Imaging patterns of cerebral ischemia in hypereosinophilic syndrome: case report and systematic review.

INTRODUCTION: Ischemic stroke is a potential complication of hypereosinophilic syndromes (HES), and little is known about underlying pathophysiological mechanisms. We aimed to describe the imaging patterns of cerebral ischemia in patients with HES. METHODS: An individual case is reported. A systematic PubMed review of all records reporting adult patients with HES who suffered ischemic stroke and for whom neuroimaging details of ischemic lesions were available was performed. RESULTS: A 60-year-old man presented with progressive subacute gait difficulty and psychomotor slowing as well as an absolute eosinophilia (2.2 × 109/L) at admission. Brain magnetic resonance tomography revealed multiple acute and subacute internal and external border zone infarcts. Cardiac diagnostic suggested the presence of endomyocarditis. After extensive diagnostic workup, idiopathic HES was diagnosed. The systematic review yielded 183 studies, of which 40 fulfilled the inclusion criteria: a total of 64 patients (31.3% female), with mean age 51.1 years and a median absolute eosinophile count at diagnosis of 10.2 × 109/L were included in the analyses. A border zone pattern of cerebral ischemic lesions was reported in 41 patients (64.1%). Isolated peripheral infarcts were reported in 7 patients (10.9%). Sixteen patients had multiple acute infarcts with no border zone distribution (25.0%). An intracardiac thrombus was reported in 15/60 patients (25%), and findings suggestive of endomyocarditis or endomyocardial fibrosis were found in 31/60 patients (51.7%). CONCLUSIONS: Border zone distribution of cerebral ischemia without hemodynamic compromise is the most frequent imaging pattern in patients with HES, occurring in 2/3 of patients who develop ischemic stroke.

Magnetic resonance spectroscopy with transcranial direct current stimulation to explore the underlying biochemical and physiological mechanism of the human brain: A systematic review.

A large body of molecular and neurophysiological evidence connects synaptic plasticity to specific functions and energy metabolism in particular areas of the brain. Furthermore, altered plasticity and energy regulation has been associated with a number of neuropsychiatric disorders. A favourable approach enabling the modulation of neuronal excitability and energy in humans is to stimulate the brain using transcranial direct current stimulation (tDCS) and then to observe the effect on neurometabolites using magnetic resonance spectroscopy (MRS). In this way, a well-defined modulation of brain energy and excitability can be achieved using a dedicated tDCS protocol to a predetermined brain region. This systematic review was guided by the preferred reporting items for systematic reviews and meta-analysis and summarises recent literature studying the effect of tDCS on neurometabolites in the human brain as measured by proton or phosphorus MRS. Limitations and recommendations are discussed for future research. The findings of this review provide clear evidence for the potential of using tDCS and MRS to examine and understand the effect of neurometabolites in the in vivo human brain.

Increasing body mass index in an elderly cohort: Effects on the quantitative MR parameters of the brain.

BACKGROUND: Body mass index (BMI) is increasing in a large number of elderly persons. This increase in BMI is known to put one at risk for many "diseases of aging," although less is known about how a change in BMI may affect the brains of the elderly. PURPOSE: To investigate the relationship between BMI and quantitative water content, T1 , T2 *, and the semi-quantitative magnetization transfer ratio (MTR) of various structures in elderly brains. STUDY TYPE: Cross-sectional. SUBJECTS: Forty-two adults (BMI range: 19.1-33.5 kg/m2 , age range: 58-80 years). FIELD STRENGTH: 3T MRI (two multi-echo gradient echoes, actual flip angle imaging, magnetization prepared rapid gradient echo, fluid attenuated inversion recovery). ASSESSMENT: The 3D two-point method was used to derive (semi-)quantitative parameters in global white (WM) and gray matter (GM) and their regions as defined by the Johns Hopkins University and the Montreal Neurological Institute atlases. STATISTICAL TESTS: Multivariate linear regression with BMI as principal regressor, corrected for the additional regressors age, gender, and glycated hemoglobin. Spearman correlation between quantitative parameters of the regions showing significant changes and the lipid spectra / C-reactive protein (CRP). Voxel-based morphometry and analysis of covariance (ANCOVA) to explore changes in the GM volume. RESULTS: T1 increased significantly (P < 0.05) in the frontal, temporal, and parietal cortices, while the bilateral corona radiata, right superior longitudinal fasciculus, as well as the corpus callosum showed significant changes in the WM regions. T2 * increased significantly in the global WM and left corona radiata. Changes in MTR and the free water content did not reach significance. No significant correlation between any quantitative parameter and the lipid spectra or CRP could be identified. DATA CONCLUSION: These results suggest that an elevated BMI predominantly affects T1 in WM as well as GM structures in the elderly human brain. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:514-523.

Quantitative MRI of cerebral white matter hyperintensities: A new approach towards understanding the underlying pathology.

Interest in white matter hyperintensities (WMH), a radiological biomarker of small vessel disease, is continuously increasing. This is, in most part, due to our better understanding of their association with various clinical disorders, such as stroke and Alzheimer's disease, and the overlapping pathology of WMH with these afflictions. Although post-mortem histological studies have reported various underlying pathophysiological substrates, in vivo research has not been specific enough to fully corroborate these findings. Furthermore, post-mortem studies are not able to capture which pathological processes are the driving force of the WMH severity. The current study attempts to fill this gap by non-invasively investigating the influence of WMH on brain tissue using quantitative MRI (qMRI) measurements of the water content (H2O), the longitudinal (T1) and effective transverse relaxation times (T2∗), as well as the semi-quantitative magnetization transfer ratio (MTR), and bound proton fraction (ƒbound). In total, seventy subjects (age range 50-80 years) were selected from a population-based aging cohort study, 1000BRAINS. Normal appearing grey (NAGM) and white matter (NAWM), as well as deep (DWMH) and periventricular (PWMH) white matter hyperintensities, were segmented and characterized in terms of their quantitative properties. The subjects were then further divided into four grades according to the Fazekas rating scale of severity. Groupwise analyses of the qMRI values in each tissue class were performed. All five qMRI parameters showed significant differences between WMH and NAWM (p < 0.001). Importantly, the parameters differed between DWMH and PWMH, the latter having higher H2O, T1, T2∗ and lower MTR and ƒbound values (p < 0.001). Following grading according to the Fazekas scale, DWMH showed an increase in the water content, T1 and a decrease in bound proton fraction corresponding to severity, exhibiting significant changes in grade 3 (p < 0.001), while NAWM revealed significantly higher H2O values in grade 3 compared to grade 0 (p < 0.001). PWMH demonstrated an increase in T2∗ values (significant in grade 3, P < 0.001). These results are in agreement with previous histopathological studies and support the interpretation that both edema and myelin loss due to a possible breakdown of the blood-brain barrier and inflammation are the major pathological substrates turning white matter into DWMH. Edema being an earlier contributing factor to the pathology, as expressed in the elevated water content values in NAWM with increasing severity. In the case of PWMH, an altered fluid dynamic and cerebrospinal fluid leakage exacerbate the changes. It was also found that the pathology, as monitored by qMRI, evolves faster in DWMH than in the PWMH following the severity.

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T.

PURPOSE: Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge. METHODS: The effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests. RESULTS: The simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI). CONCLUSION: ADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

A robust method for the detection of small changes in relaxation parameters and free water content in the vicinity of the substantia nigra in Parkinson's disease patients.

Alterations in the substantia nigra are strongly associated with Parkinson's disease. However, due to low contrast and partial volume effects present in typical MRI images, the substantia nigra is not of sufficient size to obtain a reliable segmentation for region-of-interest based analysis. To combat this problem, the approach proposed here offers a method to investigate and reveal changes in quantitative MRI parameters in the vicinity of substantia nigra without any a priori delineation. This approach uses an alternative method of statistical, voxel-based analysis of quantitative maps and was tested on 18 patients and 15 healthy controls using a well-established, quantitative free water mapping protocol. It was possible to reveal the topology and the location of pathological changes in the substantia nigra and its vicinity. Moreover, a decrease in free water content, T1 and T2* in the vicinity of substantia nigra was indicated in the Parkinson's disease patients compared to the healthy controls. These findings reflect a disruption of grey matter and iron accumulation, which is known to lead to neurodegeneration. Consequently, the proposed method demonstrates an increased sensitivity for the detection of pathological changes-even in small regions-and can facilitate disease monitoring via quantitative MR parameters.

Seeing faces, when faces can't be seen: Wearing portrait photos has a positive effect on how patients perceive medical staff when face masks have to be worn.

INTRODUCTION: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, wearing surgical face masks has become mandatory for healthcare staff in many countries when interacting with patients. Recently, it has been shown that wearing face masks impairs social interaction by diminishing a person's ability to read the emotion of their counterparts, an essential prerequisite to respond adequately in social situations. It is easily conceivable that this may have a tangible negative influence on the communication and relationship between patients and healthcare personnel. We therefore investigated whether it has an effect on how patients perceive healthcare professionals when physicians and nursing staff wear portrait photos with their smiling faces in addition to face masks. METHODS: During the study period of 16 days, the medical staff of our Department wore surgical face masks at all times during any kind of interaction with patients. In a pseudorandomized order, all members of our staff additionally affixed their portrait photos to their work clothes on 8 of the 16 days. After completion of their visit, 226 patients were interviewed anonymously in a cross-sectional study design using a questionnaire in which they rated the following three items: friendliness of staff, medical quality of treatment, and how well they felt taken care of during treatment in our Department. RESULTS: On days, on which staff wore photos, mean scores of the questionnaires were significantly higher than on non-photo days (p = 0.013; mean ± standard deviation = 92.8 ± 11.3 vs. 91.0 ± 12.6; median (range) = 97 (98) vs. 96 (76)). When analyzed separately, the increased scores were only significant for the item friendliness of staff (p = 0.009; mean ± standard deviation = 95.8 ± 6.3 vs. 92.2 ± 11.5; median (range) = 98 (39) vs. 97 (54)). CONCLUSION: Our study suggests that the use of portrait photos with smiling faces has a positive effect on how patients perceive healthcare staff.

Multi-Exponential Relaxometry Using l1 -Regularized Iterative NNLS (MERLIN) With Application to Myelin Water Fraction Imaging.

A new parameter estimation algorithm, MERLIN, is presented for accurate and robust multi-exponential relaxometry using magnetic resonance imaging, a tool that can provide valuable insight into the tissue microstructure of the brain. Multi-exponential relaxometry is used to analyze the myelin water fraction and can help to detect related diseases. However, the underlying problem is ill-conditioned, and as such, is extremely sensitive to noise and measurement imperfections, which can lead to less precise and more biased parameter estimates. MERLIN is a fully automated, multi-voxel approach that incorporates state-of-the-art l1 -regularization to enforce sparsity and spatial consistency of the estimated distributions. The proposed method is validated in simulations and in vivo experiments, using a multi-echo gradient-echo (MEGE) sequence at 3 T. MERLIN is compared to the conventional single-voxel l2 -regularized NNLS (rNNLS) and a multi-voxel extension with spatial priors (rNNLS + SP), where it consistently showed lower root mean squared errors of up to 70 percent for all parameters of interest in these simulations.

A 3D two-point method for whole-brain water content and relaxation time mapping: Comparison with gold standard methods.

Quantitative imaging of the human brain is of great interest in clinical research as it enables the identification of a range of MR biomarkers useful in diagnosis, treatment and prognosis of a wide spectrum of diseases. Here, a 3D two-point method for water content and relaxation time mapping is presented and compared to established gold standard methods. The method determines free water content, H2O, and the longitudinal relaxation time, T1, quantitatively from a two-point fit to the signal equation including corrections of the transmit and receive fields. In addition, the effective transverse relaxation time, T2*, is obtained from an exponential fit to the multi-echo signal train and its influence on H2O values is corrected. The phantom results obtained with the proposed method show good agreement for H2O and T1 values with known and spectroscopically measured values, respectively. The method is compared in vivo to already established gold standard quantitative methods. For H2O and T2* mapping, the 3D two-point results were compared to a measurement conducted with a multiple-echo GRE with long TR and T1 is compared to results from a Look-Locker method, TAPIR. In vivo results show good overall agreement between the methods, but some systematic deviations are present. Besides an expected dependence of T2* on voxel size, T1 values are systematically larger in the 3D approach than those obtained with the gold standard method. This behaviour might be due to imperfect spoiling, influencing each method differently. Results for H2O differ due to differences in the saturation of cerebrospinal fluid and partial volume effects. In addition, ground truth values of in vivo studies are unknown, even when comparing to in vivo gold standard methods. A detailed region-of-interest analysis for H2O and T1 matches well published literature values.