It is important that unruptured intracranial aneurysms (UIAs) are detected early for rupture risk and treatment assessment. Radiologists usually visually diagnose UIAs on Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) or contrast-enhanced Computed Tomography Angiographs (CTAs). Several automatic UIA detection methods using voxel-based deep learning techniques have been developed, but are limited to a single modality. We propose modality-independent UIA detection by deep learning using mesh surface representations of brain vasculature. Vessels from a training set of 90 brain TOF-MRAs with UIAs were automatically segmented and converted to triangular surface meshes. Vertices and corresponding edges on the surface meshes were labelled as either vessel or aneurysm. A mesh convolutional neural network was trained using the labeled vessel surface meshes as input with a weighted cross-entropy loss function. The network was a U-Net style architecture with convolutional and pooling layers, which operates on mesh edges. The trained network predicted edges on vessel surface meshes, which corresponded to UIAs in a test set of 10 TOF-MRAs and a separate test set of 10 CTAs. UIAs were detected in the test MRAs with an average sensitivity of 65% and an average false positive count/scan of 1.8 and in the test CTAs, with a sensitivity of 65% and a false positive count of 4.1. Using vessel surface meshes it is possible to detect UIAs in TOF-MRAs and CTAs with comparable performance to state-of-the-art UIA detection algorithms. This may aid radiologists in automatic UIA detection without requiring the same image modality or protocol for follow-up imaging.
Both internal carotid arteries (ICA) distribute blood via the anterior (ACA) and middle (MCA) cerebral arteries to the anterior part of the brain. Asymmetry of the pre-communicating part (A1-segment) of the ACA is common and is related to intracranial aneurysm formation. It is unknown if A1 asymmetry is also related to blood flow changes in the dominant A1 segment and the ipsilateral ICA and MCA. This study aims to relate artery diameters of both ICAs, M1- segments MCA and A1-segments ACA to blood-flow distribution in 10 subjects with symmetric A1s versus 10 with asymmetric A1s. Diameter measurements of the ICA (C3 and C7 segments), M1 and A1-segments were performed manually on the time-of flight magnetic resonance angiography (TOF-MRA) using an in-house developed tool. 4D phase-contrast MR imaging (PC-MRI)datasets were analyzed using CAAS software. The asymmetric group had on average 43% (range 26–84) asymmetry of the A1-segments and this asymmetry was directly related to the right-left flow difference (R2=0.890). The asymmetric group also had increased diameters and blood-flow for all ICA, M1 and A1 segments on the dominant A1 side. In conclusion, this preliminary study showed that asymmetry in A1 diameter is directly associated with increased blood-flow in the ICA, MCA and ACA on the dominant side. Our findings should be confirmed in a larger population which will also help to find an ideal cut-off in asymmetry diameter measurement that reflects a statistically significant difference in blood flow and velocity.
Deep learning approaches may help radiologists in the early diagnosis and timely treatment of cerebrovascular diseases. Accurate cerebral vessel segmentation of Time-of-Flight Magnetic Resonance Angiographs (TOFMRAs) is an essential step in this process. This study investigates deep learning approaches for automatic, fast and accurate cerebrovascular segmentation for TOF-MRAs. The performance of several data augmentation and selection methods for training a 2D and 3D U-Net for vessel segmentation was investigated in five experiments: a) without augmentation, b) Gaussian blur, c) rotation and flipping, d) Gaussian blur, rotation and flipping and e) different input patch sizes. All experiments were performed by patch-training both a 2D and 3D U-Net and predicted on a test set of MRAs. Ground truth was manually defined using an interactive threshold and region growing method. The performance was evaluated using the Dice Similarity Coefficient (DSC), Modified Hausdor
Distance and Volumetric Similarity, between the predicted images and the interactively defined ground truth. The segmentation performance of all trained networks on the test set was found to be good, with DSC scores ranging from 0.72 to 0.83. Both the 2D and 3D U-Net had the best segmentation performance with Gaussian blur, rotation and flipping compared to other experiments without augmentation or only one of those augmentation techniques. Additionally, training on larger patches or slices gave optimal segmentation results. In conclusion, vessel segmentation can be optimally performed on TOF-MRAs using a trained 3D U-Net on larger patches, where data augmentation including Gaussian blur, rotation and flipping was performed on the training data.
Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) enable visualization and analysis of cerebral arteries. This analysis may indicate normal variation of the configuration of the cerebrovascular system or vessel abnormalities, such as aneurysms. A model would be useful to represent normal cerebrovascular structure and variabilities in a healthy population and to differentiate from abnormalities. Current anomaly detection using autoencoding convolutional neural networks usually use a voxelwise mean-error for optimization. We propose optimizing a variational-autoencoder (VAE) with structural similarity loss (SSIM) for TOF-MRA reconstruction. A patch-trained 2D fully-convolutional VAE was optimized for TOF-MRA reconstruction by comparing vessel segmentations of original and reconstructed MRAs. The method was trained and tested on two datasets: the IXI dataset, and a subset from the ADAM challenge. Both trained networks were tested on a dataset including subjects with aneurysms. We compared VAE optimization with L2-loss and SSIM-loss. Performance was evaluated between original and reconstructed MRAs using mean square error, mean-SSIM, peak-signal-to-noise-ratio and dice similarity index (DSI) of segmented vessels. The L2-optimized VAE outperforms SSIM, with improved reconstruction metrics and DSIs for both datasets. Optimization using SSIM performed best for visual image quality, but with discrepancy in quantitative reconstruction and vascular segmentation. The IXI dataset had overall better performance, potentially due to the larger, more diverse training data. Reconstruction metrics, including SSIM, were lower for MRAs including aneurysms. A SSIM-optimized VAE improved the visual perceptive image quality of TOF-MRA reconstructions. A L2-optimized VAE performed best for TOF-MRA reconstruction, where the vascular segmentation is important. SSIM is a potential metric for anomaly detection of MRAs.
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