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Our study was intended to demonstrate the different signal intensity (SI) pattern of the putamen seen on susceptibility-weighted imaging (SWI) between that of Parkinson’s disease (PD) and Parkinsonism-predominant multiple system atrophy (MSA-P), and to correlate it with 18 F-flurodeoxyglucose positron-emission tomography ( 18 F-FDG PET). Thirty patients with PD and 17 with MSA-P underwent SWI, and 18 F-FDG PET were included. The SI was measured on SWI in the anterior and posterior halves of the putamen using a region-of-interest (ROI) on both sides. The normalized regional glucose metabolism (standardized uptake value ratio, SUVR) was measured on co-registered 18 F-FDG PET images using the ROI obtained with SWI. Analysis included a group-level comparison of the SI values obtained on SWI, and these results were correlated with the SUVR on 18 F-FDG PET. The SIs of the bilateral posterior, dominant-side of the posterior, mean values of the bilateral anterior and posterior halves of the putamen on SWI, differed significantly between the two groups ( P < 0.001, respectively). The SUVR of the all locations also differed significantly between PD and MSA-P ( P < 0.001, respectively). There was a moderate degree of positive correlation between the SI and the SUVR of the left posterior half, and mean value of the bilateral posterior putamen in MSA-P ( r = 0.634, P = 0.006, r = 0.492, P = 0.045). In conclusion, the low SI seen on the posterior putamen may differentiate MSA-P from PD. Furthermore, low SI in the putamen correlated with hypometabolism on 18 F-FDG PET. Therefore, SWI could be a potential complementary diagnostic tool to 18 F-FDG PET for differentiating these conditions. [ABSTRACT FROM AUTHOR]

We aimed to investigate the detection rate of brain MR and MR angiography for neuroimaging abnormality in newly diagnosed left-sided infective endocarditis patients with/without neurological symptoms. This retrospective study included consecutive patients with definite or possible left-sided infective endocarditis according to the modified Duke criteria who underwent brain MRI and MR angiography between March 2015 and October 2020. The detection rate for neuroimaging abnormality on MRI was defined as the number of patients with positive brain MRI findings divided by the number of patients with left-sided infective endocarditis. Positive imaging findings included acute ischemic lesions, cerebral microbleeds, hemorrhagic lesions, and infectious aneurysms. In addition, aneurysm rupture rate and median period to aneurysm rupture were evaluated on follow-up studies. A total 115 patients (mean age: 55 years ± 19; 65 men) were included. The detection rate for neuroimaging abnormality was 77% (89/115). The detection rate in patients without neurological symptoms was 70% (56/80). Acute ischemic lesions, cerebral microbleeds, and hemorrhagic lesions including superficial siderosis and intracranial hemorrhage were detected on MRI in 56% (64/115), 57% (66/115), and 20% (23/115) of patients, respectively. In particular, infectious aneurysms were detected on MR angiography in 3% of patients (4/115), but MR angiography in 5 patients (4.3%) was insignificant for infectious aneurysm, which were detected using CT angiography (n = 3) and digital subtraction angiography (n = 2) during follow-up. Among the 9 infectious aneurysm patients, aneurysm rupture occurred in 4 (44%), with a median period of aneurysm rupture of 5 days. The detection rate of brain MRI for neuroimaging abnormality in newly diagnosed left-sided infective endocarditis patients was high (77%), even without neurological symptoms (70%). [ABSTRACT FROM AUTHOR]

Objective: There is a paucity of large cohort-based evidence regarding the need and added value of diffusion-weighted imaging (DWI) in patients attending outpatient clinic for cognitive impairment. We aimed to evaluate the diagnostic yield of DWI in patients attending outpatient clinic for cognitive impairment. Materials and methods: This retrospective, observational, single-institution study included 3,298 consecutive patients (mean age ± SD, 71 years ± 10; 1,976 women) attending outpatient clinic for cognitive impairment with clinical dementia rating ≥ 0.5 who underwent brain MRI with DWI from January 2010 to February 2020. Diagnostic yield was defined as the proportion of patients in whom DWI supported the diagnosis that underlies cognitive impairment among all patients. Subgroup analyses were performed by age group and sex, and the Chi-square test was performed to compare the diagnostic yields between groups. Results: The overall diagnostic yield of DWI in patients with cognitive impairment was 3.2% (106/3,298; 95% CI, 2.6–3.9%). The diagnostic yield was 2.5% (83/3,298) for acute or subacute infarct, which included recent small subcortical infarct for which the diagnostic yield was 1.6% (54/3,298). The diagnostic yield was 0.33% (11/3,298) for Creutzfeldt-Jakob disease (CJD), 0.15% (5/3,298) for transient global amnesia (TGA), 0.12% (4/3,298) for encephalitis and 0.09% (3/3,298) for lymphoma. There was a trend towards a higher diagnostic yield in the older age group with age ≥ 70 years old (3.6% vs 2.6%, P =.12). There was an incremental increase in the diagnostic yield from the age group 60–69 years (2.6%; 20/773) to 90–99 years (8.0%; 2/25). Conclusion: Despite its low overall diagnostic yield, DWI supported the diagnosis of acute or subacute infarct, CJD, TGA, encephalitis and lymphoma that underlie cognitive impairment, and there was a trend towards a higher diagnostic yield in the older age group. [ABSTRACT FROM AUTHOR]

Although advanced magnetic resonance imaging (MRI) techniques provide useful information for the differential diagnosis of intra-axial mass-like lesions, the specific diagnostic role of multimodal MRI over conventional magnetic resonance imaging (CMRI) alone in the differential diagnosis of mass-like lesions from a large heterogeneous cohort has not been studied. In this study, we aimed to determine the added value of a joint approach of diffusion-weighted imaging (DWI) and dynamic-susceptibility-contrast perfusion imaging (DSC-PWI) for diagnosis of intra-axial mass-like lesions, comparing them with CMRI alone. Furthermore, we performed these evaluations in a manner simulating clinical practice. Our institutional review board approved this retrospective study and waived the requirement for informed consent. A total of 1038 patients with intra-axial mass-like lesions were retrospectively recruited according to their histological and clinico-radiological diagnoses made between January 2005 and December 2014. All patients underwent CMRI, DWI and DSC-PWI. The diagnostic accuracy and confidence in diagnosing each type of intra-axial mass-like lesions, and for differentiating the intra-axial brain tumors from non-neoplastic lesions, were compared according to the MRI protocols. The disease-specific sensitivity of joint approach differed according to specific disease entities in diagnosing each disease category. Joint approach provided the best diagnostic accuracy for discriminating intra-axial brain tumors from non-neoplastic lesions, with high diagnostic accuracy (95.3–96.7%), specificity (82–84.0%), positive-predictive-value (97.0–97.3%), and negative-predictive-value (84.8–92.7%), with the reader’s confidence values being significantly improved over those on CMRI alone (all p-values < 0.001). In conclusion, joint approach of DWI, DSC-PWI to CMRI helps to differentiate non-neoplastic lesions from intra-axial brain tumors, and improves diagnostic confidence compared with CMRI alone. The benefit from the combined imaging differs for each disease category; thus joint approach needs to be customized according to clinical suspicion. [ABSTRACT FROM AUTHOR]

Purpose: Distinguishing non-neoplastic pituitary stalk lesions (non-NPSLs) from neoplastic pituitary stalk lesions (NPSLs) is a major concern in guiding treatment for a thickened pituitary stalk. Our study aimed to aid provide preoperative diagnostic assistance by combining clinical and magnetic resonance imaging (MRI) findings to distinguish non-NPSLs from NPSLs. Materials and methods: We recruited 158 patients with thickened pituitary stalk lesions visible on MRI. Laboratory findings included hypopituitarism, diabetes insipidus (DI), and hyperprolactinemia. MR images were assessed for anterior–posterior thickness (mm), diffuse pituitary stalk thickening, cystic changes, a high T1 signal, and glandular or extrasellar involvement. A diagnostic model was developed using a recursive partitioning logistic regression analysis. The model was validated in an independent dataset comprising 63 patients, and its diagnostic performance was compared with that of the original radiological reports. Results: A univariate analysis found significant associations of DI (P = 0.006), absence of extrasellar involvement (P = 0.002), and lower stalk thickness (P = 0.031) with non-NPSLs. A diagnostic model was created using the following parameters (in order of priority): 1) lack of extrasellar involvement, 2) stalk thickness < 5.3 mm, and 3) presence of DI. The diagnostic performance (area under the curve; AUC) of this model in the independent set was 0.813, representing a significant improvement over the original radiological reports (AUC: 0.713, P = 0.029). Conclusion: The joint diagnostic approach based on clinical and imaging-based factors robustly distinguished non-NPSLs from NPSLs. This approach could guide treatment strategies and prevent unnecessary surgery in patients with non-NPSL. [ABSTRACT FROM AUTHOR]

Background and Purpose: Quantitative measurements, as well as qualitative characterizations, of the vessel walls of the small intracranial arteries became clinically available and reliable beyond the resolution limit of 1.5 T high-resolution magnetic resonance imaging (HR-MRI) with the development of 3 T HR-MRI. We present the quantitative dissection findings of spontaneous and unruptured acute intracranial artery dissection (SID) using 3 T HR-MRI and investigate the differences between each cerebral artery.Methods: Twenty-eight lesions (anterior cerebral artery = 6; middle cerebral artery = 4; vertebral artery = 18) from 26 patients (17 male and 9 female patients; mean age = 47 years; age range = 32-74 years) with presumptive diagnoses of SID were included. The diagnosis was determined based on the clinical features, findings on luminal angiography (such as digital subtraction angiography, computed tomography, or magnetic resonance angiography), and HR-MRI. HR-MRI was performed within one month from onset. The neuroimaging indices (maximal outer diameter and area, remodeling index and modified remodeling index, and wall thickness and wall area index) of aneurysmal dilatation and the signal intensities of the intramural hematomas were rated using HR-MRI. The results were compared between each cerebral artery.Results: The maximal outer diameter and area, remodeling index and modified remodeling index, and wall thickness index and length were significantly different between anterior and posterior circulation (P < .05). The mean relative signal intensities of the intramural hematoma showed consistent values, regardless of the cerebral arteries, without significant difference.Conclusions: Neuroimaging indices of aneurysmal dilatation may be adjunctive indicators in the evaluation of SID. [ABSTRACT FROM AUTHOR]

Abstract: Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images may cause misalignments, particularly in brain PET and CT images that have low correspondence rates between features due to differences in image characteristics. To cope with this limitation, we propose a robust feature-based registration technique using a Gaussian-weighted distance map (GWDM) that finds the best alignment of feature points even when features of two images are mismatched. A GWDM is generated by propagating the value of the Gaussian-weighted mask from feature points of CT images and leads the feature points of PET images to be aligned on an optimal location even though there is a localization error between feature points extracted from PET and CT images. Feature points are extracted from two images by our automatic brain segmentation method. In our experiments, simulated and clinical data sets were used to compare our method with conventional methods such as normalized mutual information (NMI)-based registration and chamfer matching in accuracy, robustness, and computational time. Experimental results showed that our method aligned the images robustly even in cases where conventional methods failed to find optimal locations. In addition, the accuracy of our method was comparable to that of the NMI-based registration method. [Copyright &y& Elsevier]

Comprehensive speech/language studies revealed mild dysarthria with limited lateral tongue movement and competent language ability except mild difficulty in repeating sentences. The data of fMRI showed the preponderant involvement of the right frontal in sentence completion and bilateral temporal areas in verb generation task (Fig. The arrows indicate the atypical areas activated during the language tasks: the right frontal on sentence completion task and bilateral temporal areas on verb generation task. [Extracted from the article]