Abstract
PurposeDifferences in site, device, and/or settings may cause large variations in the intensity profile of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images. However, the current standard to evaluate these images, the striatal binding ratio (SBR), does not efficiently account for this heterogeneity and the assessment can be unequivalent across distinct acquisition pipelines. In this work, we present a voxel-based automated approach to intensity normalize such type of data that improves on cross-session interpretation.ProceduresThe normalization method consists of a reparametrization of the voxel values based on the cumulative density function (CDF) of a Gamma distribution modeling the specific region intensity. The harmonization ability was tested in 1342 SPECT images from the PPMI repository, acquired with 7 distinct gamma camera models and at 24 different sites. We compared the striatal quantification across distinct cameras for raw intensities, SBR values, and after applying the Gamma CDF (GDCF) harmonization. As a proof-of-concept, we evaluated the impact of GCDF normalization in a classification task between controls and Parkinson disease patients.ResultsRaw striatal intensities and SBR values presented significant differences across distinct camera models. We demonstrate that GCDF normalization efficiently alleviated these differences in striatal quantification and with values constrained to a fixed interval [0, 1]. Also, our method allowed a fully automated image assessment that provided maximal classification ability, given by an area under the curve (AUC) of AUC = 0.94 when used mean regional variables and AUC = 0.98 when used voxel-based variables.ConclusionThe GCDF normalization method is useful to standardize the intensity of DAT SPECT images in an automated fashion and enables the development of unbiased algorithms using multicenter datasets. This method may constitute a key pre-processing step in the analysis of this type of images.
Highlights
The imaging of the dopamine transporter (DAT) with nuclear medicine techniques, such as single-photon emission computed tomography (SPECT) or positron emission tomography (PET), is a very useful and widespread tool for the diagnosis of Parkinson’s disease (PD) and other neurodegenerative parkinsonisms [1]
We propose to intensity normalize DAT SPECT images by modeling the intensity profile with a mixture model of Gamma distributions
We have introduced a probabilistic normalization schema valid for DAT SPECT images based on a mixture of Gamma distributions
Summary
The imaging of the dopamine transporter (DAT) with nuclear medicine techniques, such as single-photon emission computed tomography (SPECT) or positron emission tomography (PET), is a very useful and widespread tool for the diagnosis of Parkinson’s disease (PD) and other neurodegenerative parkinsonisms [1]. In these diseases, there is a progressive degeneration of the dopaminergic neurons in the nigrostriatal pathway, which projects from the substantia nigra to the striatum. Its use was approved by the European Medicines Agency in 2000 and by the Food and Drug Administration in the USA in January 2011 [3]
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