Application of pattern recognition framework for quantification of Parkinson's disease in DAT SPECT imaging

Saurabh Jain, Yousef Salimpour, Laurent Younes, Gwenn Smith, Zoltan Mari, Vesna Sossi, Arman Rahmim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Dopamine transporter (DAT) SPECT imaging is increasingly utilized for diagnostic purposes in suspected parkinsonian syndromes. Visual classification or quantitative analysis of mean regional uptake has been performed in the past. Our objective is to enable further enhanced clinical utility in the diagnosis as well as tracking of progression in Parkinson's disease via quantification based on pattern recognition. We developed and implemented two such frameworks: first, we utilized shape/texture metrics that did require registration to a common structure/template; e.g. 3D moment-invariants, Haralick texture features, and multiple others. We also used a surface registration algorithm, which falls under the broad class of Large Deformation Diffeomorphic Metric Mapping (LDDMM). In this latter framework, we obtain a common coordinate system for the entire population based on MR images, and compare SPECT intensities across subjects in this common coordinate system. This method has the advantage of estimating population-based templates for each structure individually rather than using a predetermined collective atlas for all regions, as is customary. In this common coordinate system, we then used Principal Component Analysis (PCA) on intensities to obtain sub-regions (set of voxels inside the structure of interest) with highest variance in SPECT intensities across subjects. We show that the healthy and diseased populations can be subsequently distinguished. Via these methods, we also aimed to assess correlations with different clinical measures (e.g. UPDRS score, disease duration). In addition to enabling enhanced diagnostic task performance, these methods have considerable potential as biomarkers of PD progression.

Original languageEnglish (US)
Title of host publication2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479960972
DOIs
StatePublished - Mar 10 2016
EventIEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014 - Seattle, United States
Duration: Nov 8 2014Nov 15 2014

Other

OtherIEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014
CountryUnited States
CitySeattle
Period11/8/1411/15/14

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ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Radiology Nuclear Medicine and imaging

Cite this

Jain, S., Salimpour, Y., Younes, L., Smith, G., Mari, Z., Sossi, V., & Rahmim, A. (2016). Application of pattern recognition framework for quantification of Parkinson's disease in DAT SPECT imaging. In 2014 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2014 [7430772] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSSMIC.2014.7430772