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Neuroimage Clin. 2016 Feb 23;12:e1-e9. eCollection 2016.

Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments.

Author information

1
Department of Radiology, Johns Hopkins University, Baltimore, MD, United States; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States.
2
Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, United States.
3
Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.
4
Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada.
5
Department of Physics & Astronomy, University of British Columbia, Vancouver, Canada.
6
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States.

Abstract

Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected Parkinsonian syndromes. We performed a cross-sectional study to investigate whether assessment of texture in DAT SPECT radiotracer uptake enables enhanced correlations with severity of motor and cognitive symptoms in Parkinson's disease (PD), with the long-term goal of enabling clinical utility of DAT SPECT imaging, beyond standard diagnostic tasks, to tracking of progression in PD. Quantitative analysis in routine DAT SPECT imaging, if performed at all, has been restricted to assessment of mean regional uptake. We applied a framework wherein textural features were extracted from the images. Notably, the framework did not require registration to a common template, and worked in the subject-native space. Image analysis included registration of SPECT images onto corresponding MRI images, automatic region-of-interest (ROI) extraction on the MRI images, followed by computation of Haralick texture features. We analyzed 141 subjects from the Parkinson's Progressive Marker Initiative (PPMI) database, including 85 PD and 56 healthy controls (HC) (baseline scans with accompanying 3 T MRI images). We performed univariate and multivariate regression analyses between the quantitative metrics and different clinical measures, namely (i) the UPDRS (part III - motor) score, disease duration as measured from (ii) time of diagnosis (DD-diag.) and (iii) time of appearance of symptoms (DD-sympt.), as well as (iv) the Montreal Cognitive Assessment (MoCA) score. For conventional mean uptake analysis in the putamen, we showed significant correlations with clinical measures only when both HC and PD were included (Pearson correlation r = - 0.74, p-value < 0.001). However, this was not significant when applied to PD subjects only (r = - 0.19, p-value = 0.084), and no such correlations were observed in the caudate. By contrast, for the PD subjects, significant correlations were observed in the caudate when including texture metrics, with (i) UPDRS (p-values < 0.01), (ii) DD-diag. (p-values < 0.001), (iii) DD-sympt (p-values < 0.05), and (iv) MoCA (p-values < 0.01), while no correlations were observed for conventional analysis (p-values = 0.94, 0.34, 0.88 and 0.96, respectively). Our results demonstrated the ability to capture valuable information using advanced texture metrics from striatal DAT SPECT, enabling significant correlations of striatal DAT binding with clinical, motor and cognitive outcomes, and suggesting that textural features hold potential as biomarkers of PD severity and progression.

KEYWORDS:

DAT SPECT; Disease progression; Heterogeneity; Parkinson's disease; Textural features

PMID:
27995072
PMCID:
PMC5153560
DOI:
10.1016/j.nicl.2016.02.012
[Indexed for MEDLINE]
Free PMC Article

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