1. A method of image processing, including: a first, offline process, performed prior to processing one or more input images or image volumes, wherein the first, offline process is configured to create paired data based on measured and synthetic data, wherein a first of the paired data consists of created target data, and wherein a second of the paired data consists of an observable result based on output from a measurement device, comprising: obtaining one or more image components from data representing one or more images or image volumes, the obtaining comprising: normalizing and pre-processing the data to obtain processed data with target gray scale and sampling characteristics; extracting features from the processed data to obtain a set of extracted features; and performing model-based prediction derived from the measured and synthetic data, or derivatives thereof, using at least one neural network regression model, including a deep neural network model, based on the set of extracted features, to predict one or more components based on the target data, wherein the synthetic data, or derivatives thereof, comprises the data representing one or more images or image volumes or data representing the one or more images or image volumes, or both, with the one or more components removed, and data representing one or more simulated structures incorporated into the data representing one or more images or image volumes or data representing the one or more images or image volumes, or both, with the one or more components removed; and a second process, performed on the one or more input images or image volumes, and performed following the first process, comprising: removing from data representing the one or more input images or image volumes at least one of the one or more components predicted by the model-based prediction derived through a training process using a combination of the measured and synthetic data, to obtain one or more images and/or image volumes having suppressed components.