p = 0.002). Finally, a receiver operating characteristic curve analysis differentiated MCI from CN in white matter on the QSMs (z = 3.092, p = 0.0020). CONCLUSIONS: This was the first study to evaluate the textures of QSM in AD, which overcame the limitations of voxel-based analyses. The QSM texture analysis successfully distinguished both AD and MCI from CN and outperformed the voxel-based analysis using 3DT1-weighed images in separating MCI from CN. The first-order textures were more efficient in differentiating MCI from CN than did the second-order.
Authors: Yi Wang; Pascal Spincemaille; Zhe Liu; Alexey Dimov; Kofi Deh; Jianqi Li; Yan Zhang; Yihao Yao; Kelly M Gillen; Alan H Wilman; Ajay Gupta; Apostolos John Tsiouris; Ilhami Kovanlikaya; Gloria Chia-Yi Chiang; Jonathan W Weinsaft; Lawrence Tanenbaum; Weiwei Chen; Wenzhen Zhu; Shixin Chang; Min Lou; Brian H Kopell; Michael G Kaplitt; David Devos; Toshinori Hirai; Xuemei Huang; Yukunori Korogi; Alexander Shtilbans; Geon-Ho Jahng; Daniel Pelletier; Susan A Gauthier; David Pitt; Ashley I Bush; Gary M Brittenham; Martin R Prince Journal: J Magn Reson Imaging Date: 2017-03-10 Impact factor: 4.813
Authors: Sara Ranjbar; Stefanie N Velgos; Amylou C Dueck; Yonas E Geda; J Ross Mitchell Journal: J Neuropsychiatry Clin Neurosci Date: 2019-01-14 Impact factor: 2.198
Authors: Sérgio Augusto Santana Souza; Leonardo Oliveira Reis; Allan Felipe Fattori Alves; Letícia Cotinguiba Silva; Maria Clara Korndorfer Medeiros; Danilo Leite Andrade; Athanase Billis; João Luiz Amaro; Daniel Lahan Martins; André Petean Trindade; José Ricardo Arruda Miranda; Diana Rodrigues Pina Journal: Phys Eng Sci Med Date: 2022-03-24