Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease

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

Rapid parametric mapping of the longitudinal relaxation time t1 using two-dimensional variable flip angle magnetic resonance imaging at 1.5 Tesla, 3 Tesla, and 7 Tesla

INTRODUCTION: Visual but subjective reading of longitudinal relaxation time (T1) weighted magnetic resonance images is commonly used for the detection of brain pathologies. For this non-quantitative measure, diagnostic quality depends on hardware configuration, imaging parameters, radio frequency transmission field (B1+) uniformity, as well as observer experience. Parametric quantification of the tissue T1 relaxation parameter offsets the propensity for these effects, but is typically time consuming. For this reason, this study examines the feasibility of rapid 2D T1 quantification using a variable flip angles (VFA) approach at magnetic field strengths of 1.5 Tesla, 3 Tesla, and 7 Tesla. These efforts include validation in phantom experiments and application for brain T1 mapping. METHODS: T1 quantification included simulations of the Bloch equations to correct for slice profile imperfections, and a correction for B1+. Fast gradient echo acquisitions were conducted using three adjusted flip angles for the proposed T1 quantification approach that was benchmarked against slice profile uncorrected 2D VFA and an inversion-recovery spin-echo based reference method. Brain T1 mapping was performed in six healthy subjects, one multiple sclerosis patient, and one stroke patient. RESULTS: Phantom experiments showed a mean T1 estimation error of (-63±1.5)% for slice profile uncorrected 2D VFA and (0.2±1.4)% for the proposed approach compared to the reference method. Scan time for single slice T1 mapping including B1+ mapping could be reduced to 5 seconds using an in-plane resolution of (2×2) mm2, which equals a scan time reduction of more than 99% compared to the reference method. CONCLUSION: Our results demonstrate that rapid 2D T1 quantification using a variable flip angle approach is feasible at 1.5T/3T/7T. It represents a valuable alternative for rapid T1 mapping due to the gain in speed versus conventional approaches. This progress may serve to enhance the capabilities of parametric MR based lesion detection and brain tissue characterization

Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

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

Toward sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks

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

Ultrahochfeld-MRT im Kontext neurologischer Erkrankungen [Ultrahigh field MRI in context of neurological diseases]

Ultrahigh field magnetic resonance imaging (UHF-MRI) has recently gained substantial scientific interest. At field strengths of 7 Tesla (T) and higher UHF-MRI provides unprecedented spatial resolution due to an increased signal-to-noise ratio (SNR). The UHF-MRI method has been successfully applied in various neurological disorders. In neuroinflammatory diseases UHF-MRI has already provided a detailed insight into individual pathological disease processes and elucidated differential diagnoses of several disease entities, e.g. multiple sclerosis (MS), neuromyelitis optica (NMO) and Susac's syndrome. The excellent depiction of normal blood vessels, vessel abnormalities and infarct morphology by UHF-MRI can be utilized in vascular diseases. Detailed imaging of the hippocampus in Alzheimer's disease and the substantia nigra in Parkinson's disease as well as sensitivity to iron depositions could be valuable in neurodegenerative diseases. Current UHF-MRI studies still suffer from small sample sizes, selection bias or propensity to image artefacts. In addition, the increasing clinical relevance of 3T-MRI has not been sufficiently appreciated in previous studies. Although UHF-MRI is only available at a small number of medical research centers it could provide a high-end diagnostic tool for healthcare optimization in the foreseeable future. The potential of UHF-MRI still has to be carefully validated by profound prospective research to define its place in future medicine

Brain iron accumulation in Wilson disease: a post-mortem 7 Tesla MRI - histopathological study

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 correlate 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 multi-gradient 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 to 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

A randomised controlled trial of antiplatelet therapy in combination with Rt-PA thrombolysis in ischemic stroke: rationale and design of the ARTIS-Trial

<p>Abstract</p> <p>Background</p> <p>Thrombolysis with intravenous rt-PA is currently the only approved acute therapy for ischemic stroke. Re-occlusion after initial recanalization occurs in up to 34% in patients treated with rt-PA, probably caused by platelet activation. In acute myocardial infarction, the combination of thrombolysis and antiplatelet therapy leads to a greater reduction of mortality compared to thrombolysis alone. In patients with acute ischemic stroke, several studies showed that patients already on antiplatelet treatment prior to thrombolysis had an equal or even better outcome compared to patients without prior antiplatelet treatment, despite an increased risk of intracerebral bleeding. Based on the fear of intracerebral haemorrhage, current international guidelines recommend postponing antiplatelet therapy until 24 hours after thrombolysis. Remarkably, prior use of antiplatelet therapy is not a contra-indication for thrombolysis. We hypothesize that antiplatelet therapy in combination with rt-PA thrombolysis will improve outcome by enhancing fibrinolysis and preventing re-occlusion.</p> <p>Methods/Design</p> <p>ARTIS is a randomised multi-center controlled trial with blind endpoint assessment. Our objective is to investigate whether immediate addition of aspirin to rt-PA thrombolysis improves functional outcome in ischemic stroke. Patients with acute ischemic stroke eligible for rt-PA thrombolysis are randomised to receive 300 mg aspirin within 1.5 hours after start of thrombolysis or standard care, consisting of antiplatelet therapy after 24 hours. Primary outcome is poor functional health at 3 months follow-up (modified Rankin Scale 3 - 6).</p> <p>Discussion</p> <p>This is the first clinical trial investigating the combination of rt-PA and acute aspirin by means of a simple and cheap adjustment of current antiplatelet regimen. We expect the net benefit of improved functional outcome will overcome the possible slightly increased risk of intracerebral haemorrhage.</p> <p>Trial registration</p> <p>The Netherlands National Trial Register NTR822. The condensed rationale of the ARTIS-Trial has already been published in Cerebrovascular Diseases.</p

Limbic Epileptogenesis in a Mouse Model of Fragile X Syndrome

Fragile X syndrome (FXS), caused by silencing of the Fmr1 gene, is the most common form of inherited mental retardation. Epilepsy is reported to occur in 20–25% of individuals with FXS. However, no overall increased excitability has been reported in Fmr1 knockout (KO) mice, except for increased sensitivity to auditory stimulation. Here, we report that kindling increased the expressions of Fmr1 mRNA and protein in the forebrain of wild-type (WT) mice. Kindling development was dramatically accelerated in Fmr1 KO mice, and Fmr1 KO mice also displayed prolonged electrographic seizures during kindling and more severe mossy fiber sprouting after kindling. The accelerated rate of kindling was partially repressed by inhibiting N-methyl-D-aspartic acid receptor (NMDAR) with MK-801 or mGluR5 receptor with 2-methyl-6-(phenylethynyl)-pyridine (MPEP). The rate of kindling development in WT was not effected by MPEP, however, suggesting that FMRP normally suppresses epileptogenic signaling downstream of metabolic glutamate receptors. Our findings reveal that FMRP plays a critical role in suppressing limbic epileptogenesis and predict that the enhanced susceptibility of patients with FXS to epilepsy is a direct consequence of the loss of an important homeostatic factor that mitigates vulnerability to excessive neuronal excitation