Automated detection of water-fat swaps in Dixon magnetic resonance imaging

A non-transitory computer readable medium product storing machine executable instructions for execution by a processor controlling a medical system and a convolutional neural network previously trained using training data comprising training Dixon magnetic resonance images and training water-fat swap masks, wherein the convolutional neural network is configured for receiving an initial Dixon magnetic resonance image as input, wherein the convolutional neural network is configured for identifying one or more water-fat swap regions in the initial Dixon magnetic resonance image, wherein execution of the machine executable instructions causes the processor to:

• receive the initial Dixon magnetic resonance image;
• and
• receive the one or more water-fat swap regions from the convolutional neural network in response to inputting the initial Dixon magnetic resonance image into the convolutional neural network.

A method of training a convolutional neural network, wherein the convolutional neural network is configured for receiving an initial Dixon magnetic resonance image as input, wherein the convolutional neural network is configured for identifying one or more water-fat swap regions in the initial Dixon magnetic resonance image, wherein the method comprises

• receiving training data, wherein each of the training data comprises a training Dixon magnetic resonance image and a training water-fat swap mask, wherein the training Dixon magnetic resonance image comprises the one or more water-fat swap regions, wherein the training water-fat swap mask identifies a ground truth location of the one or more water-fat swap regions;
• and
• training the convolutional neural network using the training data.