Literature DB >> 30578927

3D texture analyses within the substantia nigra of Parkinson's disease patients on quantitative susceptibility maps and R2 maps.

Gaiying Li1, Guoqiang Zhai2, Xinxin Zhao3, Hedi An4, Pascal Spincemaille5, Kelly M Gillen6, Yixuan Ku7, Yi Wang8, Dongya Huang9, Jianqi Li10.   

Abstract

Iron accumulation in the substantia nigra (SN) is spatially heterogeneous, yet no study has quantitatively evaluated how the texture of quantitative susceptibility maps (QSM) and R2∗ might evolve with Parkinson's disease (PD) and healthy controls (HC). The aim of this study was to discriminate between patients with PD and HC using texture analysis in the SN from QSM and R2∗ maps. QSM and R2∗ maps were obtained from 28 PD patients and 28 HC on a clinical 3T MR imaging scanner using 3D multi-echo gradient-echo sequence. The first- and second- order texture features of the QSM and R2∗ images were obtained to evaluate group differences using two-tailed t-test. After correction for multiple comparisons, for the first-order analysis, the susceptibility of SN from patients with PD was significantly greater (p = 0.017) compared with the SN from HC. For the second-order texture analysis, angular second moment, entropy, and sum of entropy showed significant differences in QSM (p < 0.001) and R2∗ maps (p < 0.01). In addition, correlation, contrast, sum of variance and difference of variance, significantly separated the subject groups in QSM maps (p < 0.05) but not in R2∗ images. Receiver operating characteristic analysis showed that entropy and sum of entropy of the QSM maps in the SN yielded the highest performance for differentiating PD patients from HC (area under the curve = 0.89). In conclusion, most first- and second- order QSM texture features successfully distinguished PD patients from HC and significantly outperformed R2∗ texture analysis. The second-order texture features were more accurate and sensitive than first-order texture features for classifying PD patients.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Parkinson's disease; Quantitative susceptibility mapping; R2(∗) mapping; Texture analysis

Year:  2018        PMID: 30578927     DOI: 10.1016/j.neuroimage.2018.12.041

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  17 in total

1.  Iron-sensitive magnetic resonance imaging in Parkinson's disease: a systematic review and meta-analysis.

Authors:  Se Jin Cho; Yun Jung Bae; Jong-Min Kim; Hyun Jin Kim; Sung Hyun Baik; Leonard Sunwoo; Byung Se Choi; Cheolkyu Jung; Jae Hyoung Kim
Journal:  J Neurol       Date:  2021-04-29       Impact factor: 4.849

2.  Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy.

Authors:  Xuehan Hu; Xun Sun; Fan Hu; Fang Liu; Weiwei Ruan; Tingfan Wu; Rui An; Xiaoli Lan
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-04-07       Impact factor: 9.236

Review 3.  Structural Imaging in Parkinson's Disease: New Developments.

Authors:  Stéphane Prange; Elise Metereau; Stéphane Thobois
Journal:  Curr Neurol Neurosci Rep       Date:  2019-06-18       Impact factor: 5.081

4.  Different iron deposition patterns in Parkinson's disease subtypes: a quantitative susceptibility mapping study.

Authors:  Junling Chen; Tongtong Cai; Yan Li; Jieshan Chi; Siming Rong; Chentao He; Xiaohong Li; Piao Zhang; Lijuan Wang; Yuhu Zhang
Journal:  Quant Imaging Med Surg       Date:  2020-11

5.  A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson's disease: a brain radiomics study.

Authors:  Xiao-Jun Guan; Tao Guo; Cheng Zhou; Ting Gao; Jing-Jing Wu; Victor Han; Steven Cao; Hong-Jiang Wei; Yu-Yao Zhang; Min Xuan; Quan-Quan Gu; Pei-Yu Huang; Chun-Lei Liu; Jia-Li Pu; Bao-Rong Zhang; Feng Cui; Xiao-Jun Xu; Min-Ming Zhang
Journal:  Neural Regen Res       Date:  2022-12       Impact factor: 6.058

6.  Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson's disease.

Authors:  Dafna Ben Bashat; Avner Thaler; Hedva Lerman Shacham; Einat Even-Sapir; Matthew Hutchison; Karleyton C Evans; Avi Orr-Urterger; Jesse M Cedarbaum; Amgad Droby; Nir Giladi; Anat Mirelman; Moran Artzi
Journal:  NPJ Parkinsons Dis       Date:  2022-10-21

7.  Substantia Nigra Volume Dissociates Bradykinesia and Rigidity from Tremor in Parkinson's Disease: A 7 Tesla Imaging Study.

Authors:  Kathleen L Poston; Matthew A I Ua Cruadhlaoich; Laura F Santoso; Jeffrey D Bernstein; Tian Liu; Yi Wang; Brian Rutt; Geoffrey A Kerchner; Michael M Zeineh
Journal:  J Parkinsons Dis       Date:  2020       Impact factor: 5.568

8.  Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI.

Authors:  Sumeet Shinde; Shweta Prasad; Yash Saboo; Rishabh Kaushick; Jitender Saini; Pramod Kumar Pal; Madhura Ingalhalikar
Journal:  Neuroimage Clin       Date:  2019-03-06       Impact factor: 4.881

9.  Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging.

Authors:  Panshi Liu; Han Wang; Shilei Zheng; Fan Zhang; Xianglin Zhang
Journal:  Front Neurol       Date:  2020-04-08       Impact factor: 4.003

10.  Iron Imaging as a Diagnostic Tool for Parkinson's Disease: A Systematic Review and Meta-Analysis.

Authors:  Nadya Pyatigorskaya; Clara B Sanz-Morère; Rahul Gaurav; Emma Biondetti; Romain Valabregue; Mathieu Santin; Lydia Yahia-Cherif; Stéphane Lehéricy
Journal:  Front Neurol       Date:  2020-05-28       Impact factor: 4.003

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