Parkinson Disease: Diagnostic Utility of Diffusion Kurtosis Imaging

Wang, Jiun‐Jie; Lin, Wen−Piao; Lu, Chin‐Song; Weng, Yi-Hsin; Ng, Shu‐Hang; Wang, Chi-Hong; Liu, Hao-Li; Hsieh, Ren-Hsiang; Wan, Yung‐Liang; Wai, Yau‐Yau

doi:10.1148/radiol.11102277

Cited by 216 publications

(214 citation statements)

References 38 publications

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“…Although our method could not discriminate 2 groups perfectly (i.e., there were overlaps in SNc volumes), it could discriminate PD patients from control subjects with almost equal or better performance compared with other imaging methods, such as contrast ratio method using neuromelanin images [7,8], and diffusion-kurtosis imaging [16]. Therefore, our method can be potentially used for clinical diagnosis of PD patients, especially for early stage.…”

Section: Discussionmentioning

confidence: 87%

3D neuromelanin-sensitive magnetic resonance imaging with semi-automated volume measurement of the substantia nigra pars compacta for diagnosis of Parkinson’s disease

et al. 2013

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Purpose: Neuromelanin-sensitive MRI has been reported to be used in the diagnosis of Parkinson's disease (PD), which results from loss of dopamine-producing cells in the substantia nigra pars compacta (SNc). In this study, we aimed to apply a 3D turbo field echo (TFE) sequence for neuromelanin-sensitive MRI and to evaluate the diagnostic performance of semi-automated method for measurement of SNc volume in patients with PD.Methods: We examined 18 PD patients and 27 healthy volunteers (control subjects). A 3D TFE technique with off-resonance magnetization transfer pulse was used for neuromelanin-sensitive MRI on a 3T scanner. The SNc volume was semi-automatically measured using a region-growing technique at various thresholds (ranging from 1.66 to 2.48), with the signals measured relative to that for the superior cerebellar peduncle.Receiver operating characteristic (ROC) analysis was performed at all thresholds. Intra-rater reproducibility was evaluated by intraclass correlation coefficient (ICC).Results: The average SNc volume in the PD group was significantly smaller than that in the control group at all the thresholds (P < 0.01, student t test). At higher thresholds (>2.0), the area under the curve of ROC (Az) increased (0.88). In addition, we observed balanced sensitivity and specificity (0.83 and 0.85, respectively). At lower thresholds, sensitivity tended to increase but specificity reduced in comparison with that at higher thresholds. ICC was larger than 0.9 when the threshold was over 1.86.Conclusions: Our method can distinguish the PD group from the control group with high sensitivity and specificity, especially for early stage of PD.

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Section: Discussionmentioning

confidence: 87%

3D neuromelanin-sensitive magnetic resonance imaging with semi-automated volume measurement of the substantia nigra pars compacta for diagnosis of Parkinson’s disease

et al. 2013

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“…76 FA of the cingulate 77 and Wang's team showed that the mean kurtosis of the ipsilateral substantia nigra performed best in diagnosing Parkinson disease (sensitivity, 0.92; speciˆcity, 0.87), compared with conventional diŠusion metrics including FA, mean diŠusivity, axial diŠusivity, and radial diŠusivity. 78 Therefore, non-Gaussian DWI and DKI, in particular, may be promising for the evaluation of degenerative changes in vivo.…”

Section: Neuropsychiatric Diseasementioning

confidence: 99%

Visualizing Non-Gaussian Diffusion: Clinical Application of q-Space Imaging and Diffusional Kurtosis Imaging of the Brain and Spine

Hori

Fukunaga

Masutani

et al. 2012

MRMS

105

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Recently, non-Gaussian diŠusion-weighted imaging (DWI) techniques, including qspace imaging (QSI) and diŠusional kurtosis imaging (DKI), have emerged as advanced methods to evaluate tissue microstructure in vivo using water diŠusion. QSI and DKI have shown promising results in clinical applications, such as in the evaluation of brain tumors (e.g., grading gliomas), degenerative diseases (e.g., speciˆc diagnosis of Parkinson disease), demyelinating diseases (e.g., assessment of normal-appearing tissue of multiple sclerosis), and cerebrovascular diseases (e.g., assessment of the microstructural environment of fresh infarctions). Representative metrics in clinical use are the full width at half maximum, also known as the mean displacement of the probability density function curve, which is derived from QSI, and diŠusional kurtosis, which is derived from DKI. These new metrics may provide information on tissue structure in addition to that provided by conventional Gaussian DWI investigations that use the apparent diŠusion coe‹cient and fractional anisotropy, recognized indices for evaluating disease and normal development in the brain and spine. In some clinical situations, sensitivity for detecting pathological conditions is higher using QSI and DKI than conventional DWI and diŠusion tensor imaging (DTI) because DWI and DTI calculations are based on the assumption that water molecules follow a Gaussian distribution, whereas hindrance of the distribution of water molecules by complex and restricted structures in actual neural tissues produces distributions that are far from Gaussian. We review the technical aspects and clinical applications of QSI and DKI, focusing on clinical use and in vivo studies and highlighting diŠerences from conventional diŠusional metrics.

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“…To our surprise, computation time differed only slightly among the derived methods for 6 b-values-GCF, WGCF, SOR, MOR, NS, and BNS. This can be due to pre-computation of several constant values, $ i0 , of [10], which are independent of signal values. Such methods as SOR, MOR, and BNS with several choices for minimizing residuals took slightly more time than the baseline methods of GCF and JCF, but the increase was less than 10%.…”

Section: Discussionmentioning

confidence: 99%

“…Unlike DTI and its matured metrics, such as fractional anisotropy (FA), DKI and its derived parameters are in development. However, several reports of clinical applications reveal important features of DKI, such as its potential usefulness in diagnostic imaging to evaluate neurological issues in aging 4 and disorders including stroke, 5 Alzheimer's disease, 6 schizophrenia, 7 glioma, 8,9 Parkinson disease, 10 and attention deficit hyperactivity disorder. 11 Recent trials on DKI in such regions as the liver 12 and spine 13 have also been reported.…”

Section: Introductionmentioning

confidence: 99%

Fast and Robust Estimation of Diffusional Kurtosis Imaging (DKI) Parameters by General Closed-form Expressions and their Extensions

Masutani

Aoki

2014

MRMS

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Diffusional kurtosis imaging (DKI) for clinical imaging involves time-consuming computation and demonstrates low robustness. Standard estimation of DKI parameters is based on an extension of Stejskal-Tanner's signal model with squared b-value term and is a leastsquares fitting problem. The use of numerical methods for computation requires time, and estimation of DKI parameters is noise sensitive and often produces noisy results, such as images with pepper noise.In this study, we propose general closed-form solutions for DKI parameters to avoid numerical computation for least-squares fitting, solutions that can be applied to diffusion weighted imaging (DWI) datasets with any number of b-values more than three. Solutions are obtained through stationary-point conditions of an objective function that are minimized for fitting. We use 3 techniques to extend the solutions to increase robustnessb-value-dependent weighting in fitting, removal of outliers, and addition of neighbor sampling. Based on synthetic datasets and clinical datasets that both consist of 6 b-value and 3 b-value datasets, we detail and compare the 3 methods including a method by Jensen et al. are compared and investigated in detail. The synthetic data consist of several combinations of DKI parameters and some Rician noise. In addition to visually assessing result images, we also performed quantitative evaluation using a range of estimated parameters, positivedefiniteness of the objective function for fitting, and root-mean-square error including estimation bias from the true value (synthetic data only). Methods that added neighbor sampling outperformed others in terms of low errors and visual smoothness. Though the solution by our methodis to estimate DKI parameters in a single MPG direction, it can contribute to anisotropic analysis of diffusional kurtosis such as kurtosis tensor. More robust estimation is expected by combining techniques.

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Parkinson Disease: Diagnostic Utility of Diffusion Kurtosis Imaging

Abstract: Diffusion kurtosis imaging in the basal ganglia, as compared with conventional diffusion-tensor imaging, can improve the diagnosis of PD.

Cited by 216 publications

References 38 publications

3D neuromelanin-sensitive magnetic resonance imaging with semi-automated volume measurement of the substantia nigra pars compacta for diagnosis of Parkinson’s disease

3D neuromelanin-sensitive magnetic resonance imaging with semi-automated volume measurement of the substantia nigra pars compacta for diagnosis of Parkinson’s disease

Visualizing Non-Gaussian Diffusion: Clinical Application of q-Space Imaging and Diffusional Kurtosis Imaging of the Brain and Spine

Fast and Robust Estimation of Diffusional Kurtosis Imaging (DKI) Parameters by General Closed-form Expressions and their Extensions

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