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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

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 performa nce 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.

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.

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

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.

Pulsed: arterial spin labeling (PASL) at multiple inflow times (multi-TIs) is advantageous for the measurement of brain perfusion in patients with long arterial transit tittles (ATTs) as in steno-occlusive disease, because bolus-arrival-time can be measured and blood flow measurements can be corrected accordingly. Owing to its increased signal-to-noise ratio, a combination with a three-dimensional gradient and spin echo (GRASE) readout allows acquiring a sufficient number of multi-Tls within a clinically feasible acquisition time of 5 minutes. We compared this technique with the clinical standard dynamic susceptibility-weighted contrast-enhanced imaging magnetic resonance imaging in patients with unilateral stenosis >70% of the internal carotid or middle cerebral artery (MCA) at 3 Tesla. We performed qualitative (assessment by three expert raters) and quantitative (region of interest (ROI)/volume of interest (VOI) based) comparisons. In 43 patients, multi-TI PASL-GRASE showed perfusion alterations with moderate accuracy in the qualitative analysis. Quantitatively, moderate correlation coefficients were found for the MCA territory (ROI based: r=0.52, VOI based: r=0.48). In the anterior cerebral artery (ACA) territory, a readout related right-sided susceptibility artifact impaired correlation (ROI based: r=0.29, VOI based: r=0.34). Arterial transit delay artifacts were found only in 12% of patients. In conclusion, multi-TI PASL-GRASE can correct for arterial transit delay in patients with long ATTs. These results are promising for the transfer of ASL to the clinical practice.

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, 2015. © 2015 Wiley Periodicals, Inc.

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.