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BACKGROUND: Eclampsia, a serious pregnancy complication, is associated with cerebral edema and infarctions but the underlying pathophysiology remains largely unexplored.

OBJECTIVES: To assess the pathophysiology of eclampsia using specialized magnetic resonance imaging that measures diffusion, perfusion, and vasospasm.

STUDY DESIGN: This was a cross-sectional study recruiting consecutive pregnant women between April 2018 to November 2021 at Tygerberg Hospital, Cape Town, South Africa. We recruited women with eclampsia, preeclampsia, and normotensive pregnancies who underwent magnetic resonance imaging after birth. Main outcome measures were cerebral infarcts, edema, and perfusion using intravoxel incoherent motion imaging and vasospasm using magnetic resonance imaging angiography. The imaging protocol was established before inclusion.

RESULTS: Forty-nine women with eclampsia, 20 with preeclampsia and 10 normotensive women were included. Cerebral infarcts were identified in 34% of eclamptic, 5% of preeclamptic (risk difference (RD) 0.29; 95% confidence interval (CI) 0.06 to 0.52, p=0.012) and in no normotensive controls. Eclamptic women were more likely to have vasogenic cerebral edema compared to preeclamptic (80% vs 20%, RD 0.60; CI 0.34 to 0.85, p<.001) and normotensive women (RD 0.80; CI 0.47 to 1.00, p<.001). Diffusion was increased in eclampsia in the parietooccipital white matter (mean difference (MD) 0.02 x10^-3 mm²/s, CI 0.00 to 0.05, p=0.045) and the caudate nucleus (MD 0.02 x10^-3 mm²/s, CI 0.00 to 0.04, p=0.033) when compared to preeclamptic women. Diffusion was also increased in eclamptic women in the frontal (MD 0.07 x10^-3 mm²/s, CI 0.02 to 0.12, p=0.012) and parietooccipital white matter (MD 0.05 x10^-3 mm²/s, CI 0.02 to 0.07, p=0.03) and the caudate nucleus (MD 0.04 x10^-3 mm²/s, CI 0.00 to 0.07, p=0.028) when compared to normotensive women. Perfusion was decreased in edematous regions. Hypoperfusion was present in the caudate nucleus in eclampsia (MD -0.17 x10^-3 mm²/s, CI -0.27 to -0.06, p=0.003) when compared to preeclampsia. There were no signs of hyperperfusion. Vasospasm was present in 18% of eclamptic, 6% of preeclamptic and none of the controls.

CONCLUSIONS: Eclampsia is associated with cerebral infarcts, vasogenic cerebral edema, vasospasm and decreased perfusion, all not usually evident on standard clinical imaging. This may explain why some have cerebral symptoms and signs despite having normal conventional imaging.

Background

Premenstrual dysphoric disorder (PMDD) is a depressive disorder triggered by fluctuations of progesterone and estradiol during the luteal phase of the menstrual cycle. Selective progesterone receptor modulation (SPRM), while exerting an antagonistic effect on progesterone and maintaining estradiol on moderate levels, has shown beneficial effects on the mental symptoms of PMDD. Progesterone is also known for its neuroprotective effects, while synthetic progestins have been suggested to promote myelination. However, the impact of SPRM treatment on white matter microstructure is unexplored.

Methods

Diffusion tensor imaging was employed to collect data on white matter integrity in patients with PMDD, before and after treatment with ulipristal acetate (an SPRM) or placebo, as part of a double-blind randomized controlled-trial. Tract based spatial statistics were performed to investigate SPRM treatment vs. placebo longitudinal effects on fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), on the whole white matter skeleton.

Results

Voxel-wise analyses indicated no change over time in any white matter microstructure metrics in individuals treated with SPRM versus placebo. Improvement in PMDD symptoms did not correlate with changes in white matter microstructure. In secondary, exploratory, cross-sectional comparisons during treatment, the SPRM group displayed lower FA and higher MD, RD, and AD than the placebo group in several tracts.

Conclusion

The main findings suggest that SPRM treatment did not impact white matter microstructure compared with placebo. However, secondary exploratory analyses yielded between-group differences after treatment, which call for further investigation on the tracts potentially impacted by progesterone antagonism.

Clinical trial registration

EUDRA-CT 2016–001719-19; “Selective progesterone receptor modulators for treatment of premenstrual dysphoric disorder. A randomized, double-blind, placebo-controlled study.”; https://www.clinicaltrialsregister.eu/ctr-search/trial/2016-001719-19/SE

Background: Paired cerebral blood flow (CBF) measurement is usually acquired before and after vasoactive stimulus to estimate cerebrovascular reserve (CVR). However, CVR may be confounded because of variations in time-to-maximum CBF response (t_max) following acetazolamide injection. With a mathematical model, CVR can be calculated insensitive to variations in t_max, and a model offers the possibility to calculate additional model-derived parameters. A model that describes the temporal CBF response following a vasodilating acetazolamide injection is proposed and evaluated.

Methods: A bi-exponential model was adopted and fitted to four CBF measurements acquired using arterial spin labelling before and initialised at 5, 15 and 25 min after acetazolamide injection in a total of fifteen patients with Moyamoya disease. Curve fitting was performed using a non-linear least squares method with a priori constraints based on simulations.

Results: Goodness of fit (mean absolute error) varied between 0.30 and 0.62 ml·100 g^-1·min^-1. Model-derived CVR was significantly higher compared to static CVR measures. Maximum CBF increase occurred earlier in healthy- compared to diseased vascular regions.

Conclusions: The proposed mathematical model offers the possibility to calculate CVR insensitive to variations in time to maximum CBF response which gives a more detailed characterisation of CVR compared to static CVR measures. Although the mathematical model adapts generally well to this dataset of patients with MMD it should be considered as experimental; hence, further studies in healthy populations and other patient cohorts are warranted.

A 30-year-old man presented with minute-long episodes of vertigo and severe autophony. CVEMP showed a decreased threshold when testing the left side, potentially indicating SSCD. A subsequent MRI demonstrated a multi-lobulated, cystic mass in the temporal bone and the radiological diagnosis at that time was ELST. Tumor excision was performed, and microscopic examination of the excised material revealed fibrovascular tissue without signs of papillary or cystic projections. The conclusion of the histological assessment rendered a diagnosis of angiofibroma. We were unable to find a previous report of ENA originating around the endolymphatic sac.

Volumetric quantification of tumors is usually done manually by radiologists requiring precious medical time and suffering from inter-observer variability. An automatic tool for accurate volume quantification of post-operative glioblastoma would reduce the workload of radiologists and improve the quality of follow-up monitoring and patient care. This paper deals with the 3-D segmentation of post-operative glioblastoma using channel squeeze and excitation based attention gated network (ASE-Net). The proposed deep neural network has a 3-D encoder and decoder based architecture with channel squeeze and excitation (CSE) blocks and attention blocks. The CSE block reduces the dependency on space information and put more emphasize on the channel information. The attention block suppresses the feature maps of irrelevant background and helps highlighting the relevant feature maps. The Uppsala university data set used has post-operative follow-up MRI scans for fifteen patients. A patient specific fine-tuning approach is used to improve the segmentation results for each patient. ASE-Net is also cross-validated with BraTS-2021 data set. The mean dice score of five-fold cross validation results with BraTS-2021 data set for enhanced tumor is 0.8244. The proposed network outperforms the competing networks like U-Net, Attention U-Net and Res U-Net. On the Uppsala University glioblastoma data set, the mean Dice score obtained with the proposed network is 0.7084, Hausdorff Distance-95 is 7.14 and the mean volumetric similarity achieved is 0.8579. With fine-tuning the pre-trained network, the mean dice score improved to 0.7368, Hausdorff Distance-95 decreased to 6.10 and volumetric similarity improved to 0.8736. ASE-Net outperforms the competing networks and can be used for volumetric quantification of post-operative glioblastoma from follow-up MRI scans. The network significantly reduces the probability of over segmentation.

Precise localization and volumetric segmentation of glioblastoma before and after surgery are crucial for various clinical purposes, including post-surgery treatment planning, monitoring tumour recurrence, and creating radiotherapy maps. Manual delineation is time-consuming and prone to errors, hence the adoption of automated 3D quantification methods using deep learning algorithms from MRI scans in recent times. However, automated segmentation often leads to over-segmentation or under-segmentation of tumour regions. Introducing an interactive deep-learning tool would empower radiologists to rectify these inaccuracies by adjusting the over-segmented and under-segmented voxels as needed. This paper proposes a network named Atten-SEVNETR, that has a combined architecture of vision transformers and convolutional neural networks (CNN). This hybrid architecture helps to learn the input volume representation in sequences and focuses on the global multi-scale information. An interactive graphical user interface is also developed where the initial 3D segmentation of glioblastoma can be interactively corrected to remove falsely detected spurious tumour regions. Atten-SEVNETR is trained on BraTS training dataset and tested on BraTS validation dataset and on Uppsala University post-operative glioblastoma dataset. The methodology outperformed state-of-the-art networks like nnFormer, SwinUNet, and SwinUNETR. The mean dice score achieved is 0.7302, and the mean Hausdorff distance-95 got is 7.78 mm for the Uppsala University dataset.

Purpose

Very preterm birth increases risk for neonatal white matter injury, but there is limited data on to what extent this persists into adolescence and how this relates to ophthalmological outcomes. The aim of this study was to assess brain MRI findings in 12-year-old children born very preterm compared to controls and their association with concurrent ophthalmological outcomes.

Methods

We included 47 children born very preterm and 22 full-term controls (gestational age <32 and >37 weeks, respectively). Brain MRI findings were studied in association with concurrent ophthalmological outcomes at 12-year follow-up.

Results

Evans index (0.27 vs 0.25, p<0.001) and a proposed “posterior ventricle index” (0.47 vs 0.45, p=0.018) were increased in children born very preterm. Higher gestational age associated with larger corpus callosum area (β=10.7, 95%CI 0.59–20.8). Focal white matter lesions were observed in 15 (32%) of very preterm children and in 1 (5%) of full-term controls. Increased posterior ventricle index increased risk for visual acuity ≤1.0 (OR=1.07×10¹¹, 95%CI=7.78–1.48×10²¹) and contrast sensitivity <0.5 (OR=2.6×10²⁷, 95%CI=1.9×10⁸–3.5×10⁴⁶). Decreased peritrigonal white matter thickness associated with impaired visual acuity (β=0.04, 95%CI 0.002–0.07).

Conclusion

More white matter lesions and evidence of lower white matter volume were found in children born very preterm compared with full-term controls at 12-year follow-up. The association between larger posterior ventricle index and reduced visual acuity and contrast sensitivity suggests disturbances of the posterior visual pathway due to diffuse white matter lesions.

Accurate delineation of residual tumor tissue from post-surgical MR images is crucial for assessing the prognosis of patients with glioblastoma, with the extent of surgical removal being a key prognostic factor. Though nearly accurate post-surgical residual tumor segmentation can be achieved with deep neural architectures, interactive refinement can improve the segmentation further. In this study, we implemented the novel network named Class Attention Augmented Transformers (CAAT) for quantifying the post-operative residual enhanced brain tumors. The 3D segmentation output from the post-operative baseline scan was further corrected with level-set method to reduce the over-segmentation and under-segmentation. The dataset comprises the post-operative glioblastoma data from Uppsala University Hospital, encompassing the baseline and follow-up MRI for each patient. The corrected baseline scan was used to fine-tune the network further, which finally resulted in the improvement of dice score sof the follow-up. The mean dice score with CAAT is 0.6536a nd it increased to 0.6601 after level-set correction.

Background: Impaired cognitive ability is one of the most frequently reported neuropsychiatric symptoms in the post-COVID phase among patients. It is unclear whether this condition is related to structural or functional brain changes.

Purpose: In this study, we present a multimodal magnetic resonance imaging study of 36 post-COVID patients and 36 individually matched controls who had a mild form of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection from March 2020 to February 2022. This study aimed to investigate structural and functional brain alterations and their correlation with post-COVID symptoms and neurocognitive functions.

Materials and methods: The study protocol comprised an assessment of physical fatigue [Fatigue Severity Scale (FSS)], mental fatigue (Mental Fatigue Scale (MFS)], depression [Montgomery Asberg Depression Rating Scale (MADRS)], anxiety [Hospital Anxiety and Depression Scale (HAD)], post-COVID Symptoms Severity Score, and neurocognitive status [Repeatable Battery for the Assessment of Neuropsychological Status Update (RBANS)]. The magnetic resonance imaging protocol included morphological sequences, arterial spin labeling (ASL) and dynamic susceptibility contrast-enhanced (DSC) perfusion, diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (fMRI) sequences. Using these protocols, the assessments of macrostructural abnormalities, perfusion, gray matter density, white matter integrity, and brain connectivity were performed.

Results: Post-COVID patients had higher levels of physical fatigue, mental fatigue, depression, and anxiety than controls and showed cognitive impairment in all the RBANS domains except in Visuospatial/Construction. The subjective mental fatigue correlated with objective impaired cognitive ability in the RBANS test, particularly in the Attention domain. There were no differences between patients and controls regarding macrostructural abnormalities, regional volumes, regional perfusion metrics, gray matter density, or DTI parameters. We observed a significant positive correlation between RBANS Total Scale Index score and gray matter volume in the right superior/middle-temporal gyrus (p < 0.05) and a significant negative correlation between the white matter integrity and post-COVID symptoms (p < 0.05) in the same area. The connectivity differences were observed between patients and controls in a few regions, including the right middle frontal gyrus, an important area of convergence of the dorsal and ventral attention networks. We also noted a positive correlation between post-COVID symptoms and increased connectivity in the right temporoparietal junction, which is part of the ventral attention system.

Conclusion: In non-hospitalized subjects with post-COVID, we did not find any structural brain changes or changes in perfusion, compared to controls. However, we noted differences in connectivity within an important area for attention processes, which may be associated with post-COVID brain fog.

Background

Phase-contrast magnetic resonance imaging (PC-MRI) quantifies blood flow and velocity noninvasively. Challenges arise in neurovascular disorders due to small vessels. We evaluated the impact of voxel size, number of signal averages (NSA), and velocity encoding (VENC) on PC-MRI measurement accuracy and precision in a small-lumen vessel phantom.

Methods

We constructed an in vitro model with a constant flow rate using a 2.2-mm inner diameter plastic tube. A reservoir with a weight scale and timer was used as standard reference. Gradient-echo T1 weighted PC-MRI sequence was performed on a 3-T scanner with varying voxel size (2.5, 5.0, 7.5 mm³), NSA (1, 2, 3), and VENC (200, 300, 400 cm/s). We repeated measurements nine times per setting, calculating mean flow rate, maximum velocity, and least detectable difference (LDD).

Results

PC-MRI flow measurements were higher than standard reference values (mean ranging from 7.3 to 9.5 mL/s compared with 6.6 mL/s). Decreased voxel size improved accuracy, reducing flow rate measurements from 9.5 to 7.3 mL/s. The LDD for flow rate and velocity varied between 1 and 5%. The LDD for flow rate decreased with increased voxel size and NSA (p = 0.033 and 0.042). The LDD for velocity decreased with increased voxel size (p < 10^-16). No change was observed when VENC varied.

Conclusions

PC-MRI overestimated flow. However, it has high precision in a small-vessel phantom with constant flow rate. Improved accuracy was obtained with increasing spatial resolution (smaller voxels). Improved precision was obtained with increasing signal-to-noise ratio (larger voxels and/or higher NSA).

Relevance statement

Phase-contrast MRI is clinically used in large vessels. To further investigate the possibility of using phase-contrast MRI for smaller intracranial vessels in neurovascular disorders, we need to understand how acquisition parameters affect phase-contrast MRI-measured flow rate and velocity in small vessels.

Key points

• PC-MRI measures flow and velocity in a small lumen phantom with high precision but overestimates flow rate.

• The precision of PC-MRI measurements matches the precision of standard reference for flow rate measurements.

• Optimizing PC-MRI settings can enhance accuracy and precision in flow rate and velocity measurements.