A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson’s disease at 3T: An efficient susceptibility map‐weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet)

2020 ◽  
Vol 47 (3) ◽  
pp. 1151-1160 ◽  
Author(s):  
Minju Jo ◽  
Se‐Hong Oh
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Deep Neural Network ◽  
Susceptibility Mapping ◽  
Susceptibility Map ◽  
Quantitative Susceptibility Mapping ◽  
Preliminary Attempt

scholarly journals Stability of AI-Enabled Diagnosis of Parkinson’s Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Bin Xiao ◽  
Naying He ◽  
Qian Wang ◽  
Feng Shi ◽  
Zenghui Cheng ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Logistic Regression ◽  
Image Segmentation ◽  
Substantia Nigra ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Negative Impact ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

Purpose: Parkinson’s disease (PD) diagnosis algorithms based on quantitative susceptibility mapping (QSM) and image algorithms rely on substantia nigra (SN) labeling. However, the difference between SN labels from different experts (or segmentation algorithms) will have a negative impact on downstream diagnostic tasks, such as the decrease of the accuracy of the algorithm or different diagnostic results for the same sample. In this article, we quantify the accuracy of the algorithm on different label sets and then improve the convolutional neural network (CNN) model to obtain a high-precision and highly robust diagnosis algorithm.Methods: The logistic regression model and CNN model were first compared for classification between PD patients and healthy controls (HC), given different sets of SN labeling. Then, based on the CNN model with better performance, we further proposed a novel “gated pooling” operation and integrated it with deep learning to attain a joint framework for image segmentation and classification.Results: The experimental results show that, with different sets of SN labeling that mimic different experts, the CNN model can maintain a stable classification accuracy at around 86.4%, while the conventional logistic regression model yields a large fluctuation ranging from 78.9 to 67.9%. Furthermore, the “gated pooling” operation, after being integrated for joint image segmentation and classification, can improve the diagnosis accuracy to 86.9% consistently, which is statistically better than the baseline.Conclusion: The CNN model, compared with the conventional logistic regression model using radiomics features, has better stability in PD diagnosis. Furthermore, the joint end-to-end CNN model is shown to be suitable for PD diagnosis from the perspectives of accuracy, stability, and convenience in actual use.

Quantitative Susceptibility Mapping of the Substantia Nigra in Parkinson’s Disease

2017 ◽  
Vol 48 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Xinxin Zhao ◽  
Hedi An ◽  
Tian Liu ◽  
Nan Shen ◽  
Binshi Bo ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

Quantitative susceptibility mapping as a biomarker for evaluating white matter alterations in Parkinson’s disease

2018 ◽  
Vol 13 (1) ◽  
pp. 220-231 ◽  
Author(s):  
Xiaojun Guan ◽  
Peiyu Huang ◽  
Qiaoling Zeng ◽  
Chunlei Liu ◽  
Hongjiang Wei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping
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