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Abstract

Background

Neuromelanin magnetic resonance imaging (NmMRI) and ¹²³I-FP-CIT dopamine transporter single photon emission computed tomography (DAT-SPECT) provide specific information that distinguishes Parkinson's disease (PD) from non-degenerative parkinsonian syndrome (NDPS).

Purpose

To determine whether a multiparametric scoring system (MSS) could improve accuracy compared to each parameter of DAT-SPECT and NmMRI in differentiating PD from NDPS.

Material and Methods

A total of 49 patients, including 14 with NDPS, 30 with PD, and five with atypical parkinsonian disorder (APD) underwent both NmMRI and DAT-SPECT and were evaluated. The average (Ave) and the asymmetry index (AI) were calculated in the substantia nigra compacta area (SNc-area), SNc midbrain-tegmentum contrast ratio (SNc-CR), and specific binding ratio (SBR). Cut-off values were determined, using receiver operating characteristic (ROC) analysis, for the differentiation of PD from NDPS on the statistically significant parameters. All cases were scored as either 1 (PD) or 0 (NDPS) for each parameter according to its threshold. These individual scores were totaled for each case, yielding a combined score for each case to obtain a cut-off value for the MSS.

Results

The Ave-SNc-area, Ave-SNc-CR, and Ave-SBR in PD were significantly lower than those in NDPS. The AI-SNc-area and AI-SBR in PD were significantly higher than those in NDPS. Of the five parameters, the highest accuracy was 93% for the Ave-SNc-area. For the MSS, a cut-off value of 3 was the accuracy of 96%. Besides, no significant difference was observed between PD and APD on all parameters.

Conclusion

An MSS has comparable or better accuracy compared to each parameter of DAT-SPECT and NmMRI in distinguishing PD from NDPS.

Keywords

Magnetic resonance imaging neuromelanin imaging dopamine transporter imaging Parkinson's disease combined analysis

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The utility of the combined use of 123 I-FP-CIT SPECT and neuromelanin MRI in differentiating Parkinson's disease from other parkinsonian syndromes

Abstract

Background

Purpose

Material and Methods

Results

Conclusion

Keywords

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References