Automatic segmentation and quantification of Nigrosome-1 neuromelanin and iron in MRI: A candidate biomarker for parkinson's disease

Date

2023

Authors

Martínez, Martín
Álvarez, Ignacio
Fernández Seara, María A.
Castellanos, Gabriel
Catalonian Neuroimaging Parkinson's Disease Consortium
Pastor, Pau
Pastor, María A.
Ortiz de Solórzano, Carlos

Director

Publisher

Wiley
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

  • //PDI2021-122409OB-C22/
  • AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094494-B-C22/ES/ recolecta
  • //TED2021-131300B-I00/
  • //SAF2016-81016-R/
Impacto

Abstract

Background: There is a lack of automated tools for the segmentation and quantification of neuromelanin (NM) and iron in the nigrosome-1 (N1). Existing tools evaluate the N1 sign, i.e., the presence or absence of the ‘swallow-tail’ in iron-sensitive MRI, or globally analyze the MRI signal in an area containing the N1, without providing a volumetric delineation. Purpose: Present an automated method to segment the N1 and quantify differences in N1's NM and iron content between Parkinson's disease (PD) patients and healthy controls (HCs). Study whether N1 degeneration is clinically related to PD and could be used as a biomarker of the disease. Study Type: Prospective. Subjects: Seventy-one PD (65.3 ± 10.3 years old, 34 female/37 male); 30 HC (62.7 ± 7.8 years old, 17 female/13 male). Field Strength/Sequence: 3 T Anatomical T1-weighted MPRAGE, NM-MRI T1-weighted gradient with magnetization transfer, susceptibility-weighted imaging (SWI). Assessment: N1 was automatically segmented in SWI images using a multi-image atlas, populated with healthy N1 structures manually annotated by a neurologist. Relative NM and iron content were quantified and their diagnostic performance assessed and compared with the substantia nigra pars compacta (SNc). The association between image parameters and clinically relevant variables was studied. Statistical Tests: Nonparametric tests were used (Mann-Whitney's U, chi-square, and Friedman tests) at P = 0.05. Results: N1's relative NM content decreased and relative iron content increased in PD patients compared with HCs (NM-CRHC = 22.55 ± 1.49; NM-CRPD = 19.79 ± 1.92; NM-nVolHC = 2.69 × 10-5 ± 1.02 × 10-5; NM-nVolPD = 1.18 × 10-5 ± 0.96 × 10−5; Iron-CRHC = 10.51 ± 2.64; Iron-CRPD = 19.35 ± 7.88; Iron-nVolHC = 0.72 × 10-5 ± 0.81 × 10-5; Iron-nVolPD = 2.82 × 10−5 ± 2.04 × 10−5). Binary logistic regression analyses combining N1 and SNc image parameters yielded a top AUC = 0.955. Significant correlation was found between most N1 parameters and both disease duration (ρNM-CR = -0.31; ρiron-CR = 0.43; ρiron-nVol = 0.46) and the motor status ρNM-nVol = -0.27; ρiron-CR = 0.33; ρiron-nVol = 0.28), suggesting NM reduction along with iron accumulation in N1 as the disease progresses. Data Conclusion: This method provides a fully automatic N1 segmentation, and the analyses performed reveal that N1 relative NM and iron quantification improves diagnostic performance and suggest a relative NM reduction along with a relative iron accumulation in N1 as the disease progresses. Evidence Level: 1. Technical Efficacy: Stage 1.

Description

Keywords

Automatic segmentation, Iron, Neuromelanin, Nigrosome-1, Parkinson's disease, Susceptibility weighted imaging

Department

Ingeniería Eléctrica, Electrónica y de Comunicación / Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza / Institute of Smart Cities - ISC

Faculty/School

Degree

Doctorate program

item.page.cita

Ariz, M., Martínez, M., Alvarez, I., Fernández¿Seara, M. A., Castellanos, G., The Catalonian Neuroimaging Parkinson¿s Disease Consortium, Pastor, P., Pastor, M. A., Ortiz de Solórzano, C. (2023) Automatic segmentation and quantification of Nigrosome-1 neuromelanin and iron in MRI: A candidate biomarker for parkinson's disease. Journal of Magnetic Resonance Imaging, 1-14. https://doi.org/10.1002/jmri.29073.

item.page.rights

© 2023 Fundacion para la Investigacion Médica Aplicada. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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