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Purpose: To generate perfusion parameter maps from Time-of-flight magnetic resonance angiography (TOF-MRA) images using artificial intelligence to provide an alternative to traditional perfusion imaging techniques., Materials and Methods: This retrospective study included a total of 272 patients with cerebrovascular diseases; 200 with acute stroke (from 2010 to 2018), and 72 with steno-occlusive disease (from 2011 to 2014). For each patient the TOF MRA image and the corresponding Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) were retrieved from the datasets. The authors propose an adapted generative adversarial network (GAN) architecture, 3D pix2pix GAN, that generates common perfusion maps (CBF, CBV, MTT, TTP, Tmax) from TOF-MRA images. The performance was evaluated by the structural similarity index measure (SSIM). For a subset of 20 patients from the acute stroke dataset, the Dice coefficient was calculated to measure the overlap between the generated and real hypoperfused lesions with a time-to-maximum (Tmax) > 6 s., Results: The GAN model exhibited high visual overlap and performance for all perfusion maps in both datasets: acute stroke (mean SSIM 0.88-0.92, mean PSNR 28.48-30.89, mean MAE 0.02-0.04 and mean NRMSE 0.14-0.37) and steno-occlusive disease patients (mean SSIM 0.83-0.98, mean PSNR 23.62-38.21, mean MAE 0.01-0.05 and mean NRMSE 0.03-0.15). For the overlap analysis for lesions with Tmax>6 s, the median Dice coefficient was 0.49., Conclusion: Our AI model can successfully generate perfusion parameter maps from TOF-MRA images, paving the way for a non-invasive alternative for assessing cerebral hemodynamics in cerebrovascular disease patients. This method could impact the stratification of patients with cerebrovascular diseases. Our results warrant more extensive refinement and validation of the method., Competing Interests: Declaration of competing interest Not related to this work, Dr Sobesky reports receipt of speakers' honoraria from Pfizer, Boehringer Ingelheim, and Daiichi Sankyo. The other authors report no conflicts of interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Intracranial atherosclerotic disease (ICAD) poses a significant risk of subsequent stroke but current prevention strategies are limited. Mechanistic simulations of brain hemodynamics offer an alternative precision medicine approach by utilising individual patient characteristics. For clinical use, however, current simulation frameworks have insufficient validation. In this study, we performed the first quantitative validation of a simulation-based precision medicine framework to assess cerebral hemodynamics in patients with ICAD against clinical standard perfusion imaging. In a retrospective analysis, we used a 0-dimensional simulation model to detect brain areas that are hemodynamically vulnerable to subsequent stroke. The main outcome measures were sensitivity, specificity, and area under the receiver operating characteristics curve (ROC AUC) of the simulation to identify brain areas vulnerable to subsequent stroke as defined by quantitative measurements of relative mean transit time (relMTT) from dynamic susceptibility contrast MRI (DSC-MRI). In 68 subjects with unilateral stenosis >70% of the internal carotid artery (ICA) or middle cerebral artery (MCA), the sensitivity and specificity of the simulation were 0.65 and 0.67, respectively. The ROC AUC was 0.68. The low-to-moderate accuracy of the simulation may be attributed to assumptions of Newtonian blood flow, rigid vessel walls, and the use of time-of-flight MRI for geometric representation of subject vasculature. Future simulation approaches should focus on integrating additional patient data, increasing accessibility of precision medicine tools to clinicians, addressing disease burden disparities amongst different populations, and quantifying patient benefit. Our results underscore the need for further improvement of mechanistic simulations of brain hemodynamics to foster the translation of the technology to clinical practice., Competing Interests: VM, TK, and AH reported receiving personal fees from ai4medicine outside the submitted work. Whilst not related to this work, JS reports receipt of speakers’ honoraria from Pfizer, Boehringer Ingelheim, Daiichi Sankyo, Gore, Maquet, Paion, Sanofi, Takeda pharma, UCB, and Berlin Chemie. DF reported receiving grants from the European Commission, reported receiving personal fees from and holding an equity interest in ai4medicine outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Behland, Madai, Aydin, Akay, Kossen, Hilbert, Sobesky, Vajkoczy and Frey.)

Stroke is a major cause of death or disability. As imaging-based patient stratification improves acute stroke therapy, dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is of major interest in image brain perfusion. However, expert-level perfusion maps require a manual or semi-manual post-processing by a medical expert making the procedure time-consuming and less-standardized. Modern machine learning methods such as generative adversarial networks (GANs) have the potential to automate the perfusion map generation on an expert level without manual validation. We propose a modified pix2pix GAN with a temporal component (temp-pix2pix-GAN) that generates perfusion maps in an end-to-end fashion. We train our model on perfusion maps infused with expert knowledge to encode it into the GANs. The performance was trained and evaluated using the structural similarity index measure (SSIM) on two datasets including patients with acute stroke and the steno-occlusive disease. Our temp-pix2pix architecture showed high performance on the acute stroke dataset for all perfusion maps (mean SSIM 0.92-0.99) and good performance on data including patients with the steno-occlusive disease (mean SSIM 0.84-0.99). While clinical validation is still necessary for future studies, our results mark an important step toward automated expert-level perfusion maps and thus fast patient stratification., Competing Interests: TK, VM, and AHi reported receiving personal fees from ai4medicine outside the submitted work. While not related to this work, JS reports receipt of speakers' honoraria from Pfizer, Boehringer Ingelheim, and Daiichi Sankyo. DF reported receiving grants from the European Commission, reported receiving personal fees from and holding an equity interest in ai4medicine outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kossen, Madai, Mutke, Hennemuth, Hildebrand, Behland, Aslan, Hilbert, Sobesky, Bendszus and Frey.)

Introduction: When time since stroke onset is unknown, DWI-FLAIR mismatch rating is an established technique for patient stratification. A visible DWI lesion without corresponding parenchymal hyperintensity on FLAIR suggests time since onset of under 4.5 h and thus a potential benefit from intravenous thrombolysis. To improve accuracy and availability of the mismatch concept, deep learning might be able to augment human rating and support decision-making in these cases., Methods: We used unprocessed DWI and coregistered FLAIR imaging data to train a deep learning model to predict dichotomized time since ischemic stroke onset. We analyzed the performance of Group Convolutional Neural Networks compared to other deep learning methods. Unlabeled imaging data was used for pre-training. Prediction performance of the best deep learning model was compared to the performance of four independent junior and senior raters. Additionally, in cases deemed indeterminable by human raters, model ratings were used to augment human performance. Post-hoc gradient-based explanations were analyzed to gain insights into model predictions., Results: Our best predictive model performed comparably to human raters. Using model ratings in cases deemed indeterminable by human raters improved rating accuracy and interrater agreement for junior and senior ratings. Post-hoc explainability analyses showed that the model localized stroke lesions to derive predictions., Discussion: Our analysis shows that deep learning based clinical decision support has the potential to improve the accessibility of the DWI-FLAIR mismatch concept by supporting patient stratification., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Brain arteries are routinely imaged in the clinical setting by various modalities, e.g., time-of-flight magnetic resonance angiography (TOF-MRA). These imaging techniques have great potential for the diagnosis of cerebrovascular disease, disease progression, and response to treatment. Currently, however, only qualitative assessment is implemented in clinical applications, relying on visual inspection. While manual or semi-automated approaches for quantification exist, such solutions are impractical in the clinical setting as they are time-consuming, involve too many processing steps, and/or neglect image intensity information. In this study, we present a deep learning-based solution for the anatomical labeling of intracranial arteries that utilizes complete information from 3D TOF-MRA images. We adapted and trained a state-of-the-art multi-scale Unet architecture using imaging data of 242 patients with cerebrovascular disease to distinguish 24 arterial segments. The proposed model utilizes vessel-specific information as well as raw image intensity information, and can thus take tissue characteristics into account. Our method yielded a performance of 0.89 macro F1 and 0.90 balanced class accuracy (bAcc) in labeling aggregated segments and 0.80 macro F1 and 0.83 bAcc in labeling detailed arterial segments on average. In particular, a higher F1 score than 0.75 for most arteries of clinical interest for cerebrovascular disease was achieved, with higher than 0.90 F1 scores in the larger, main arteries. Due to minimal pre-processing, simple usability, and fast predictions, our method could be highly applicable in the clinical setting., Competing Interests: Author AH reported receiving personal fees from ai4medicine outside the submitted work. Author VM reported receiving personal fees from ai4medicine outside the submitted work. Author DF reported receiving grants from the European Commission and the German Federal Ministry of Education and Research, reported receiving personal fees from and holding an equity interest in ai4medicine outside the submitted work. Author JF has received consulting and advisory board fees from BioClinica, Cerevast, Artemida, Brainomix, Biogen, BMS, EISAI, and Guerbet. There is no connection, commercial exploitation, transfer, or association between the projects of ai4medicine and the results presented in this work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hilbert, Rieger, Madai, Akay, Aydin, Behland, Khalil, Galinovic, Sobesky, Fiebach, Livne and Frey.)

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging., Competing Interests: TK, MH, VM, and AHi are employed by ai4medicine. FB and AHe are employed by Fraunhofer. JS reports receipt of speakers' honoraria from Pfizer, Boehringer Ingelheim, and Daiichi Sankyo. JF has received consulting and advisory board fees from BioClinica, Cerevast, Artemida, Brainomix, Biogen, BMS, EISAI, and Guerbet. DF receiving grants from the European Commission, reported receiving personal fees from and holding an equity interest in ai4medicine. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kossen, Hirzel, Madai, Boenisch, Hennemuth, Hildebrand, Pokutta, Sharma, Hilbert, Sobesky, Galinovic, Khalil, Fiebach and Frey.)

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark U-Net trained on real data (DSC 0.901; bAVD 0.294) for intracranial vessels. In conclusion, our solution generates realistic 3D TOF-MRA patches and labels for brain vessel segmentation. We demonstrate the benefit of using mixed precision for computational efficiency resulting in the best-performing GAN-architecture. Our work paves the way towards sharing of labeled 3D medical data which would increase generalizability of deep learning models for clinical use., Competing Interests: Declaration of Competing Interest All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript: None of the authors have direct or indirect financial interest in the subject matter discussed in the manuscript. However, the following disclosures unrelated to the current work is as follows: Pooja Subramaniam reported receiving personal fees from ai4medicine outside the submitted work. Tabea Kossen reported receiving personal fees from ai4medicine outside the submitted work. Dr Madai reported receiving personal fees from ai4medicine outside the submitted work. Adam Hilbert reported receiving personal fees from ai4medicine outside the submitted work. Dr Frey reported receiving grants from the European Commission, reported receiving personal fees from and holding an equity interest in ai4medicine outside the submitted work. There is no connection, commercial exploitation, transfer or association between the projects of ai4medicine and the results presented in this work. While not related to this work, Dr Sobesky reports receipt of speakers honoraria from Pfizer, Boehringer Ingelheim, and Daiichi Sankyo. Furthermore, Dr Fiebach has received consulting and advisory board fees from BioClinica, Cerevast, Artemida, Brainomix, Biogen, BMS, EISAI, and Guerbet., (Copyright © 2022. Published by Elsevier B.V.)

Background: Cerebrovascular disease, in particular stroke, is a major public health challenge. An important biomarker is cerebral hemodynamics. To measure and quantify cerebral hemodynamics, however, only invasive, potentially harmful or time-to-treatment prolonging methods are available., Results: We present a simulation-based approach which allows calculation of cerebral hemodynamics based on the patient-individual vessel configuration derived from structural vessel imaging. For this, we implemented a framework allowing segmentation and annotation of brain vessels from structural imaging followed by 0-dimensional lumped simulation modeling of cerebral hemodynamics. For annotation, a 3D-graphical user interface was implemented. For 0D-simulation, we used a modified nodal analysis, which was adapted for easy implementation by code. The simulation enables identification of areas vulnerable to stroke and simulation of changes due to different systemic blood pressures. Moreover, sensitivity analysis was implemented allowing the live simulation of changes to simulate procedures and disease progression. Beyond presentation of the framework, we demonstrated in an exploratory analysis in 67 patients that the simulation has a high specificity and low-to-moderate sensitivity to detect perfusion changes in classic perfusion imaging., Conclusions: The presented precision medicine approach using novel biomarkers has the potential to make the application of harmful and complex perfusion methods obsolete.

Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for medical image analysis. This is a big challenge, especially for neuroimaging. Here, the brain's unique structure allows for re-identification and thus requires non-conventional anonymization. Generative adversarial networks (GANs) have the potential to provide anonymous images while preserving predictive properties. Analyzing brain vessel segmentation, we trained 3 GANs on time-of-flight (TOF) magnetic resonance angiography (MRA) patches for image-label generation: 1) Deep convolutional GAN, 2) Wasserstein-GAN with gradient penalty (WGAN-GP) and 3) WGAN-GP with spectral normalization (WGAN-GP-SN). The generated image-labels from each GAN were used to train a U-net for segmentation and tested on real data. Moreover, we applied our synthetic patches using transfer learning on a second dataset. For an increasing number of up to 15 patients we evaluated the model performance on real data with and without pre-training. The performance for all models was assessed by the Dice Similarity Coefficient (DSC) and the 95th percentile of the Hausdorff Distance (95HD). Comparing the 3 GANs, the U-net trained on synthetic data generated by the WGAN-GP-SN showed the highest performance to predict vessels (DSC/95HD 0.85/30.00) benchmarked by the U-net trained on real data (0.89/26.57). The transfer learning approach showed superior performance for the same GAN compared to no pre-training, especially for one patient only (0.91/24.66 vs. 0.84/27.36). In this work, synthetic image-label pairs retained generalizable information and showed good performance for vessel segmentation. Besides, we showed that synthetic patches can be used in a transfer learning approach with independent data. This paves the way to overcome the challenges of scarce data and anonymization in medical imaging., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Introduction: Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries. They are, however, only visually assessed. Fully automated vessel segmentation integrated into the clinical routine could facilitate the time-critical diagnosis of vessel abnormalities and might facilitate the identification of valuable biomarkers for cerebrovascular events. In the present work, we developed and validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a large aggregated dataset of patients with cerebrovascular diseases. Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from three different studies enrolling patients with cerebrovascular diseases. A context path, dually capturing high- and low-resolution volumes, and deep supervision were implemented. The BRAVE-NET model was compared to a baseline Unet model and variants with only context paths and deep supervision, respectively. The models were developed and validated using high-quality manual labels as ground truth. Next to precision and recall, the performance was assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD); 95-percentile Hausdorff distance (95HD); and via visual qualitative rating. Results: The BRAVE-NET performance surpassed the other models for arterial brain vessel segmentation with a DSC = 0.931, AVD = 0.165, and 95HD = 29.153. The BRAVE-NET model was also the most resistant toward false labelings as revealed by the visual analysis. The performance improvement is primarily attributed to the integration of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep supervision architectural component. Discussion: We present a new state-of-the-art of arterial brain vessel segmentation tailored to cerebrovascular pathology. We provide an extensive experimental validation of the model using a large aggregated dataset encompassing a large variability of cerebrovascular disease and an external set of healthy volunteers. The framework provides the technological foundation for improving the clinical workflow and can serve as a biomarker extraction tool in cerebrovascular diseases., (Copyright © 2020 Hilbert, Madai, Akay, Aydin, Behland, Sobesky, Galinovic, Khalil, Taha, Wuerfel, Dusek, Niendorf, Fiebach, Frey and Livne.)

Importance: The association of surgical hematoma evacuation with clinical outcomes in patients with cerebellar intracerebral hemorrhage (ICH) has not been established., Objective: To determine the association of surgical hematoma evacuation with clinical outcomes in cerebellar ICH., Design, Setting, and Participants: Individual participant data (IPD) meta-analysis of 4 observational ICH studies incorporating 6580 patients treated at 64 hospitals across the United States and Germany (2006-2015)., Exposure: Surgical hematoma evacuation vs conservative treatment., Main Outcomes and Measures: The primary outcome was functional disability evaluated by the modified Rankin Scale ([mRS] score range: 0, no functional deficit to 6, death) at 3 months; favorable (mRS, 0-3) vs unfavorable (mRS, 4-6). Secondary outcomes included survival at 3 months and at 12 months. Analyses included propensity score matching and covariate adjustment, and predicted probabilities were used to identify treatment-related cutoff values for cerebellar ICH., Results: Among 578 patients with cerebellar ICH, propensity score-matched groups included 152 patients with surgical hematoma evacuation vs 152 patients with conservative treatment (age, 68.9 vs 69.2 years; men, 55.9% vs 51.3%; prior anticoagulation, 60.5% vs 63.8%; and median ICH volume, 20.5 cm3 vs 18.8 cm3). After adjustment, surgical hematoma evacuation vs conservative treatment was not significantly associated with likelihood of better functional disability at 3 months (30.9% vs 35.5%; adjusted odds ratio [AOR], 0.94 [95% CI, 0.81 to 1.09], P = .43; adjusted risk difference [ARD], -3.7% [95% CI, -8.7% to 1.2%]) but was significantly associated with greater probability of survival at 3 months (78.3% vs 61.2%; AOR, 1.25 [95% CI, 1.07 to 1.45], P = .005; ARD, 18.5% [95% CI, 13.8% to 23.2%]) and at 12 months (71.7% vs 57.2%; AOR, 1.21 [95% CI, 1.03 to 1.42], P = .02; ARD, 17.0% [95% CI, 11.5% to 22.6%]). A volume range of 12 to 15 cm3 was identified; below this level, surgical hematoma evacuation was associated with lower likelihood of favorable functional outcome (volume ≤12 cm3, 30.6% vs 62.3% [P = .003]; ARD, -34.7% [-38.8% to -30.6%]; P value for interaction, .01), and above, it was associated with greater likelihood of survival (volume ≥15 cm3, 74.5% vs 45.1% [P < .001]; ARD, 28.2% [95% CI, 24.6% to 31.8%]; P value for interaction, .02)., Conclusions and Relevance: Among patients with cerebellar ICH, surgical hematoma evacuation, compared with conservative treatment, was not associated with improved functional outcome. Given the null primary outcome, investigation is necessary to establish whether there are differing associations based on hematoma volume.

Objective: To determine the occurrence of intracranial haemorrhagic complications (IHC) on heparin prophylaxis (low-dose subcutaneous heparin, LDSH) in primary spontaneous intracerebral haemorrhage (ICH) (not oral anticoagulation-associated ICH, non-OAC-ICH), vitamin K antagonist (VKA)-associated ICH and non-vitamin K antagonist oral anticoagulant (NOAC)-associated ICH., Methods: Retrospective cohort study (RETRACE) of 22 participating centres and prospective single-centre study with 1702 patients with VKA-associated or NOAC-associated ICH and 1022 patients with non-OAC-ICH with heparin prophylaxis between 2006 and 2015. Outcomes were defined as rates of IHC during hospital stay among patients with non-OAC-ICH, VKA-ICH and NOAC-ICH, mortality and functional outcome at 3 months between patients with ICH with and without IHC., Results: IHC occurred in 1.7% (42/2416) of patients with ICH. There were no differences in crude incidence rates among patients with VKA-ICH, NOAC-ICH and non-OAC-ICH (log-rank p=0.645; VKA-ICH: 27/1406 (1.9%), NOAC-ICH 1/130 (0.8%), non-OAC-ICH 14/880 (1.6%); p=0.577). Detailed analysis according to treatment exposure (days with and without LDSH) revealed no differences in incidence rates of IHC per 1000 patient-days (LDSH: 1.43 (1.04-1.93) vs non-LDSH: 1.32 (0.33-3.58), conditional maximum likelihood incidence rate ratio: 1.09 (0.38-4.43); p=0.953). Secondary outcomes showed differences in functional outcome (modified Rankin Scale=4-6: IHC: 29/37 (78.4%) vs non-IHC: 1213/2048 (59.2%); p=0.019) and mortality (IHC: 14/37 (37.8%) vs non-IHC: 485/2048 (23.7%); p=0.045) in disfavour of patients with IHC. Small ICH volume (OR: volume <4.4 mL: 0.18 (0.04-0.78); p=0.022) and low National Institutes of Health Stroke Scale (NIHSS) score on admission (OR: NIHSS <4: 0.29 (0.11-0.78); p=0.014) were significantly associated with fewer IHC., Conclusions: Heparin administration for venous thromboembolism (VTE) prophylaxis in patients with ICH appears to be safe regarding IHC among non-OAC-ICH, VKA-ICH and NOAC-ICH in this observational cohort analysis. Randomised controlled trials are needed to verify the safety and efficacy of heparin compared with other methods for VTE prevention., Competing Interests: Competing interests: JBK reports grants from Covidien (Medtronic), personal fees from Bayer, Pfizer and Sanofi. ME reports grants and other from Bayer, other from Boehringer Ingelheim, BMS/Pfizer and Daiichi Sankyo during the conduct of the study; grants from DFG, BMBF, EU, Corona Foundation and Fondation Leducq, other from Amgen, GSK, Novartis, Sanofi and Covidien. KGH reports grants and personal fees from Bayer, personal fees from Daiichi Sankyo, BMS, Pfizer, Boehringer Ingelheim, Sanofi-Aventis and Edwards Lifesciences, non-financial support from Getemed, personal fees from EIP Pharma and Medtronic. JS reports personal fees from Bayer, Pfizer/BMS, Daiichi and Boehringer Ingelheim. PAR reports personal fees from Boehringer Ingelheim, Bayer, Daiichi Sankyo and Pfizer. JCP reports personal fees from Boehringer Ingelheim and Pfizer. TR reports personal fees from BMS Pfizer, Bayer Healthcare, Daiichi Sankyo and Boehringer Ingelheim. JV reports grants from Medtronic and Boston Scientific and fees from Medtronic, St Jude, Boston Scientific, UCB, Merz, Allergan, Teva, Novartis and AbbVie. WM reports personal fees from Boehringer Ingelheim and Bayer Pharma. PK reports personal fees from Bayer, Boehringer Ingelheim, Daiichi Sankyo and Pfizer/Bristol-Myers Squibb. FE reports grants and personal fees from Boehringer Ingelheim, personal fees from Bayer Pharma, Pfizer Pharma, Bristol-Myers Squibb and Daiichi Sankyo. MN reports personal fees from Speaker’s fee Pfizer/BMS and Boehringer Ingelheim. PDS reports personal fees from Boehringer Ingelheim, Bayer, BMS/Pfizer, Daiichi and Medtronic. JG reports personal fees from Pfizer. UJK reports other from Daiichi Sankyo and Bayer. GRF reports personal fees from Bayer and Boehringer. CD reports speaking fees from Bayer, UCB, Daiichi Sankyo and Pfizer. JM reports personal fees from Boehringer Ingelheim and Bayer Healthcare. HN reports personal fees from Boehringer Ingelheim and Daiichi. JR reports personal fees from Bayer, Boehringer, Pfizer and Bristol Myers Squibb. MS reports grants from Deutsche Forschungsgemeinschaft (DFG), Bundesministerium für Bildung und Forschung (BMBF), and the European Union (EU) and fees from Bristol-Myers Squibb, GlaxoSmithKline, Merz Pharmaceuticals, Novartis Pharma, Orion Pharma, Pharmacia, Roche, Sanofi, Teva Pharma and UCB Pharma. GR reports personal fees from Boehringer Ingelheim, Pfizer and Bayer, grants from Daiichi. HB reports personal fees from Honoraria for lectures from Bayer Vital, Boehringer Ingelheim, Bristol-Myers Squibb and Daiichi Sankyo. HS reports grants from Bayer Healthcare. FP reports personal fees from Pfizer/BMS, Bayer, Boehringer Ingelheim and Daiichi Sankyo. JCW reports personal fees from Boehringer Ingelheim Pharma GmbH & Co. KGH reports personal fees from Daiichi Sankyo Pharma. AG reports personal fees from Daiichi Sankyo, Bayer, Boehringer Ingelheim and Bristol-Myers Squibb/Pfizer. GFH reports participation in the Respect-ESUS trial. SS reports personal fees from Boehringer Ingelheim and grants from Daiichi. HBH reports personal fees from Boehringer Ingelheim, Daiichi Sankyo and Novartis, grants from Medtronic., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Background and Purpose- Given inconclusive studies, it is debated whether clinical and imaging characteristics, as well as functional outcome, differ among patients with intracerebral hemorrhage (ICH) related to vitamin K antagonists (VKA) versus non-vitamin K antagonist (NOAC)-related ICH. Notably, clinical characteristics according to different NOAC agents and dosages are not established. Methods- Multicenter observational cohort study integrating individual patient data of 1328 patients with oral anticoagulation-associated ICH, including 190 NOAC-related ICH patients, recruited from 2011 to 2015 at 19 tertiary centers across Germany. Imaging, clinical characteristics, and 3-months modified Rankin Scale (mRS) outcomes were compared in NOAC- versus VKA-related ICH patients. Propensity score matching was conducted to adjust for clinically relevant differences in baseline parameters. Subgroup analyses were performed regarding NOAC agent, dosing and present clinically relevant anticoagulatory activity (last intake <12h/24h or NOAC level >30 ng/mL). Results- Despite older age in NOAC patients, there were no relevant differences in clinical and hematoma characteristics between NOAC- and VKA-related ICH regarding baseline hematoma volume (median [interquartile range]: NOAC, 14.7 [5.1-42.3] mL versus VKA, 16.4 [5.8-40.6] mL; P=0.33), rate of hematoma expansion (NOAC, 49/146 [33.6%] versus VKA, 235/688 [34.2%]; P=0.89), and the proportion of patients with unfavorable outcome at 3 months (mRS, 4-6: NOAC 126/179 [70.4%] versus VKA 473/682 [69.4%]; P=0.79). Subgroup analyses revealed that NOAC patients with clinically relevant anticoagulatory effect had higher rates of intraventricular hemorrhage (n/N [%]: present 52/109 [47.7%] versus absent 9/35 [25.7%]; P=0.022) and hematoma expansion (present 35/90 [38.9%] versus absent 5/30 [16.7%]; P=0.040), whereas type of NOAC agent or different NOAC-dosing regimens did not result in relevant differences in imaging characteristics or outcome. Conclusions- If effectively anticoagulated, there are no differences in hematoma characteristics and functional outcome among patients with NOAC- or VKA-related ICH. Clinical Trial Registration- URL: https://www.clinicaltrials.gov . Unique identifier: NCT03093233.

Background and Purpose: Brain perfusion measurement in the subacute phase of stroke may support therapeutic decisions. We evaluated whether arterial spin labeling (ASL), a noninvasive perfusion imaging technique based on magnetic resonance imaging (MRI), adds diagnostic and prognostic benefit to diffusion-weighted imaging (DWI) in subacute stroke., Methods: In a single-center imaging study, patients with DWI lesion(s) in the middle cerebral artery (MCA) territory were included. Onset to imaging time was ≤7 days and imaging included ASL and DWI sequences. Qualitative (standardized visual analysis) and quantitative perfusion analyses (region of interest analysis) were performed. Dichotomized early outcome (modified Rankin Scale [mRS] 0-2 vs. 3-6) was analyzed in two logistic regression models. Model 1 included DWI lesion volume, age, vascular pathology, admission NIHSS, and acute stroke treatment as covariates. Model 2 added the ASL-based perfusion pattern to Model 1. Receiver-operating-characteristic (ROC) and area-under-the-curve (AUC) were calculated for both models to assess their predictive power. The likelihood-ratio-test compared both models., Results: Thirty-eight patients were included (median age 70 years, admission NIHSS 4, onset to imaging time 67 hr, discharge mRS 2). Qualitative perfusion analysis yielded additional diagnostic information in 84% of the patients. In the quantitative analysis, AUC for outcome prediction was 0.88 (95% CI 0.77-0.99) for Model 1 and 0.97 (95% CI 0.91-1.00) for Model 2. Inclusion of perfusion data significantly improved performance and outcome prediction (p = 0.002) of stroke imaging., Conclusions: In patients with subacute stroke, our study showed that adding perfusion imaging to structural imaging and clinical data significantly improved outcome prediction. This highlights the usefulness of ASL and noninvasive perfusion biomarkers in stroke diagnosis and management., (© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.)

Importance: Moderate hypothermia in addition to early decompressive hemicraniectomy has been suggested to further reduce mortality and improve functional outcome in patients with malignant middle cerebral artery (MCA) stroke., Objective: To investigate whether moderate hypothermia vs standard treatment after early hemicraniectomy reduces mortality at day 14 in patients with malignant MCA stroke., Design, Setting, and Participants: This randomized clinical trial recruited patients from August 2011 through September 2015 at 6 German university hospitals with dedicated neurointensive care units. Of the patients treated with hemicraniectomy and assessed for eligibility, patients were randomly assigned to either standard care or moderate hypothermia. Data analysis was completed from December 2016 to June 2018., Interventions: Moderate hypothermia (temperature, 33.0 ± 1.0°C) was maintained for at least 72 hours immediately after hemicraniectomy., Main Outcomes and Measures: The primary outcome was mortality rate at day 14 compared with the Fisher exact test and expressed as odds ratio (ORs) with 95% CIs. Rates of patients with serious adverse events were estimated for the period of the first 14 days after hemicraniectomy and 12 months of follow-up. Secondary outcome measures included functional outcome at 12 months., Results: Of the 50 study participants, 24 were assigned to standard care and 26 to moderate hypothermia. Twenty-eight were male (56%); the mean (SD) patient age was 51.3 (6.6) years. Recruitment was suspended for safety concerns: 12 of 26 patients (46%) in the hypothermia group and 7 of 24 patients (29%) receiving standard care had at least 1 serious adverse event within 14 days (OR, 2.05 [95% CI, 0.56-8.00]; P = .26); after 12 months, rates of serious adverse events were 80% (n = 20 of 25) in the hypothermia group and 43% (n = 10 of 23) in the standard care group (hazard ratio, 2.54 [95% CI, 1.29-5.00]; P = .005). The mortality rate at day 14 was 19% (5 of 26 patients) in the hypothermia group and 13% (3 of 24 patients) in the group receiving standard care (OR, 1.65 [95% CI, 0.28-12.01]; P = .70). There was no significant difference regarding functional outcome after 12 months of follow-up., Interpretation: In patients with malignant MCA stroke, moderate hypothermia early after hemicraniectomy did not improve mortality and functional outcome compared with standard care, but may cause serious harm in this specific setting., Trial Registration: http://www.drks.de, identifier DRKS00000623.

Purpose: The quality and precision of post-mortem MRI microscopy may vary depending on the embedding medium used. To investigate this, our study evaluated the impact of 5 widely used media on: (1) image quality, (2) contrast of high spatial resolution gradient-echo (T 1 and T 2 * -weighted) MR images, (3) effective transverse relaxation rate (R 2 * ), and (4) quantitative susceptibility measurements (QSM) of post-mortem brain specimens., Methods: Five formaldehyde-fixed brain slices were scanned using 7.0T MRI in: (1) formaldehyde solution (formalin), (2) phosphate-buffered saline (PBS), (3) deuterium oxide (D 2 O), (4) perfluoropolyether (Galden), and (5) agarose gel. SNR and contrast-to-noise ratii (SNR/CNR) were calculated for cortex/white matter (WM) and basal ganglia/WM regions. In addition, median R 2 * and QSM values were extracted from caudate nucleus, putamen, globus pallidus, WM, and cortical regions., Results: PBS, Galden, and agarose returned higher SNR/CNR compared to formalin and D 2 O. Formalin fixation, and its use as embedding medium for scanning, increased tissue R 2 * . Imaging with agarose, D 2 O, and Galden returned lower R 2 * values than PBS (and formalin). No major QSM offsets were observed, although spatial variance was increased (with respect to R 2 * behaviors) for formalin and agarose., Conclusions: Embedding media affect gradient-echo image quality, R 2 * , and QSM in differing ways. In this study, PBS embedding was identified as the most stable experimental setup, although by a small margin. Agarose and Galden were preferred to formalin or D 2 O embedding. Formalin significantly increased R 2 * causing noisier data and increased QSM variance., (© 2018 International Society for Magnetic Resonance in Medicine.)

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method-the U-net-is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies.

Aims: Evidence is lacking regarding acute anticoagulation management in patients after intracerebral haemorrhage (ICH) with implanted mechanical heart valves (MHVs). Our objective was to investigate anticoagulation reversal and resumption strategies by evaluating incidences of haemorrhagic and thromboembolic complications, thereby defining an optimal time-window when to restart therapeutic anticoagulation (TA) in patients with MHV and ICH., Methods and Results: We pooled individual patient-data (n = 2504) from a nationwide multicentre cohort-study (RETRACE, conducted at 22 German centres) and eventually identified MHV-patients (n = 137) with anticoagulation-associated ICH for outcome analyses. The primary outcome consisted of major haemorrhagic complications analysed during hospital stay according to treatment exposure (restarted TA vs. no-TA). Secondary outcomes comprised thromboembolic complications, the composite outcome (haemorrhagic and thromboembolic complications), timing of TA, and mortality. Adjusted analyses involved propensity-score matching and multivariable cox-regressions to identify optimal timing of TA. In 66/137 (48%) of patients TA was restarted, being associated with increased haemorrhagic (TA = 17/66 (26%) vs. no-TA = 4/71 (6%); P < 0.01) and a trend to decreased thromboembolic complications (TA = 1/66 (2%) vs. no-TA = 7/71 (10%); P = 0.06). Controlling treatment crossovers provided an incidence rate-ratio [hazard ratio (HR) 10.31, 95% confidence interval (CI) 3.67-35.70; P < 0.01] in disadvantage of TA for haemorrhagic complications. Analyses of TA-timing displayed significant harm until Day 13 after ICH (HR 7.06, 95% CI 2.33-21.37; P < 0.01). The hazard for the composite-balancing both complications, was increased for restarted TA until Day 6 (HR 2.51, 95% CI 1.10-5.70; P = 0.03)., Conclusion: Restarting TA within less than 2 weeks after ICH in patients with MHV was associated with increased haemorrhagic complications. Optimal weighing-between least risks for thromboembolic and haemorrhagic complications-provided an earliest starting point of TA at Day 6, reserved only for patients at high thromboembolic risk.

Background and Purpose: Stroke imaging is pivotal for diagnosis and stratification of patients with acute ischemic stroke to treatment. The potential of combining multimodal information into reliable estimates of outcome learning calls for robust machine learning techniques with high flexibility and accuracy. We applied the novel extreme gradient boosting algorithm for multimodal magnetic resonance imaging-based infarct prediction., Methods: In a retrospective analysis of 195 patients with acute ischemic stroke, fluid-attenuated inversion recovery, diffusion-weighted imaging, and 10 perfusion parameters were derived from acute magnetic resonance imaging scans. They were integrated to predict final infarct as seen on follow-up T2-fluid-attenuated inversion recovery using the extreme gradient boosting and compared with a standard generalized linear model approach using cross-validation. Submodels for recanalization and persistent occlusion were calculated and were used to identify the important imaging markers. Performance in infarct prediction was analyzed with receiver operating characteristics. Resulting areas under the curve and accuracy rates were compared using Wilcoxon signed-rank test., Results: The extreme gradient boosting model demonstrated significantly higher performance in infarct prediction compared with generalized linear model in both cross-validation approaches: 5-folds ( P <10e-16) and leave-one-out ( P <0.015). The imaging parameters time-to-peak, mean transit time, time-to-maximum, and diffusion-weighted imaging were indicated as most valuable for infarct prediction by the systematic algorithm rating. Notably, the performance improvement was higher with 5-folds cross-validation approach than leave-one-out., Conclusions: We demonstrate extreme gradient boosting as a state-of-the-art method for clinically applicable multimodal magnetic resonance imaging infarct prediction in acute ischemic stroke. Our findings emphasize the role of perfusion parameters as important biomarkers for infarct prediction. The effect of cross-validation techniques on performance indicates that the intrapatient variability is expressed in nonlinear dynamics of the imaging modalities., (© 2018 American Heart Association, Inc.)

Background: In acute stroke, the magnetic resonance (MR) imaging-based mismatch concept is used to select patients with tissue at risk of infarction for reperfusion therapies. There is however a controversy if non-deconvolved or deconvolved perfusion weighted (PW) parameter maps perform better in tissue at risk prediction and which parameters and thresholds should be used to guide treatment decisions., Methods: In a group of 22 acute stroke patients with consecutive MR and quantitative positron emission tomography (PET) imaging, non-deconvolved parameters were validated with the gold standard for penumbral-flow (PF) detection 15O-water PET. Performance of PW parameters was assessed by a receiver operating characteristic curve analysis to identify the accuracy of each PWI map to detect the -upper PF threshold as defined by PET cerebral blood flow <20 mL/100 g/min., Results: Among normalized non-deconvolved parameters, PW-first moment without delay correction (FM without DC) > 3.6 s (area under the curve [AUC] = 0.89, interquartile range [IQR] 0.85-0.94), PW-maximum of the concentration curve (Cmax) < 0.66 (AUC = 0.92, IQR 0.84-0.96) and PW-time to peak (TTP) > 4.0 s (AUC = 0.92, IQR 0.87-0.94) perform significantly better than other non-deconvolved parameters to detect the PF threshold as defined by PET., Conclusions: Non-deconvolved parameters FM without DC, Cmax and TTP are an observer-independent alternative to established deconvolved parameters (e.g., Tmax) to guide treatment decisions in acute stroke., (© 2018 S. Karger AG, Basel.)

Aims: In Wilson disease (WD), T2/T2*-weighted (T2*w) MRI frequently shows hypointensity in the basal ganglia that is suggestive of paramagnetic deposits. It is currently unknown whether this hypointensity is related to copper or iron deposition. We examined the neuropathological correlates of this MRI pattern, particularly in relation to iron and copper concentrations., Methods: Brain slices from nine WD and six control cases were investigated using a 7T-MRI system. High-resolution T2*w images were acquired and R2* parametric maps were reconstructed using a multigradient recalled echo sequence. R2* was measured in the globus pallidus (GP) and the putamen. Corresponding histopathological sections containing the lentiform nucleus were examined using Turnbull iron staining, and double staining combining Turnbull with immunohistochemistry for macrophages or astrocytes. Quantitative densitometry of the iron staining as well as copper and iron concentrations were measured in the GP and putamen and correlated with R2* values., Results: T2*w hypointensity in the GP and/or putamen was apparent in WD cases and R2* values correlated with quantitative densitometry of iron staining. In WD, iron and copper concentrations were increased in the putamen compared to controls. R2* was correlated with the iron concentration in the GP and putamen, whereas no correlation was observed for the copper concentration. Patients with more pronounced pathological severity in the putamen displayed increased iron concentration, which correlated with an elevated number of iron-containing macrophages., Conclusions: T2/T2*w hypointensity observed in vivo in the basal ganglia of WD patients is related to iron rather than copper deposits., (© 2016 British Neuropathological Society.)

Background and Purpose: In acute stroke, arterial-input-function (AIF) determination is essential for obtaining perfusion estimates with dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging (DSC-MRI). Standard DSC-MRI postprocessing applies single AIF selection, ie, global AIF. Physiological considerations, however, suggest that a multiple AIFs selection method would improve perfusion estimates to detect penumbral flow. In this study, we developed a framework based on comparable DSC-MRI and positron emission tomography (PET) images to compare the two AIF selection approaches and assess their performance in penumbral flow detection in acute stroke., Methods: In a retrospective analysis of 17 sub(acute) stroke patients with consecutive MRI and PET scans, voxel-wise optimized AIFs were calculated based on the kinetic model as derived from both imaging modalities. Perfusion maps were calculated based on the optimized-AIF using two methodologies: (1) Global AIF and (2) multiple AIFs as identified by cluster analysis. Performance of penumbral-flow detection was tested by receiver-operating characteristics (ROC) curve analysis, ie, the area under the curve (AUC)., Results: Large variation of optimized AIFs across brain voxels demonstrated that there is no optimal single AIF. Subsequently, the multiple-AIF method (AUC range over all maps: .82-.90) outperformed the global AIF methodology (AUC .72-.85) significantly., Conclusions: We provide PET imaging-based evidence that a multiple AIF methodology is beneficial for penumbral flow detection in comparison with the standard global AIF methodology in acute stroke., (Copyright © 2017 by the American Society of Neuroimaging.)

Perfusion-weighted (PW) magnetic resonance imaging (MRI) is used to detect penumbral tissue in acute stroke, but the selection of optimal PW-maps and thresholds for tissue at risk detection remains a matter of debate. We validated the performance of PW-maps with 15O-water-positron emission tomography (PET) in a large comparative PET-MR cohort of acute stroke patients. In acute and subacute stroke patients with back-to-back MRI and PET imaging, PW-maps were validated with 15O-water-PET. We pooled two different cerebral blood flow (CBF) PET-maps to define the critical flow (CF) threshold, (i) quantitative (q)CBF-PET with the CF threshold <20 ml/100 g/min and (ii) normalized non-quantitative (nq)CBF-PET with a CF threshold of <70% (corresponding to <20 ml/100 g/min according to a previously published normogram). A receiver operating characteristic (ROC) curve analysis was performed to specify the accuracy and the optimal critical flow threshold of each PW-map as defined by PET. In 53 patients, (stroke to imaging: 9.8 h; PET to MRI: 52 min) PW-time-to-maximum (Tmax) with a threshold >6.1 s (AUC = 0.94) and non-deconvolved PW-time-to-peak (TTP) >4.8 s (AUC = 0.93) showed the best performance to detect the CF threshold as defined by PET. PW-Tmax with a threshold >6.1 s and TTP with a threshold >4.8 s are the most predictive in detecting the CF threshold for MR-based mismatch definition.

Background and Purpose: Identification of salvageable penumbra tissue by dynamic susceptibility contrast magnetic resonance imaging is a valuable tool for acute stroke patient stratification for treatment. However, prior studies have not attempted to combine the different perfusion maps into a predictive model. In this study, we established a multiparametric perfusion imaging model and cross-validated it using positron emission tomography perfusion for detection of penumbral flow., Methods: In a retrospective analysis of 17 subacute stroke patients with consecutive magnetic resonance imaging and H2O15 positron emission tomography scans, perfusion maps of cerebral blood flow, cerebral blood volume, mean transit time, time-to-maximum, and time-to-peak were constructed and combined using a generalized linear model (GLM). Both the GLM maps and the single perfusion maps alone were cross-validated with positron emission tomography-cerebral blood flow scans to predict penumbral flow on a voxel-wise level. Performance was tested by receiver-operating characteristics curve analysis, that is, the area under the curve, and the models' fits were compared using the likelihood ratio test., Results: The GLM demonstrated significantly improved model fit compared with each of the single perfusion maps ( P <1×e-5) and demonstrated higher performance, with an area under the curve of 0.91. However, the absolute difference between the performance of GLM and the best-performing single perfusion parameter (time-to-maximum) was relatively low (area under the curve difference =0.04)., Conclusions: Our results support a dynamic susceptibility contrast magnetic resonance imaging-based GLM as an improved model for penumbral flow prediction in stroke patients. With given perfusion maps, this model is a straightforward and observer-independent alternative for therapy stratification., (© 2017 American Heart Association, Inc.)

Purpose: Walsh ordering of Hadamard encoding-matrices and an additional averaging strategy are proposed for Hadamard-encoded pseudocontinuous arterial spin labeling (H-pCASL). In contrast to conventional H-pCASL the proposed method generates more perfusion-weighted images which are accessible already during a running experiment and even from incomplete sets of encoded images., Theory: Walsh-ordered Hadamard matrices consist of fully decodable Hadamard submatrices. At any time during a measurement these submatrices may yield perfusion-weighted images, even at runtime and with incomplete datasets. The usage of an additional so-called mirrored matrix for averaging leads to more decodable subboli., Methods: Perfusion-weighted images (five healthy volunteers) are generated using both a Walsh-ordered and a corresponding mirrored Hadamard matrix. To test their correctness they are compared with equivalent images from conventional multi postlabeling-delay (PLD) pCASL-measurements., Results: All predicted perfusion-weighted images could be generated and correlated very well with multi-PLD images. Already small subsets of encoded images, acquired early during the measurement, yielded perfusion-weighted images. The mirrored matrix generates more perfusion-weighted images without time penalty., Conclusion: Early access to perfusion-weighted images despite incomplete datasets allows dynamic adaptation of parameters and increases robustness against artifacts. Thus, the approach may be well suited for clinical applications, where pathologies demand rapid parameter adaptation and increase the chance of artifacts. Magn Reson Med 76:1814-1824, 2016. © 2015 International Society for Magnetic Resonance in Medicine., (© 2015 International Society for Magnetic Resonance in Medicine.)

Background and Purpose: Arterial spin labeling (ASL) is an MRI technique to measure cerebral blood flow (CBF) without the need of exogenous contrast agents and is thus a promising alternative to the clinical standard dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion imaging. Latest international guidelines encourage its application in the clinical setting. However, susceptibility-induced image distortions impair ASL with fast readout modules (eg Echo Planar Imaging, EPI; gradient and spin echo, GRASE). In the present study, we investigated the benefit of a distortion correction for ASL compared to DSC., Methods: A pulsed ASL (PASL) sequence combined with a 3D-GRASE readout at multiple inflow times (multi-TI) was used and was corrected for susceptibility distortions using a FMRIB Software Library (FSL) implemented tool TOPUP. We performed qualitative (three expert raters) and quantitative (volume of interest [VOI]-based) comparisons of ASL and DSC imaging in 13 patients with chronic steno-occlusive disease., Results: In the qualitative analysis, distortion correction of the images led to a strong increase in diagnostic precision of ASL compared to DSC in the anterior cerebral artery (ACA) perfusion territory, where the susceptibility artifact was most pronounced (specificity 8% vs. 75%). In the quantitative analysis, the correlation between ASL and DSC values increased for all perfusion territories with the best improvement for the ACA territory (for anterior, middle and posterior cerebral artery: ACA: rho -0.22 vs. 0.71; MCA: rho 0.58 vs. 0.76; PCA: rho 0.58 vs. 0.63)., Conclusions: We showed that susceptibility distortion correction strongly improves the comparability of multi-TI ASL 3D-GRASE to DSC in steno-occlusive disease. We suggest it to be implemented in ASL postprocessing routines., (Copyright © 2016 by the American Society of Neuroimaging.)

Background: Prompt diagnosis of vessel pathology and appropriate treatment of moyamoya vasculopathy (MMV) are essential to improve long-term prognosis. The aims of our study were to explore the diagnostic value of ultra-high-field (UHF) magnetic resonance imaging at 7.0 T in MMV patients and to compare the applicability of two different 7.0 T vessel imaging modalities to 3.0 T magnetic resonance angiography (MRA) and digital subtraction angiography (DSA)., Methods: In a World Health Organization-registered and prospective imaging trial, patients were investigated at 7.0 T magnetization-prepared rapid-acquisition gradient echo (MPRAGE)-MRA and time-of-flight (TOF)-MRA, 3.0 T TOF-MRA, and by DSA., Results: Six patients were included in our study and evaluated for MMV. 3.0 T TOF-MRA and 7.0 T MPRAGE-MRA were able to depict the complete major vascular tree and confirmed MMV-specific steno-occlusions of major intracranial arteries, as previously identified by DSA. 7.0 T TOF-MRA was limited to visualization of the circle of Willis as well as the internal carotid artery only. Donor vessels for bypass surgery (i.e., branches of superficial temporal artery) could be sufficiently visualized with all magnetic resonance modalities., Conclusions: Our results indicate that a specific 7.0 T vascular imaging protocol yields diagnostic information about vessel pathology in MMV that approximates conventional DSA. 7.0 T MPRAGE was superior to 7.0 T TOF-MRA due to shorter scanning times and better brain coverage. To date, however, limited availability of 7.0 T technology in medical facilities as well as technical and procedural constraints excludes a fair amount of patients from the clinical 7.0 T imaging process., (Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.)

To predict short-term outcome in acute ischemic stroke, we analyzed somatosensory evoked potentials (SEP) and biochemical parameters [neuron-specific enolase (NSE) and S100 protein] in a prospective study with serial measurement. In 31 patients with 1st middle cerebral artery infarction, serum NSE and S100 protein were measured daily between days 1 and 6 poststroke. The N20 and N70 components of the SEP (SEP20 and SEP70) were determined on days 1 and 6. SEP and biochemical markers in stroke patients were compared with a control group. Short-term outcome was assessed by the modified Rankin Scale (mRS) at days 7-10 and was dichotomized between good (mRS 0-2) and poor (mRS ≥3) outcome. Specificity and positive predictive value (PPV) were high at day 1 for SEP (SEP20: 100% for both; SEP70: 93 and 88%, respectively) compared with lower values for NSE (67 and 50%) and S100 (23 and 57%). In contrast, S100 showed the highest sensitivity at day 1 with 77% compared with a relatively low sensitivity of NSE (31%) and SEP (SEP20: 35%, SEP70: 47%). The biochemical markers showed an improving sensitivity over time with best values (>90%) between days 3 and 4 at the expense of a lower specificity. Specificity and PPV of SEP on day 6 was still 100% with sensitivity increasing up to 53% (SEP20) and 60% (SEP70). SEP could early differentiate between good and poor outcome and reliably predict poor outcome. Since biochemical markers and SEP complement each other in the prognosis of stroke, a combined application of these markers seems promising., (Copyright © 2016 the American Physiological Society.)

Background: With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch., Methods: In a retrospective study, patients from 2 centers with proven stroke with onset <12 h were included. The DWI lesion was segmented and overlaid on ADC and FLAIR images. rSI mean and SD, were calculated as follows: (mean ROI value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly., Results: In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and DWI-SD (0.91). The best prediction results based on the AUC were found for the final stratified and adjusted models of DWI-SD (0.94) and FLAIR-mean (0.96) and a multivariable DWI-FLAIR model (0.95). The adjusted visual DWI-FLAIR mismatch did not perform in a significantly worse manner (0.89). ADC-rSIs showed fair performance in all models., Conclusions: Quantitative DWI and FLAIR MRI biomarkers as well as the visual DWI-FLAIR mismatch provide excellent prediction of eligibility for thrombolysis in acute stroke, when easily obtainable clinical-radiological parameters are included in the prediction models., (© 2016 S. Karger AG, Basel.)

Objectives: Time-of-flight (TOF) magnetic-resonance-angiography (MRA) identifies vessel pathology in cerebrovascular disease. At 7.0 T, the clinical performance of TOF-MRA is constrained owing to radio frequency power deposition. We studied the diagnostic value of whole-brain MPRAGE-based MRA as an alternative imaging technique in comparison to the clinical standard 3.0 T TOF-MRA., Methods: Patients with stroke and/or moya-moya disease were included. TOF-MRA was performed at 3.0 T and MPRAGE-MRA at 7.0 T. Two radiologists rated the MRAs independently for overall quality and local arterial segment visualization. The identification of steno-occlusive pathology was reported for each protocol., Results: In 18 patients (9 females; 6 patients with moya-moya) 7.0 T MPRAGE-MRA provided better overall image quality and better distinction of small structures compared to 3.0 T TOF-MRA. These findings were pronounced in the proximal segments of the anterior cerebral artery (A1), middle cerebral artery (M1, M2), posterior cerebral artery (P1) and the posterior communicating artery. Seven steno-occlusive findings were identified by both imaging protocols., Conclusions: For clinical studies using ultrahigh field MRI, 7.0 T MPRAGE-MRA provides a suitable alternative to TOF-MRA imaging to identify brain vessel pathology and yields simultaneous structural brain imaging within clinically feasible acquisition times., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Background and Purpose: Dynamic susceptibility-weighted contrast-enhanced (DSC) magnetic resonance imaging (MRI) is used to identify the tissue-at-risk in acute stroke, but the choice of optimal DSC postprocessing in the clinical setting remains a matter of debate. Using 15O-water positron emission tomography (PET), we validated the performance of 2 common deconvolution methods for DSC-MRI., Methods: In (sub)acute stroke patients with consecutive MRI and PET imaging, DSC maps were calculated applying 2 deconvolution methods, standard and block-circulant single value decomposition. We used 2 standardized analysis methods, a region of interest-based and a voxel-based analysis, where PET cerebral blood flow masks of <20 mL/100 g per minute (penumbral flow) and gray matter masks were overlaid on DSC parameter maps. For both methods, receiver operating characteristic curve analysis was performed to identify the accuracy of each DSC-MR map for the detection of PET penumbral flow., Results: In 18 data sets (median time after stroke onset: 18 hours; median time PET to MRI: 101 minutes), block-circulant single value decomposition showed significantly better performance to detect PET penumbral flow only for mean transit time maps. Time-to-maximum (Tmax) had the highest performance independent of the deconvolution method., Conclusions: Block-circulant single value decomposition seems only significantly beneficial for mean transit time maps in (sub)acute stroke. Tmax is likely the most stable deconvolved parameter for the detection of tissue-at-risk using DSC-MRI., (© 2015 American Heart Association, Inc.)