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Fast 3 T nigral hyperintensity magnetic resonance imaging in Parkinson's disease.

ABSTRACT: The absence of nigral hyperintensity is a promising MR marker for Parkinson's disease (PD), but its small size imposes limitations on its routine use. Our aim was to compare Multi Echo Data Image Combination (MEDIC), segmented echo-planar imaging (EPISEG) and fluid-attenuated inversion recovery (FLAIR) sequences, as well as both magnitude (MAG) and susceptibility-weighted imaging (SWI) reconstructions of single-echo gradient echo for nigral hyperintensity imaging. Twenty-five healthy and twenty PD subjects were included. Sensitivity to motion artefacts, confidence of the radiologist in interpretation, rate of nondiagnostic scans and diagnostic accuracy were assessed. EPISEG was less motion-sensitive than MEDIC, MAG, and SWI, while FLAIR was less motion-sensitive than MAG and SWI. The reviewers were more confident when using EPISEG compared to any other techniques and MEDIC was superior to FLAIR. The proportions of nondiagnostic scans were lower for EPISEG than for other sequences. The best diagnostic performance was achieved for EPISEG (sensitivity?=?65%, specificity?=?96%). Using EPISEG, the absence of nigral hyperintensity in PD was associated with higher Hoehn-Yahr stage and MDS-UPDRS II?+?III. Nigral hyperintensity may be intact at the very early stages of PD. The promising properties of EPISEG may help the transfer of nigral hyperintensity imaging into daily clinical practice.

SUBMITTER: Hernadi G

PROVIDER: S-EPMC7806895 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Fast 3 T nigral hyperintensity magnetic resonance imaging in Parkinson's disease.

Hernadi Gabriella G Pinter David D Nagy Szilvia Anett SA Orsi Gergely G Komoly Samuel S Janszky Jozsef J Kovacs Norbert N Perlaki Gabor G

Scientific reports 20210113 1

The absence of nigral hyperintensity is a promising MR marker for Parkinson's disease (PD), but its small size imposes limitations on its routine use. Our aim was to compare Multi Echo Data Image Combination (MEDIC), segmented echo-planar imaging (EPISEG) and fluid-attenuated inversion recovery (FLAIR) sequences, as well as both magnitude (MAG) and susceptibility-weighted imaging (SWI) reconstructions of single-echo gradient echo for nigral hyperintensity imaging. Twenty-five healthy and twenty ...[more]

PMID: 33441889

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Project description:Background and objectivesMagnetic resonance imaging with arterial spin labeling (ASL) perfusion imaging is a noninvasive method for quantifying cerebral blood flow (CBF). We aimed to evaluate the clinical utility of ASL perfusion imaging to aid in the diagnosis of Creutzfeldt-Jakob disease (CJD).MethodsThis retrospective study enrolled 10 clinically diagnosed with probable sporadic CJD (sCJD) based on the National CJD Research & Surveillance Unit and EuroCJD criteria and 18 healthy controls (HCs). Diffusion-weighted images (DWIs), CBF images obtained from ASL, N-isopropyl-(123I)-p-iodoamphetamine (123IMP)-single-photon emission computed tomography (SPECT) images, and 18F-fluorodeoxyglucose (18FDG)-positron emission tomography (PET) images were analyzed. First, the cortical values obtained using volume-of-interest (VOI) analysis were normalized using the global mean in each modality. The cortical regions were classified into DWI-High (≥ +1 SD) and DWI-Normal (< +1 SD) regions according to the DWI-intensity values. The normalized cortical values were compared between the two regions for each modality. Second, each modality value was defined as ASL hypoperfusion (< -1 SD), SPECT hypoperfusion (< -1 SD), and PET low accumulation (< -1 SD). The overall agreement rate of DWIs with ASL-CBF, SPECT, and PET was calculated. Third, regression analyses between the normalized ASL-CBF values and normalized SPECT or PET values derived from the VOIs were performed using a scatter plot.ResultsThe mean values of ASL-CBF (N = 10), 123IMP-SPECT (N = 8), and 18FDG-PET (N = 3) in DWI-High regions were significantly lower than those in the DWI-Normal regions (p < 0.001 for all); however, HCs (N = 18) showed no significant differences in ASL-CBF between the two regions. The overall agreement rate of DWI (high or normal) with ASL-CBF (hypoperfusion or normal) (81.8%) was similar to that of SPECT (85.2%) and PET (78.5%) in CJD. The regression analysis showed that the normalized ASL-CBF values significantly correlated with the normalized SPECT (r = 0.44, p < 0.001) and PET values (r = 0.46, p < 0.001) in CJD.DiscussionPatients with CJD showed ASL hypoperfusion in lesions with DWI hyperintensity, suggesting that ASL-CBF could be beneficial for the diagnostic aid of CJD.

Project description:Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD. Methods: T2-weighted images from 69 PD patients and 69 age- and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group. Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986-0.9833) and 0.7732 (95% CI: 0.6292-0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066-0.9469) and 0.7143 (95% CI: 0.5540-0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05). Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.

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Project description:PurposeThis study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.MethodsBrain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.ResultsAfter feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.ConclusionThe combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

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Fast 3 T nigral hyperintensity magnetic resonance imaging in Parkinson's disease.

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Fast 3 T nigral hyperintensity magnetic resonance imaging in Parkinson's disease.

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