scholarly journals Dopamine transporter SPECT imaging in Parkinson's disease and parkinsonian disorders

2021 ◽  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Spect Imaging ◽  
Parkinsonian Disorders

scholarly journals Serotonin Transporter Availability in Early Stage Parkinson’s Disease and Multiple System Atrophy

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
S. R. Suwijn ◽  
H. W. Berendse ◽  
C. V. M. Verschuur ◽  
R. M. A. de Bie ◽  
J. Booij
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Multiple System Atrophy ◽  
Dopamine Transporter ◽  
Serotonin Transporter ◽  
Disease Duration ◽  
Early Stage ◽  
Spect Imaging ◽  
Parkinsonian Syndrome ◽  
Atypical Parkinsonian Syndrome

Background. Differentiating Parkinson’s disease (PD) from multiple system atrophy (MSA) can be challenging especially early in the course of the disease. Previous studies have shown that midbrain serotonin transporter (SERT) availability in patients with established MSA was significantly lower compared to PD. It is unknown if this is also true for early-stage patients. Methods. 77 early-stage, untreated PD patients were recruited between 1995 and 1998, underwent [123I]β-CIT SPECT imaging, and were followed for at least five years. 16 patients were lost to followup, and in 4 the diagnosis was changed to another atypical parkinsonian syndrome, but not in MSA. In 50 patients, the PD diagnosis was unchanged at followup. In seven patients, the diagnosis was changed to MSA at followup. We retrospectively assessed baseline midbrain SERT availability as well as midbrain SERT-to-striatal dopamine transporter (DAT) ratios. Results. No difference in baseline [123I]β-CIT SERT availability was found. The midbrain SERT-to-striatal DAT ratio for whole striatum was significantly lower in patients with PD compared to MSA (P=0.049). However, when adjusting for the disease duration at imaging this difference is not significant (P=0.070). Conclusion. Midbrain SERT availability is not different between early-stage PD and MSA. Therefore, SERT imaging is not useful to differentiate between early PD and MSA.

The effect of levodopa therapy on dopamine transporter SPECT imaging with 123I-FP-CIT in patients with Parkinson’s disease

2005 ◽  
Vol 32 (12) ◽  
pp. 1452-1456 ◽  
Author(s):  
Orazio Schillaci ◽  
Mariangela Pierantozzi ◽  
Luca Filippi ◽  
Carlo Manni ◽  
Livia Brusa ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Spect Imaging ◽  
Levodopa Therapy

In vivo multimodal (MRI, SPECT) imaging of the 6-OHDA rat model for Parkinson's disease correlated with behavior and histology

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S392-S392
Author(s):  
Nadja Van Camp ◽  
Koen Van Laere ◽  
Ruth Vreys ◽  
Marleen Verhoye ◽  
Erwin Lauwers ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rat Model ◽  
Spect Imaging ◽  
Multimodal Mri

scholarly journals Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Biomedicines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 12
Author(s):  
Chung-Yao Chien ◽  
Szu-Wei Hsu ◽  
Tsung-Lin Lee ◽  
Pi-Shan Sung ◽  
Chou-Ching Lin
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Support Vector ◽  
Dat Spect ◽  
Ann Classifier ◽  
Spect Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

scholarly journals Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging

2017 ◽  
Vol 16 ◽  
pp. 586-594 ◽  
Author(s):  
Hongyoon Choi ◽  
Seunggyun Ha ◽  
Hyung Jun Im ◽  
Sun Ha Paek ◽  
Dong Soo Lee
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Dopamine Transporter ◽  
Dopamine Transporter Imaging
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