A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain.

Age-related changes in brain structure include atrophy of the brain parenchyma and white matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to atrophy or impaired cerebrospinal fluid (CSF) circulation. The co-occurrence of these changes in neurodegenerative diseases and in aging brains often requires investigators to take both into account when studying the brain, however, automated segmentation of enlarged ventricles and white matter hyperintensities (WMHs) can be a challenging task. Here, we present a hybrid multi-atlas segmentation and convolutional autoencoder approach for joint ventricle parcellation and WMH segmentation from magnetic resonance images (MRIs). Our fully automated approach uses a convolutional autoencoder to generate a standardized image of grey matter, white matter, CSF, and WMHs, which, in conjunction with labels generated by a multi-atlas segmentation approach, is then fed into a convolutional neural network to parcellate the ventricular system. Hence, our approach does not depend on manually delineated training data for new data sets. The segmentation pipeline was validated on both healthy elderly subjects and subjects with normal pressure hydrocephalus using ground truth manual labels and compared with state-of-the-art segmentation methods. We then applied the method to a cohort of 2401 elderly brains to investigate associations of ventricle volume and WMH load with various demographics and clinical biomarkers, using a multiple regression model. Our results indicate that the ventricle volume and WMH load are both highly variable in a cohort of elderly subjects and there is an independent association between the two, which highlights the importance of taking both the possibility of enlarged ventricles and WMHs into account when studying the aging brain.

[Reversible cerebral vasoconstriction syndrome - a common cause of thunderclap headache].

Reversible cerebral vasoconstriction is characterized by thunderclap headache and vasoconstriction of cerebral arteries, with or without focal neurologic symptoms. The syndrome is three times more common in women with a mean age around 45 years. In approximately 60% of cases a cause can be identified, commonly after intake of vasoactive substances. The pathophysiology of reversible cerebral vasoconstriction syndrome is unknown, though temporary dysregulation in cerebral vascular tone is thought to be a key underlying mechanism. The syndrome typically follows a benign course; however, complications such as ischemic stroke or intracranial hemorrhage can cause permanent disability or death in a small minority of patients. Vascular imaging reveals alternating cerebral vasoconstriction and vasodilation that normalizes within 12 weeks. Calcium channel antagonists such as nimodipine reduce the frequency of thunderclap headaches but do not decidedly affect the risk of cerebral ischemia or hemorrhage. In this article the epidemiology, risk factors, pathophysiology, symptoms, diagnosis and treatment of RCVS is reviewed.

SegAE: Unsupervised white matter lesion segmentation from brain MRIs using a CNN autoencoder.

White matter hyperintensities (WMHs) of presumed vascular origin are frequently observed in magnetic resonance images (MRIs) of the elderly. Detection and quantification of WMHs is important to help doctors make diagnoses and evaluate prognosis of their elderly patients, and once quantified, these can act as biomarkers in clinical research studies. Manual delineation of WMHs can be both time-consuming and inconsistent, hence, automatic segmentation methods are often preferred. However, fully automatic methods can be challenging to construct due to the variability in lesion load, placement of lesions, and voxel intensities. Several state-of-the-art lesion segmentation methods based on supervised Convolutional Neural Networks (CNNs) have been proposed. These approaches require manually delineated lesions for training the parameters of the network. Here we present a novel approach for WMH segmentation using a CNN trained in an unsupervised manner, by reconstructing multiple MRI sequences as weighted sums of segmentations of WMHs and tissues present in the images. After training, our method can be used to segment new images that are not part of the training set to provide fast and robust segmentation of WMHs in a matter of seconds per subject. Comparisons with state-of-the-art WMH segmentation methods evaluated on ground truth manual labels from two distinct data sets and six different scanners indicate that the proposed method works well at generating accurate WMH segmentations without the need for manual delineations.

MAP1B mutations cause intellectual disability and extensive white matter deficit.

Discovery of coding variants in genes that confer risk of neurodevelopmental disorders is an important step towards understanding the pathophysiology of these disorders. Whole-genome sequencing of 31,463 Icelanders uncovers a frameshift variant (E712KfsTer10) in microtubule-associated protein 1B (MAP1B) that associates with ID/low IQ in a large pedigree (genome-wide corrected P = 0.022). Additional stop-gain variants in MAP1B (E1032Ter and R1664Ter) validate the association with ID and IQ. Carriers have 24% less white matter (WM) volume (ß = -2.1SD, P = 5.1 × 10-8), 47% less corpus callosum (CC) volume (ß = -2.4SD, P = 5.5 × 10-10) and lower brain-wide fractional anisotropy (P = 6.7 × 10-4). In summary, we show that loss of MAP1B function affects general cognitive ability through a profound, brain-wide WM deficit with likely disordered or compromised axons.

Regression models for analyzing radiological visual grading studies--an empirical comparison.

BACKGROUND: For optimizing and evaluating image quality in medical imaging, one can use visual grading experiments, where observers rate some aspect of image quality on an ordinal scale. To analyze the grading data, several regression methods are available, and this study aimed at empirically comparing such techniques, in particular when including random effects in the models, which is appropriate for observers and patients. METHODS: Data were taken from a previous study where 6 observers graded or ranked in 40 patients the image quality of four imaging protocols, differing in radiation dose and image reconstruction method. The models tested included linear regression, the proportional odds model for ordinal logistic regression, the partial proportional odds model, the stereotype logistic regression model and rank-order logistic regression (for ranking data). In the first two models, random effects as well as fixed effects could be included; in the remaining three, only fixed effects. RESULTS: In general, the goodness of fit (AIC and McFadden's Pseudo R (2)) showed small differences between the models with fixed effects only. For the mixed-effects models, higher AIC and lower Pseudo R (2) was obtained, which may be related to the different number of parameters in these models. The estimated potential for dose reduction by new image reconstruction methods varied only slightly between models. CONCLUSIONS: The authors suggest that the most suitable approach may be to use ordinal logistic regression, which can handle ordinal data and random effects appropriately.

[Acute ischemic stroke in female adolescent - Case report]. / Brátt blóðþurrðarslag hjá unglingsstúlku Sjúkratilfelli.

During soccer practice a fifteen year old girl experienced a sudden onset of pain in the left side of her neck and collapsed. Upon arrival at the emergency room she had right hemiparesis and expressive aphasia. On CT angiography a left carotid arterial dissection was suspected. Symptoms improved during the first three days but worsened again on the fourth and a CT scan showed an ischemic area in the brain. Conventional angiography showed decreased perfusion in the left middle cerebral artery but no evidence of dissection or thrombus. The most likely diagnosis was thought to be reverse cerebral vasoconstriction syndrome and the girl was treated with calcium channel inhibitors. Here we report the case and review the literature.

Hybrid iterative reconstruction algorithm in brain CT: a radiation dose reduction and image quality assessment study.

BACKGROUND: Iterative reconstruction (IR) algorithms improve image quality and allow for radiation dose reduction in CT. Dose reduction is particularly challenging in brain CT where good low-contrast resolution is essential. Ideally, evaluation of image quality combines objective measurements and subjective assessment of clinically relevant quality criteria. Subjective assessment is associated with various pitfalls and biases. PURPOSE: To evaluate the potential of the hybrid IR algorithm iDOSE(4) to preserve image quality in phantom and clinical brain CT acquired with 30% reduced radiation dose, and to discuss the image quality assessment methods. MATERIAL AND METHODS: Forty patients underwent two consecutive brain CTs with normal radiation dose (ND) and 30% reduced dose (RD). Both ND and RD were reconstructed with FBP. In addition the reduced dose CTs were reconstructed with two levels of IR (ID2, ID4). Three image quality criteria (grey-white-matter discrimination, basal ganglia delineation, general image quality) were graded and ranked by six neuroradiologists. Noise levels and contrast-to-noise ratios (CNR) were measured in clinical data. Noise, signal-to-noise ratio (SNR), spatial resolution, and noise-power spectrum (NPS) were also assessed in a phantom. RESULTS: Subjective image quality was considered adequate for clinical use for all reconstructions, graded good or excellent in 93% of cases for ND, 83% for ID4, 79% for ID2, and 67% for RD. For all quality parameters, ID4 and ID2 were graded better than RD (P < 0.0055 and P < 0.035), but worse than ND (P < 0.001). In clinical images, objective measurements showed lower noise and significantly higher CNR in ID4 compared with ND and RD (P < 0.001). CNR was similar for ID2 and ND. In the phantom, IR reduced noise while maintaining spatial resolution and NPS. CONCLUSION: The IR algorithm improves image quality of reduced dose CTs and consistently delivers sufficient image quality for clinical purposes. Pitfalls related to subjective assessment can be addressed with careful study design.

Hybrid iterative reconstruction algorithm improves image quality in craniocervical CT angiography.

OBJECTIVE: The purpose of this study was to evaluate the potential of a hybrid iterative reconstruction algorithm for improving image quality in craniocervical CT angiography (CTA) and to assess observer performance. SUBJECTS AND METHODS: Thirty patients (mean age, 58 years; range 16-80 years) underwent standard craniocervical CTA (volume CT dose index, 6.8 mGy, 2.8 mSv). Images were reconstructed using both filtered back projection (FBP) and a hybrid iterative reconstruction algorithm. Five neuroradiologists assessed general image quality and delineation of the vessel lumen in seven arterial segments using a 4-grade scale. Interobserver and intraobserver variability were determined. Mean attenuation and noise were measured and signal-to-noise and contrast-to-noise ratios calculated. Descriptive statistics are presented and data analyzed using linear mixed-effects models. RESULTS: In pooled data, image quality in iterative reconstruction was graded superior to FBP regarding all five quality criteria (p < 0.0001), with the greatest improvement observed in the vertebral arteries. Iterative reconstruction resulted in elimination of arterial segments graded poor. Interobserver percentage agreement was significantly better (p = 0.024) for iterative reconstruction (69%) than for FBP (66%) but worse than intraobserver percentage agreement (mean, 79%). Noise levels, signal-to-noise ratio, and contrast-to-noise ratio were significantly (p < 0.001) improved in iterative reconstruction at all measured levels. CONCLUSION: The iterative reconstruction algorithm significantly improves image quality in craniocervical CT, especially at the thoracic inlet. Despite careful study design, considerable interobserver and intraobserver variability was noted.

Comprehensive CT Evaluation in Acute Ischemic Stroke: Impact on Diagnosis and Treatment Decisions.

Background. With modern CT imaging a comprehensive overview of cerebral macro- and microcirculation can be obtained within minutes in acute ischemic stroke. This opens for patient stratification and individualized treatment. Methods. Four patients with acute ischemic stroke of different aetiologies and/or treatments were chosen for illustration of the comprehensive CT protocol and its value in subsequent treatment decisions. The patients were clinically evaluated according to the NIHSS-scale, examined with the comprehensive CT protocol including both CT angiography and CT perfusion, and followed up by MRI. Results. The comprehensive CT examination protocol increased the examination time but did not delay treatment initiation. In some cases CT angiography revealed the cause of stroke while CT perfusion located and graded the perfusion defect with reasonable accuracy, confirmed by follow-up MR-diffusion. In the presented cases findings of the comprehensive CT examination influenced the treatment strategy. Conclusions. The comprehensive CT examination is a fast and safe method allowing accurate diagnosis and making way for individualized treatment in acute ischemic stroke.