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While high-resolution proton density-weighted magnetic resonance imaging (MRI) of intracranial vessel walls is significant for a precise diagnosis of intracranial artery disease, its long acquisition time is a clinical burden. Compressed sensing MRI is a prospective technology with acceleration factors that could potentially reduce the scan time. However, high acceleration factors result in degraded image quality. Although recent advances in deep-learning-based image restoration algorithms can alleviate this problem, clinical image pairs used in deep learning training typically do not align pixel-wise. Therefore, in this study, two different deep-learning-based denoising algorithms—self-supervised learning and unsupervised learning—are proposed; these algorithms are applicable to clinical datasets that are not aligned pixel-wise. The two approaches are compared quantitatively and qualitatively. Both methods produced promising results in terms of image denoising and visual grading. While the image noise and signal-to-noise ratio of self-supervised learning were superior to those of unsupervised learning, unsupervised learning was preferable over self-supervised learning in terms of radiomic feature reproducibility.

ObjectivesTo evaluate the imaging features of isocitrate dehydrogenase (IDH) mutant glioma and to assess the diagnostic performance of magnetic resonance imaging (MRI) for prediction of IDH mutation in patients with glioma.MethodsA systematic search of Ovid-MEDLINE and EMBASE up to 10 October 2017 was conducted to find relevant studies. The search terms combined synonyms for ‘glioma’, ‘IDH mutation’ and ‘MRI’. Studies evaluating the imaging features of IDH mutant glioma and the diagnostic performance of MRI for prediction of IDH mutation in patients with glioma were selected. The pooled summary estimates of sensitivity and specificity and their 95% confidence intervals (CIs) were calculated using a bivariate random-effects model. The results of multiple subgroup analyses are reported.ResultsTwenty-eight original articles in a total of 2,146 patients with glioma were included. IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high apparent diffusion coefficient (ADC) value and low relative cerebral blood volume (rCBV) value. For the meta-analysis that included 18 original articles, the summary sensitivity was 86% (95% CI, 79%–91%) and the summary specificity was 87% (95% CI, 78–92%). In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate magnetic resonance spectroscopy (MRS) [96% (95% CI, 91–100%)] was higher than the summary sensitivities of other imaging modalities.ConclusionsIDH mutant glioma consistently demonstrated less aggressive imaging features than IDH wild-type glioma. Despite the variety of different MRI techniques used, MRI showed the potential to non-invasively predict IDH mutation in patients with glioma. 2-Hydroxyglutarate MRS shows higher pooled sensitivity than other imaging modalities.Key Points• IDH mutant glioma showed frontal lobe predominance, less contrast enhancement, well-defined border, high ADC value, and low rCBV value.• The diagnostic performance of MRI for prediction of IDH mutation in patients with glioma is within a clinically acceptable range, the summary sensitivity was 86% (95% CI, 79–91%) and the summary specificity was 87% (95% CI, 78–92%).• In a subgroup analysis, the summary sensitivity of 2-hydroxyglutarate MRS [96% (95% CI, 91–100%)] was higher than the summary sensitivities of other imaging modalities.

We aimed to assess whether brain volumes may affect the results of deep brain stimulation (DBS) in patients with Parkinson’s disease (PD). Eighty-one consecutive patients with PD (male:female 40:41), treated with DBS between June 2012 and December 2017, were enrolled. Total and regional brain volumes were measured using automated brain volumetry (NeuroQuant). The Unified Parkinson Disease Rating Scale motor score quotient was used to assess changes in clinical outcome and compare the preoperative regional brain volume in patients categorized into the higher motor improvement and lower motor improvement groups based on changes in the postoperative scores. The study groups showed significant volume differences in multiple brain areas. In the higher motor improvement group, the anterior cingulate and right thalamus showed high volumes after false discovery rate (FDR) correction. In the lower motor improvement group, the left caudate, paracentral, right primary sensory and left primary motor cortex showed high volume, but no area showed high volumes after FDR correction. Our data suggest that the effectiveness of DBS in patients with PD may be affected by decreased brain volume in different areas, including the cingulate gyrus and thalamus. Preoperative volumetry could help predict outcomes in patients with PD undergoing DBS.

ObjectivesDifferentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters.MethodsA computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity.ResultsEighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84–94%) and 91% (95% CI, 84–95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94–0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84–97%) and the pooled specificity was 94% (95% CI, 85–98%).ConclusionsAlthough various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis.Key Points• Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis.• The pooled sensitivity was 90% and pooled specificity was 91%.• Peritumoral rCBV derived from DSC is a relatively well-validated.

ObjectiveTo investigate the diagnostic performance of perfusion CT for prediction of hemorrhagic transformation in acute ischemic stroke.MethodsA computerized literature search of Ovid MEDLINE and EMBASE was conducted up to October 29, 2018. Search terms included acute ischemic stroke, hemorrhagic transformation, and perfusion CT. Studies assessing the diagnostic performance of perfusion CT for prediction of hemorrhagic transformation in acute ischemic stroke were included. Two reviewers independently evaluated the eligibility of the studies. A bivariate random effects model was used to calculate the pooled sensitivity and pooled specificity. Multiple subgroup analyses were performed.ResultsFifteen original articles with a total of 1134 patients were included. High blood-brain barrier permeability and hypoperfusion status derived from perfusion CT are associated with hemorrhagic transformation. The pooled sensitivity and specificity were 84% (95% CI, 71–91%) and 74% (95% CI, 67–81%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.84 (95% CI, 0.81–0.87). The Higgins I2 statistic demonstrated that heterogeneity was present in the sensitivity (I2 = 80.21%) and specificity (I2 = 85.94%).ConclusionAlthough various perfusion CT parameters have been used across studies, the current evidence supports the use of perfusion CT to predict hemorrhagic transformation in acute ischemic stroke.Key Points• High blood-brain barrier permeability and hypoperfusion status derived from perfusion CT were associated with hemorrhagic transformation.• Perfusion CT has moderate diagnostic performance for the prediction of hemorrhagic transformation in acute ischemic stroke.• The pooled sensitivity was 84%, and the pooled specificity was 74%.

ObjectivesTo investigate whether clinical condition, imaging session, and locations affect repeatability of amide proton transfer–weighted (APTw) magnetic resonance imaging (MRI) in the brain.Materials and methodsThree APTw MRI data sets were acquired, involving two intrasession scans and one intersession scan for 19 healthy, 15 glioma, and 12 acute stroke adult participants (mean age 53.8, 54.6, and 68.5, respectively) on a 3T MR scanner. The mean APTw signals from five locations in healthy brain (supratentorial and infratentorial locations) and from entire tumor and stroke lesions (supratentorial location) were calculated. The within-subject coefficient of variation (wCV) and intraclass correlation coefficient (ICC) were calculated for each clinical conditions, image sessions, and anatomic locations. Differences in APTw signals between sessions were analyzed using repeated-measures analysis of variance.ResultsThe ICC and wCV were 0.96 (95% confidence interval [CI], 0.91–0.99) and 16.1 (12.6–21.3) in glioma, 0.93 (0.82–0.98) and 15.0 (11.4–20.6) in stroke, and 0.84 (0.72–0.91) and 34.0 (28.7–41.0) in healthy brain. There were no significant differences in APTw signal between three sessions, irrespective of disease condition and location. The ICC and wCV were 0.85 (0.68–0.94) and 27.4 (21.8–35.6) in supratentorial, and 0.44 (− 0.18 to 0.76) and 32.7 (25.9 to 42.9) in infratentorial locations. There were significant differences in APTw signal between supra- (mean, 0.49%; 95% CI, 0.38–0.61) and infratentorial locations (1.09%, 0.98–1.20; p < 0.001).ConclusionThe repeatability of APTw signal was excellent in supratentorial locations, while it was poor in infratentorial locations due to severe B0 inhomogeneity and susceptibility which affects MTR asymmetry.Key Points• In supratentorial locations, APTw MRI showed excellent intrasession and intersession repeatability in brains of healthy controls and patients with glioma, as well as in stroke-affected regions.• APTw MRI showed excellent repeatability in supratentorial locations, but poor repeatability in infratentorial locations.• Considering poor repeatability in the infratentorial locations, the use of APTw MRI in longitudinal assessment in infratentorial locations is not indicated.

ObjectiveTo evaluate the value of multiparametric MRI for determination of early treatment response following concurrent chemoradiotherapy in patients with newly diagnosed glioblastoma.MethodsA computerized search of Ovid-MEDLINE and EMBASE up to 1 October 2017 was performed to find studies on the diagnostic performance of multiparametric MRI for differentiating true progression from pseudoprogression. The beginning search date was not specified. Pooled estimates of sensitivity and specificity were obtained using hierarchical logistic regression modeling. We performed meta-regression and sensitivity analyses to explain the effects of the study heterogeneity.ResultsNine studies including 456 patients were included. Pooled sensitivity and specificity were 84 % (95 % CI 74–91) and 95 % (95 % CI 83–99), respectively. Area under the hierarchical summary receiver operating characteristic curve was 0.95 (95 % CI 0.92–0.96). Meta-regression showed true progression in the study population, the mean age and the reference standard were significant factors affecting heterogeneity.ConclusionMultiparametric MRI may be used as a potential surrogate endpoint for assessment of early treatment response, especially in the differentiation of true progression from pseudoprogression. However, based on the current evidence, monoparametric and multiparametric MRI perform equally in the clinical context. Further evaluation will be needed.Key Points• Multiparametric MRI shows high diagnostic performance for early treatment response in glioblastoma.• Multiparametric MRI could differentiate true progression from pseudoprogression in newly diagnosed glioblastoma.• The normalized rCBV derived from DSC was the most commonly used parameter.

ObjectivesTo compare the diagnostic performance of amide proton transfer (APT) imaging and 11-C methionine positron emission tomography (MET-PET) for in vivo molecular imaging of protein metabolism in post-treatment gliomas.Materials and methodsThis study included 43 patients (12 low and 31 high grade) with post-treatment gliomas who underwent both APT and MET-PET imaging within 3 weeks. APT-weighted voxel values and semi-quantitative tumour-to-normal ratios (TNR) were obtained from tumour portions. The voxel-wise relationships between TNR and APT were assessed. The diagnostic performance for recurrence of high-grade gliomas was calculated, using the area under the receiver operating characteristic curve (AUC) with maximum (TNRmax and APTmax) and 90% histogram values (TNR90 and APT90).ResultsA moderate positive correlation between TNR and APT was found in low-grade recurrences (r = 0.47, p < 0.001), but not in high-grade ones (r = −0.24, p < 0.001). For distinguishing recurrence in post-treatment high-grade gliomas, APTmax (AUC, 0.88) and APT90 (AUC, 0.78–0.83) had a similar to better diagnostic performance than TNRmax (AUC, 0.71, p = 0.08) or TNR90 (AUC, 0.53–0.59, p = 0.01–0.05).ConclusionsIn post-treatment high-grade gliomas, APT provides different regional information to MET-PET and provides higher diagnostic performance. This difference needs to be considered when using APT or MET-PET as a surrogate marker for tumour protein metabolism.Key Points• APT and TNR values in low-grade recurrence showed a moderate voxel-wise correlation.• APT and TNR demonstrated regional differences in post-treatment high-grade gliomas.• APT90 showed better diagnostic performance than TNR90 in high-grade recurrence.

From May 2015 to June 2016, data on 296 patients undergoing 1.5-Tesla MRI for symptoms of acute ischemic stroke were retrospectively collected. Conventional, echo-planar imaging (EPI) and echo train length (ETL)-T2-FLAIR were simultaneously obtained in 118 patients (first group), and conventional, ETL-, and repetition time (TR)-T2-FLAIR were simultaneously obtained in 178 patients (second group). A total of 595 radiomics features were extracted from one region-of-interest (ROI) reflecting the acute and chronic ischemic hyperintensity, and concordance correlation coefficients (CCC) of the radiomics features were calculated between the fast scanned and conventional T2-FLAIR for paired patients (1st group and 2nd group). Stabilities of the radiomics features were compared with the proportions of features with a CCC higher than 0.85, which were considered to be stable in the fast scanned T2-FLAIR. EPI-T2-FLAIR showed higher proportions of stable features than ETL-T2-FLAIR, and TR-T2-FLAIR also showed higher proportions of stable features than ETL-T2-FLAIR, both in acute and chronic ischemic hyperintensities of whole- and intersection masks (p < .002). Radiomics features in fast scanned T2-FLAIR showed variable stabilities according to the sequences compared with conventional T2-FLAIR. Therefore, radiomics features may be used cautiously in applications for feature analysis as their stability and robustness can be variable.

Purpose Grading of brain gliomas is of clinical importance, and noninvasive molecular imaging may help differentiate low- and high-grade gliomas. We aimed to evaluate the diagnostic performance of amide proton transfer-weighted (APTw) MRI for differentiating low- and high-grade gliomas on 3-T scanners. Methods A systematic literature search of Ovid-MEDLINE and EMBASE was performed up to March 28, 2018. Original articles evaluating the diagnostic performance of APTw MRI for differentiating low- and high-grade gliomas were selected. The pooled sensitivity and specificity were calculated using a bivariate random-effects model. A coupled forest plot and a hierarchical summary receiver operating characteristic curve were obtained. Heterogeneity was investigated using Higgins inconsistency index ( I 2 ) test. Meta-regression was performed. Results Ten original articles with a total of 353 patients were included. High-grade gliomas showed significantly higher APT signal intensity than low-grade gliomas. The pooled sensitivity and specificity for the diagnostic performance of APTw MRI for differentiating low-grade and high-grade gliomas were 88% (95% CI, 77–94%) and 91% (95% CI, 82–96%), respectively. Higgins I 2 statistic demonstrated heterogeneity in the sensitivity ( I 2  = 68.17%), whereas no heterogeneity was noted in the specificity ( I 2  = 44.84%). In meta-regression, RF saturation power was associated with study heterogeneity. Correlation coefficients between APT signal intensity and Ki-67 cellular proliferation index ranged from 0.430 to 0.597, indicating moderate correlation. All studies showed excellent interobserver agreement. Conclusions Although heterogeneous protocols were used, APTw MRI demonstrated excellent diagnostic performance for differentiating low- and high-grade gliomas. APTw MRI could be a reliable technique for glioma grading in clinical practice.