Apparatus and method for medical image reconstruction using deep learning for computed tomography (CT) image noise and artifacts reduction

A medical image processing apparatus, comprising:

• processing circuitry configured to
• receive an image of an object based on radiation data detected at a detector,
• select, for reducing noise in the image, a neural network based on an anatomical structure corresponding to the image, from a plurality of neural networks, wherein the plurality of neural networks has been trained for noise reduction with images of corresponding anatomical structures, and the selected neural network has been trained with multiple images including the anatomical structure, and
• apply the selected neural network to the image to generate a processed image as an output of the selected neural network,
• wherein the processing circuitry is further configured to
• train the neural network using training data and a function that represents a disagreement between pairs of data as an error value, the training data including pairs in which a pair includes defect-exhibiting data paired with corresponding defect-minimized data, and the neural network is trained by performing, for each of the pairs, the steps of
• applying the neural network to defect-exhibiting data of a pair to generate network processed data,
• calculating, using the function, the error value between the network processed data and the defect-minimized data of the pair,
• updating, based on the calculated error value, weighting coefficients of the neural network, and
• repeating the steps of applying, calculating, and updating using respective pairs of the training data until one or more stopping criteria are satisfied.

A medical image processing apparatus, comprising:

• processing circuitry configured to
• receive an image of an object based on radiation data detected at a detector,
• select, for reducing noise in the image, a neural network based on an anatomical structure corresponding to the image, from a plurality of neural networks, wherein the plurality of neural networks has been trained for noise reduction with images of corresponding anatomical structures, and the selected neural network has been trained with multiple images including the anatomical structure, and
• apply the selected neural network to the image to generate a processed image as an output of the selected neural network,
• wherein the trained neural networks are obtained in a process including:
• training the neural network using training data and a function that represents a disagreement between pairs of data as an error value, the training data including pairs in which a pair includes defect-exhibiting data paired with corresponding defect-minimized data, and the neural network is trained by performing, for each of the pairs, the steps of
• applying the neural network to defect-exhibiting data of a pair to generate network processed data,
• calculating, using the function, the error value between the network processed data and the defect-minimized data of the pair,
• updating, based on the calculated error value, weighting coefficients of the neural network, and
• repeating the steps of applying, calculating, and updating using respective pairs of the training data until one or more stopping criteria are satisfied.