Literature DB >> 23570700

[Elaboration of the SPM template for the standardization of SPECT images with 123I-Ioflupane].

F J García-Gómez1, D García-Solís, F J Luis-Simón, V A Marín-Oyaga, F Carrillo, P Mir, R J Vázquez-Albertino.   

Abstract

PURPOSE: Statistical parametric mapping (SPM) is a widely used produced for normalization of functional images. This study has aimed to develop a normalization template of (123)I-Ioflupane SPECT-imaging DaTSCAN(®), GE Healthcare), not available in SPM5, and to validate it compared to other quantification methods.
MATERIAL AND METHODS: In order to write the template we retrospectively selected 26 subjects who had no evidence of nigrostriatal degeneration and whose age distribution was similar to that of the patients in the usual practice of our Department: 2 subjects (7.6%) were < 35 years, 9 between 35-65 years (34.6%) and 15 > 65 years (57.7%). All the studies were normalized with the T1-template available in SPM5 and an average image of the value was obtained for each voxel. For validation we analyzed 60 patients: 30 with idiopathic Parkinson's disease patients (iPD) with right involvement (66.83±12.20 years) and 30 with essential tremor patients (ET) (67.27±8.33 years). Specific uptake rates (SUR) of different striatal regions were compared after image normalization with our template and the application of a semiautomated VOIs-map created with Analyze v9.0 ((©)BIR, Mayo Clinic), against two quantification methods: a) manual adjustment of a ROIs-map drawn in Analyze, and b) semi-automated method (HERMES-BRASS) with normalization and implementation of VOIs-map.
RESULTS: No statistically significant differences in the iPD/ET discriminatory capacity between the three methods analyzed were observed (p<0,001). The correlation of SUR after normalization with our «template» was higher than that obtained by method b) (R>0,871, p<0,001). This difference was greater in patients with PD.
CONCLUSIONS: Our study demonstrates the efficacy of our SPM «template» for (123)I-Ioflupane SPECT-imaging, obtained from normalization with «T1-template».
Copyright © 2012 Elsevier España, S.L. and SEMNIM. All rights reserved.

Entities:  

Keywords:  Dopamina; Dopamine; Dopamine transporters; Enfermedad de Parkinson; Mapas estadísticos paramétricos; Movement disorders; Parkinson's disease; Statistic parametric mapping; Striatal dopamine transporters; Transportadores de dopamina; Transportadores de dopamina en el estriado; Trastornos del movimiento

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Year:  2013        PMID: 23570700     DOI: 10.1016/j.remn.2013.02.009

Source DB:  PubMed          Journal:  Rev Esp Med Nucl Imagen Mol        ISSN: 2253-654X            Impact factor:   1.359


  22 in total

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2.  Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [(123)I]FP-CIT SPECT.

Authors:  I Huertas-Fernández; F J García-Gómez; D García-Solís; S Benítez-Rivero; V A Marín-Oyaga; S Jesús; M T Cáceres-Redondo; J A Lojo; J F Martín-Rodríguez; F Carrillo; P Mir
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Journal:  Nat Med       Date:  2020-03-23       Impact factor: 53.440

5.  Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models.

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7.  Physiological Whole-Brain Distribution of [18F]FDOPA Uptake Index in Relation to Age and Gender: Results from a Voxel-Based Semi-quantitative Analysis.

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8.  Effect of sex on aging-related decline of dopamine transporter in healthy subjects.

Authors:  Seunghyeon Shin; Hyun-Yeol Nam; Myung Jun Lee; Kyoungjune Pak; Keunyoung Kim; In Joo Kim
Journal:  Ann Nucl Med       Date:  2020-10-14       Impact factor: 2.668

9.  Mapping dopaminergic projections in the human brain with resting-state fMRI.

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Journal:  Elife       Date:  2022-02-03       Impact factor: 8.140

10.  Source-Based Morphometry Multivariate Approach to Analyze [123I]FP-CIT SPECT Imaging.

Authors:  Enrico Premi; V D Calhoun; V Garibotto; R Turrone; A Alberici; E Cottini; A Pilotto; S Gazzina; M Magoni; B Paghera; B Borroni; A Padovani
Journal:  Mol Imaging Biol       Date:  2017-10       Impact factor: 3.488

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