A system for providing a prediction of a representation of an examination region that was generated using a medical imaging technique involving a contrast agent, comprising at least one processor programmed or configured to: receive a first representation (F'1) in frequency space of an examination region of an examination object, wherein the first representation (F'1) comprises a representation of the examination region without an amount of a contrast agent administered during a medical imaging technique or with a first amount of the contrast agent administered during the medical imaging technique; receive a second representation (F'2) in the frequency space of the examination region of the examination object, wherein the second representation (F'2) comprises a representation of the examination region with a second amount of the contrast agent administered during the medical imaging technique, wherein the second amount of the contrast agent is different from the first amount of the contrast agent; provide an input to a predictive machine learning model, wherein the input to the predictive machine learning model comprises at least a portion of the first representation (F'1) including a center of the frequency space and at least a portion of the second representation (F'2) including a center of the frequency space, wherein the predictive machine learning model comprises a trained machine learning model configured to provide, as an output, a prediction of a representation (F'3*) in the frequency space of the examination region with a third amount of the contrast agent administered during the medical imaging technique, wherein the third amount of the contrast agent is greater than the first amount of the contrast agent and the second amount of the contrast agent; receive the output of the predictive machine learning model based on the input; convert the output of the predictive machine learning model to a predicted representation (O'3*) in real space of the examination region of the examination object; and provide the predicted representation (O'3*) in the real space of the examination region of the examination object. receive a first representation (F'1) in frequency space of an examination region of an examination object, wherein the first representation (F'1) comprises a representation of the examination region without an amount of a contrast agent administered during a medical imaging technique or with a first amount of the contrast agent administered during the medical imaging technique; receive a second representation (F'2) in the frequency space of the examination region of the examination object, wherein the second representation (F'2) comprises a representation of the examination region with a second amount of the contrast agent administered during the medical imaging technique, wherein the second amount of the contrast agent is different from the first amount of the contrast agent; provide an input to a predictive machine learning model, wherein the input to the predictive machine learning model comprises at least a portion of the first representation (F'1) including a center of the frequency space and at least a portion of the second representation (F'2) including a center of the frequency space, wherein the predictive machine learning model comprises a trained machine learning model configured to provide, as an output, a prediction of a representation (F'3*) in the frequency space of the examination region with a third amount of the contrast agent administered during the medical imaging technique, wherein the third amount of the contrast agent is greater than the first amount of the contrast agent and the second amount of the contrast agent; receive the output of the predictive machine learning model based on the input; convert the output of the predictive machine learning model to a predicted representation (O'3*) in real space of the examination region of the examination object; and provide the predicted representation (O'3*) in the real space of the examination region of the examination object.