Generating neural networks tailored to optimize specific medical image properties using novel loss functions

A method, comprising:

• training, by a system comprising a processor, a neural network to generate a reconstructed version of a computed tomography image comprising one or more optimized properties relative to the computed tomography image using a loss function tailored to control error in output data of the neural network based on a defined subset of variables associated with the computed tomography data;
• and
• generating, by the system, a trained neural network model as a result of the training.

A non-transitory machine-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:

• training a neural network to generate a reconstructed version of a computed tomography image comprising one or more optimized properties relative to the computed tomography image using a loss function tailored to control error in output data of the neural network based on one or more defined components, the one or more defined components comprising at least one of a frequency component or a spatial feature component;
• and
• generating a trained neural network model as a result of the training.