Devices, systems, and methods for deep-learning kernel-based scatter estimation and correction

A method for training one or more machine-learning models, the method comprising:

• obtaining a training dataset for a scatter model, wherein the training dataset includes projection data, radiographic-image data, and target data, wherein the target data define a scatter distribution, and wherein the scatter model accepts, as inputs, a set of parameters and projection data;
• applying the projection data and the radiographic-image data to one or more machine-learning models to generate the set of parameters for the scatter model;
• using the scatter model to generate, based on the set of parameters and on the projection data, output scatter-distribution data;
• applying the output scatter-distribution data and the target data to a loss function to obtain a loss value;
• and
• updating the one or more machine-learning models based on the loss value.

An information processing apparatus comprising:

• one or more computer-readable storage media;
• and
• one or more processors, wherein the one or more processors cooperate with the one or more computer-readable storage media to cause the information processing apparatus to
• obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan;
• apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter model;
• apply the scatter model, using the set of parameters and the set of projection data as inputs, to obtain scatter-distribution data;
• and
• perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.