Diffusion kurtosis imaging for diagnosis of Parkinson’s disease: A novel software tool proposal

2017 ◽  
Vol 25 (4) ◽  
pp. 561-571
Author(s):  
Gloria Cruz ◽  
Sheng Dong Nie
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Software Tool ◽  
Diffusion Kurtosis Imaging ◽  
Diffusion Kurtosis

scholarly journals Diffusion Kurtosis Imaging of Substantia Nigra Is a Sensitive Method for Early Diagnosis and Disease Evaluation in Parkinson’s Disease

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Guohua Zhang ◽  
Yuhu Zhang ◽  
Chengguo Zhang ◽  
Yukai Wang ◽  
Guixian Ma ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Diffusion Kurtosis Imaging ◽  
Healthy Controls ◽  
Potential Biomarker ◽  
Diffusion Kurtosis ◽  
Sensitive Method

Background.To diagnose Parkinson disease (PD) in an early stage and accurately evaluate severity, it is important to develop a sensitive method for detecting structural changes in the substantia nigra (SN).Method.Seventy-two untreated patients with early PD and 72 healthy controls underwent diffusion tensor and diffusion kurtosis imaging. Regions of interest were drawn in the rostral, middle, and caudal SN by two blinded and independent raters. Mean kurtosis (MK) and fractional anisotropy in the SN were compared between the groups. Receiver operating characteristic (ROC) and Spearman correlation analyses were used to compare the diagnostic accuracy and correlate imaging findings with Hoehn-Yahr (H-Y) staging and part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III).Result.MK in the SN was increased significantly in PD patients compared with healthy controls. The area under the ROC curve was 0.976 for MK in the SN (sensitivity, 0.944; specificity, 0.917). MK in the SN had a positive correlation with H-Y staging and UPDRS-III scores.Conclusion.Diffusion kurtosis imaging is a sensitive method for PD diagnosis and severity evaluation. MK in the SN is a potential biomarker for imaging studies of early PD that can be widely used in clinic.

scholarly journals Late-stage α-synuclein accumulation in TNWT-61 mouse model of Parkinson's disease detected by diffusion kurtosis imaging

2016 ◽  
Vol 136 (6) ◽  
pp. 1259-1269 ◽  
Author(s):  
Amit Khairnar ◽  
Jana Ruda-Kucerova ◽  
Eva Drazanova ◽  
Nikoletta Szabó ◽  
Peter Latta ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Mouse Model ◽  
Late Stage ◽  
Diffusion Kurtosis Imaging ◽  
Diffusion Kurtosis

scholarly journals CHANGES IN THE MICROSTRUCTURE AND FUNCTION OF BRAIN TISSUE IN PD BY DIFFUSION KURTOSIS IMAGING

2021 ◽  
Author(s):  
LEI WANG ◽  
XIN LIU ◽  
SHUOHUA WU ◽  
FANG CHEN ◽  
YE ZHENG ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Rating Scale ◽  
Early Stage ◽  
Roc Curves ◽  
Diffusion Kurtosis Imaging ◽  
Diagnostic Efficacy ◽  
Brain Microstructure ◽  
Diffusion Kurtosis

This study proposed to detect changes in brain microstructure in patients with Parkinson’s disease (PD) using diffusion kurtosis imaging (DKI) to quantitatively diagnose early-stage PD. Conventional magnetic resonance imaging and DKI scanning were performed in 24 patients with PD and in 12 age- and sex-matched healthy participants. Hoehn and Yahr (H–Y) stage and Unified Parkinson’s Disease Rating Scale-III (UPDRS-III) scores were obtained from both groups. The mean kurtosis (MK), axial kurtosis, and radial kurtosis of the bilateral substantia nigra on DKI were measured and compared between the two groups. The correlations between MK, H–Y stage, and UPDRS-III scores were determined. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of MK for PD in the substantia nigra. The MK value in the PD group was 0.971. The area under the ROC curve of the substantia nigra was 0.905; the sensitivity and specificity were 0.917 and 0.875, respectively, and the cutoff value was 1.046. The MK of the substantia nigra in the PD group had no significant correlation with the H–Y stages but was negatively correlated with the UPDRS-III scores ([Formula: see text]; [Formula: see text]). Our research identified DKI as a novel tool for the qualitative diagnosis of PD. The optimal MK value for PD diagnosis could be determined with ROC analysis.

scholarly journals Alteration of putaminal fractional anisotropy in Parkinson’s disease: a longitudinal diffusion kurtosis imaging study

2018 ◽  
Vol 60 (3) ◽  
pp. 247-254 ◽  
Author(s):  
Yulia Surova ◽  
Markus Nilsson ◽  
Björn Lampinen ◽  
Jimmy Lätt ◽  
Sara Hall ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fractional Anisotropy ◽  
Imaging Study ◽  
Diffusion Kurtosis Imaging ◽  
Longitudinal Diffusion ◽  
Diffusion Kurtosis

scholarly journals Convolutional Neural Network Optimize the Application of Diffusion Kurtosis Imaging in Parkinson’s Disease

2021 ◽  
Author(s):  
Junyan Sun ◽  
Ruike Chen ◽  
Qiqi Tong ◽  
Jinghong Ma ◽  
Linlin Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Model Fitting ◽  
Diffusion Kurtosis Imaging ◽  
Clinical Assessments ◽  
Fitting Method ◽  
Diffusion Kurtosis

Abstract Objectives: This work attempted to assess the feasibility of deep-learning based method in detecting the alterations of diffusion kurtosis measurements associated with Parkinson's disease (PD). Methods: A group of 68 PD patients and 77 healthy controls (HCs) were scanned on the scanner-A (3T Skyra) (DATASET-1). Meanwhile, an additional 5 healthy travelling volunteers were scanned with both the scanner-A and an additional scanner-B (3T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 has an extra b shell than that of DATASET-1. In addition, a 3D convolutional neural network (CNN) was trained from Dataset 2 to harmonize the quality of scalar measures of scanner-A to a similar level of scanner-B. Whole-brain unpaired t-test and Tract-Based Spatial Statistics (TBSS) was performed to validate the differences between PD and control groups with model fitting method and CNN method respectively. We further clarified the correlation between clinical assessments and DKI results.Results: In the left substantia nigra (SN), an increase of mean diffusivity (MD) was found in PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn & Yahr (H&Y) scales. In the putamen, FA values was positively correlated with H&Y scales. It is worth noting that, these findings were only observed with the deep-learning method. There was neither group difference, nor correlation with clinical assessments in the SN or striatum exceeding the significant level by using the conventional model fitting method.Conclusions: CNN method improves the robustness of DKI and can help to explore PD-associated imaging features.

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