Deep learning based automated left atrial segmentation and flow quantification of real time phase contrast MRI in patients with atrial fibrillation

Figures

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  Fig. 1. LA RTPC plane is placed parallel to the mitral valve. 2D RTPC raw data is reconstructed into magnitude and phase cardiac data based on acquisition time (366 frames total for this patient). The left atrium is contoured to quantify blood flow velocities. LA- left atrium

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  Fig. 2. RTPC magnitude volume data (2D image data + time) is preprocessed and inputted into a dense U-net architecture convolutional neural network. The network features 2,4,6,8, 12 dense (outputs of each layer feed forward as inputs to subsequent) layers per layer with each layer consisting of batch, relu, convolution and drop out with constant feature map size of 12. Max-pooling was utilized for down sampling within the encoder structure and transposed convolution within the decoder structure. The final layer consisted of a 1 × 1 × 1 convolution and softmax function to generate a probability of each voxel (2D spatial + time) of the magnitude input image volume belonging to the left atrium or the background for each time frame

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  Fig. 3. Best (A), median (B), and worst (C) dice score performing frames over the entire patient cohorot. Semi-manual (blue) is diplayed over AI (red) contours. Each frame comes from a different patient

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  Fig. 4. Flow curves for the patients having the best (top), median (middle), and worst (bottom) dice score frame. AI (red) displayed excellent agreement with Semi-Manual (blue) flow values over time, despite low performing frames

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  Fig. 5. Bland Altman analysis of agreement between semi-manual and AI per heartbeat measurement of peak velocity, mean velocity, net flow, and stasis