In this paper, we introduce a deep residual learning approach to extract brain vessels from contrast-enhanced(CE) magnetic resonance images. Our experiment results show that we can successfully achieve and visualize brain vessel information from CE MRI without magnetic resonance angiography and magnetic resonance venography(MRA/V) that are currently used for brain vessel extraction.
In CE T1-weighted scans, a contrast agent(usually gadolinium) is administered to increase the signal intensity of vessels and surrounding tissues, and thereby discover abnormalities in tissue. We utilized the relatively high intensity of vessels in CE T1-weighted images to extract them. CE T1-weighted images were scanned from a 3.0-T MRI Scanner(Ingenia CX, Philips Medical Systems, Best, The Netherlands) with an eight-channel sensitivity-encoding head coil using 3D spoiled gradient-echo sequence. The image acquisition parameters for were as follows : FOV = 20-24cm; matrix = 240x240; section thickness = 1mm. A total of 5 subject data were available and we selectively used 90 consecutive slices in axial direction from each subject as training data.
First, brain vessel masks were created using MIPAV software5. We manually segmented vessels on every 2D axial slice and concatenated these to make a 3D vessel mask. Using this mask, brain vessels in the original data were suppressed to zero intensity to obtain images that differ from original images only in vessel region. Next, the original dataset and vessel-suppressed dataset were fed as input and output of our deep CNN so that the network could suppress brain vessels by learning their non-linear features. Our deep CNN is depicted in Figure 1. It capitalizes on its deeply stacked 25 convolutional layers so that the cascaded filters can efficiently exploit contextual information from input data. In addition, the elementwise skip connection between input and output of the last convolutional layer helps the network to converge faster since it helps the network to learn only the residuals, which are brain vessels in our case. This type of architecture can be effectively used when input and output are highly correlated6, which makes it suitable for our vessel extracting problem. Mean squared error between the ground truth and network output was used as a loss function to train the network.
Our network was trained on patches of size 31x31x31. Each convolutional layer consists of kernels of size 3x3x3 with 64 feature maps except the last layer which has only 1 feature map to match the output size with the ground truth. Batch normalization and rectified linear unit(ReLU) activation were performed after each convolutional layer except the last one. Kernel weights were updated by backpropagation using Adam optimizer. Once training is done, our proposed network is capable of suppressing the brain vessels of unseen CE-GRE data to zero intensity. The network output of this test data was then subtracted from the original test data to extract brain vessels(Figure 2). Visualization of the extracted vessels was acquired using maximum intensity projection(MIP) on axial, sagittal, and coronal planes.
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