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We present DeepVesselNet, an architecture tailored to the challenges faced when extracting vessel trees and networks and corresponding features in 3-D angiographic volumes using deep learning. We discuss the problems of low execution speed and high memory requirements associated with full 3-D networks, high-class imbalance arising from the low percentage (<3%) of vessel voxels, and unavailability of accurately annotated 3-D training data—and offer solutions as the building blocks of DeepVesselNet. First, we formulate 2-D orthogonal cross-hair filters which make use of 3-D context information at a reduced computational burden. Second, we introduce a class balancing cross-entropy loss function with false-positive rate correction to handle the high-class imbalance and high false positive rate problems associated with existing loss functions. Finally, we generate a synthetic dataset using a computational angiogenesis model capable of simulating vascular tree growth under physiological constraints on local network structure and topology and use these data for transfer learning. We demonstrate the performance on a range of angiographic volumes at different spatial scales including clinical MRA data of the human brain, as well as CTA microscopy scans of the rat brain. Our results show that cross-hair filters achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy that does not differ from full 3-D filters. Our class balancing metric is crucial for training the network, and transfer learning with synthetic data is an efficient, robust, and very generalizable approach leading to a network that excels in a variety of angiography segmentation tasks. We observe that sub-sampling and max pooling layers may lead to a drop in performance in tasks that involve voxel-sized structures. To this end, the DeepVesselNet architecture does not use any form of sub-sampling layer and works well for vessel segmentation, centerline prediction, and bifurcation detection. We make our synthetic training data publicly available, fostering future research, and serving as one of the first public datasets for brain vessel tree segmentation and analysis.

In Multiple Sclerosis (MS), detection of T2-hyperintense white matter (WM) lesions on magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and predicting prognosis in early disease. Automated lesion detection is not only desirable with regard to time and cost effectiveness but also constitutes a prerequisite to minimize user bias. Here, we developed and evaluated an algorithm for automated lesion detection requiring a three-dimensional (3D) gradient echo (GRE) T1-weighted and a FLAIR image at 3 Tesla (T). Our tool determines the three tissue classes of gray matter (GM) and WM as well as cerebrospinal fluid (CSF) from the T1-weighted image, and, then, the FLAIR intensity distribution of each tissue class in order to detect outliers, which are interpreted as lesion beliefs. Next, a conservative lesion belief is expanded toward a liberal lesion belief. To this end, neighboring voxels are analyzed and assigned to lesions under certain conditions. This is done iteratively until no further voxels are assigned to lesions. Herein, the likelihood of belonging to WM or GM is weighed against the likelihood of belonging to lesions. We evaluated our algorithm in 53 MS patients with different lesion volumes, in 10 patients with posterior fossa lesions, and 18 control subjects that were all scanned at the same 3T scanner (Achieva, Philips, Netherlands). We found good agreement with lesions determined by manual tracing (R2 values of over 0.93 independent of FLAIR slice thickness up to 6mm). These results require validation with data from other protocols based on a conventional FLAIR sequence and a 3D GRE T1-weighted sequence. Yet, we believe that our tool allows fast and reliable segmentation of FLAIR-hyperintense lesions, which might simplify the quantification of lesions in basic research and even clinical trials.

BackgroundRecent studies suggested that modified Thrombolysis in Cerebral Infarction grade (mTICI) 3 reperfusions are associated with superior outcome to mTICI2b reperfusions, questioning if neurointerventionalists should generally strive to achieve mTICI3.MethodsRetrospective analysis of successfully reperfused MCA occlusions (n=246) with available angiography runs between every manoeuvre was performed. Final reperfusion success and those between all single manoeuvres were evaluated applying the modified version of the TICI score (including TICI2c). Final TICI2c/3 reperfusions were dichotomized as ‘direct’ (reperfusion before final manoeuvre ≤mTICI2a) or ‘secondary improved’ (mTICI2b was achieved).ResultsPatients with mTICI2c reperfusion had similar outcome to patients with mTICI3 rather than mTICI2b reperfusions. Compared with mTICI2c/3-patients, mTICI2b-patients had lower rates of neurological improvement (33.3% vs. 61.2%, p<0.001) and good functional outcome (28.7% vs. 46.5%, p=0.008). In 28 patients, mTICI2b reperfusion was improved to mTICI2c/3 without complications. Outcome of patients with ‘direct’ or ‘secondary improved’ mTICI2c/3 did not differ (p>0.5).ConclusionImproving mTICI2b reperfusions to mTICI2c/3 reperfusions is sometimes technically feasible and safe, and associated with clinical benefit comparable to ‘direct’ mTICI2c/3 reperfusions. If confirmed, a more aggressive treatment approach in cases of already achieved mTICI2b may be justified, although proper patient selection is needed.Key Points• Patients with mTICI2c or 3 reperfusions have a comparable clinical course.• mTICI2c/3 are associated with a larger therapeutic benefit than are mTICI2b reperfusions.• Improving reperfusion from mTICI2b to mTICI2c/3 is sometimes feasible and reasonably safe.• Outcome of patients with ‘secondary improved’ and ‘direct’ mTICI2c/3 is not different.

Alzheimer's disease (AD) is a neurodegenerative disorder that prominently affects cerebral connectivity. Assessing the functional connectivity at rest, recent functional MRI (fMRI) studies reported on the existence of resting-state networks (RSNs). RSNs are characterized by spatially coherent, spontaneous fluctuations in the blood oxygen level-dependent signal and are made up of regional patterns commonly involved in functions such as sensory, attention, or default mode processing. In AD, the default mode network (DMN) is affected by reduced functional connectivity and atrophy. In this work, we analyzed functional and structural MRI data from healthy elderly (n = 16) and patients with amnestic mild cognitive impairment (aMCI) (n = 24), a syndrome of high risk for developing AD. Two questions were addressed: (i) Are any RSNs altered in aMCI? (ii) Do changes in functional connectivity relate to possible structural changes? Independent component analysis of restingstate fMRI data identified eight spatially consistent RSNs. Only selected areas of the DMN and the executive attention network demonstrated reduced network-related activity in the patient group. Voxel-based morphometry revealed atrophy in both medial temporal lobes (MTL) of the patients. The functional connectivity between both hippocampi in the MTLs and the posterior cingulate of the DMN was present in healthy controls but absent in patients. We conclude that in individuals at risk for AD, a specific subset of RSNs is altered, likely representing effects of ongoing early neurodegeneration. We interpret our finding as a proof of principle, demonstrating that functional brain disorders can be characterized by functional-disconnectivity profiles of RSNs.

Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions (https://doi.org/10.5281/zenodo.7153326). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge (https://www.isles-challenge.org/) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.

Despite great advances in brain tumor segmentation and clear clinical need, translation of state-of-the-art computational methods into clinical routine and scientific practice remains a major challenge. Several factors impede successful implementations, including data standardization and preprocessing. However, these steps are pivotal for the deployment of state-of-the-art image segmentation algorithms. To overcome these issues, we present BraTS Toolkit. BraTS Toolkit is a holistic approach to brain tumor segmentation and consists of three components: First, the BraTS Preprocessor facilitates data standardization and preprocessing for researchers and clinicians alike. It covers the entire image analysis workflow prior to tumor segmentation, from image conversion and registration to brain extraction. Second, BraTS Segmentor enables orchestration of BraTS brain tumor segmentation algorithms for generation of fully-automated segmentations. Finally, Brats Fusionator can combine the resulting candidate segmentations into consensus segmentations using fusion methods such as majority voting and iterative SIMPLE fusion. The capabilities of our tools are illustrated with a practical example to enable easy translation to clinical and scientific practice.

•gGM-CSF coupling serves as a non-invasive measure of the ventricular CSF system.•We analyzed fMRI-based coupling after sleep deprivation and regular sleep.•gGM-CSF coupling increased after sleep deprivation in young, healthy adults.•This increase may reflect a compensatory adaptive response to sleep loss.•Younger adults may exhibit greater responsiveness in CSF dynamics than older adults. Evidence indicates that brain waste clearance happens more efficiently during sleep. Recent studies suggest that the correlation, i.e., coupling, between the cortical grey matter (gGM) blood oxygenation level-dependent signal and cerebrospinal fluid (CSF) signal in the foramen magnum, measured via resting-state functional MRI (fMRI), serves as a non-invasive measure of the ventricular CSF system. Sleep deprivation has been demonstrated to affect brain function and health. Our aim is to assess gGM-CSF coupling after partial sleep deprivation, hypothesizing a change in the coupling measure relative to normal sleep. To test this hypothesis, we analyzed resting-state fMRI data from 63 healthy participants in the “The Stockholm Sleepy Brain Study”, grouped according to age: younger (20 - 29 years) and older (65 - 75 years) adults. We examined gGM-CSF coupling twice in each subject, in the evening following a night of normal sleep and after a night of partial sleep deprivation (≤ 3 h of sleep). Our results revealed significantly increased gGM-CSF coupling after sleep deprivation compared to normal sleep (mean r = -0.30 ± 0.19 vs. -0.25 ± 0.14; t(62) = 2.05, p = 0.045). A linear mixed model demonstrated a significant interaction of age with the sleep condition (β = 0.0031, t = 2.73, p = 0.0083), showing significant changes only in the younger subgroup (t(35) = 2.99, p = 0.0050). These findings indicate that gGM-CSF coupling increases after partial sleep deprivation in younger adults, which may reflect compensatory mechanisms in response to reduced sleep duration. Furthermore, the results suggest that this compensatory response could be diminished in older adults.

Objectives To determine the correlation between cervicothoracic and lumbar volumetric bone mineral density (vBMD) in an average cohort of adults and to identify specific diagnostic thresholds for the cervicothoracic spine on the individual subject level. Methods In this HIPPA–compliant study, we retrospectively included 260 patients (59.7 ± 18.3 years, 105 women), who received a contrast-enhanced or non-contrast-enhanced CT scan. vBMD was extracted using an automated pipeline ( https://anduin.bonescreen.de ). The association of vBMD between each vertebra spanning C2–T12 and the averaged values at the lumbar spine (L1–L3) was analyzed before and after semiquantitative assessment of fracture status and degeneration, and respective vertebra-specific cut-off values for osteoporosis were calculated using linear regression. Results In both women and men, trabecular vBMD decreased with age in the cervical, thoracic, and lumbar regions. vBMD values of cervicothoracic vertebrae showed strong correlations with lumbar vertebrae (L1–L3), with a median Pearson value of r  = 0.87 (range: r C2  = 0.76 to r T12  = 0.96). The correlation coefficients were significantly lower ( p  < 0.0001) without excluding fractured and degenerated vertebrae, median r  = 0.82 (range: r C2  = 0.69 to r T12  = 0.93). Respective cut-off values for osteoporosis peaked at C4 (209.2 mg/ml) and decreased to 83.8 mg/ml at T12. Conclusion Our data show a high correlation between clinically used mean L1–L3 values and vBMD values elsewhere in the spine, independent of age. The proposed cut-off values for the cervicothoracic spine therefore may allow the determination of low bone mass even in clinical cases where only parts of the spine are imaged. Key Points vBMD of all cervicothoracic vertebrae showed strong correlation with lumbar vertebrae (L1–L3), with a median Pearson’s correlation coefficient of r = 0.87 (range: r C2   = 0.76 to r T12   = 0.96). The correlation coefficients were significantly lower (p < 0.0001) without excluding fractured and moderate to severely degenerated vertebrae, median r = 0.82 (range: r C2   = 0.69 to r T12   = 0.93). We postulate that trabecular vBMD < 200 mg/ml for the cervical spine and < 100 mg/ml for the thoracic spine are strong indicators of osteoporosis, similar to < 80 mg/ml at the lumbar spine.

•Temporal fatty tissue as a reference region for retrospective T1w MRI intensity scaling.•High correlation of intensity-scaled T1w images with quantitative R1 maps.•Lower mean R1 values and fat-scaled T1w intensities in NAWM of patients with high EDSS.•NAWM intensity variability in bias-corrected T1w images increases with MS disease severity.•Conventional T1w MRI intensities comprise exploitable information on MS pathology. Conventional T1-weighted (T1w) magnetic resonance imaging (MRI) is commonly used in multiple sclerosis (MS) morphometry and volumetry research. However, arbitrary intensity scales preclude interpretation of signal values across patients, sites, and time. This requires quantitative MRI techniques, which are not always available. This study assessed T1w image intensity scaling methods, relying on extracerebral reference regions, for quantitative analysis of brain MRI in MS. In total, 701 people with a diagnosis of radiologically isolated syndrome, clinically isolated syndrome, or MS were included. Four intensity scaling strategies were applied: 1) MRI signal modeling, 2) linear scaling with reference regions, 3) z-score standardization, and 4) none (only bias field correction). Methods were evaluated using variance analysis, R1 map comparison, and normal-appearing white matter (NAWM) intensity group comparison, using mean and coefficient of variation (CoV), between low (≤3) and high (>3) expanded disability status scale (EDSS) scores. Statistical analysis was conducted using Pearson’s r, two-sided Welch two-sample t-test, ANCOVA, and Cohen’s d. Linear scaling with temporal fatty tissue achieved the most consistent variance reduction and strong correlation with R1 maps (r = 0.84). R1 values in NAWM were significantly lower in people with high compared to low EDSS scores (d = -0.351). Similarly, group differences in mean NAWM intensity of fat-scaled images were significant (d = -0.252). The largest group differences were found in NAWM CoV in bias field-corrected T1w images (d = 0.818). Linear scaling with fatty tissue most accurately reproduced the results obtained with R1 maps. Changes in MS NAWM appear to increase intensity variability detectable in conventional T1w images.

•Thalamic nuclei volume is globally, not locally, lower in preterm-born adults.•These alterations are linked to the extent of stress exposure after birth.•Lateral, medial, and pulvinar nuclei volume aberrations are relevant for cognition. Lasting thalamus volume reduction after preterm birth is a prominent finding. However, whether thalamic nuclei volumes are affected differentially by preterm birth and whether nuclei aberrations are relevant for cognitive functioning remains unknown. Using T1-weighted MR-images of 83 adults born very preterm (≤ 32 weeks’ gestation; VP) and/or with very low body weight (≤ 1,500 g; VLBW) as well as of 92 full-term born (≥ 37 weeks’ gestation) controls, we compared thalamic nuclei volumes of six subregions (anterior, lateral, ventral, intralaminar, medial, and pulvinar) across groups at the age of 26 years. To characterize the functional relevance of volume aberrations, cognitive performance was assessed by full-scale intelligence quotient using the Wechsler Adult Intelligence Scale and linked to volume reductions using multiple linear regression analyses. Thalamic volumes were significantly lower across all examined nuclei in VP/VLBW adults compared to controls, suggesting an overall rather than focal impairment. Lower nuclei volumes were linked to higher intensity of neonatal treatment, indicating vulnerability to stress exposure after birth. Furthermore, we found that single results for lateral, medial, and pulvinar nuclei volumes were associated with full-scale intelligence quotient in preterm adults, albeit not surviving correction for multiple hypotheses testing. These findings provide evidence that lower thalamic volume in preterm adults is observable across all subregions rather than focused on single nuclei. Data suggest the same mechanisms of aberrant thalamus development across all nuclei after premature birth.