Predicting scattered signal of x-ray, and correcting scattered beam

An apparatus for predicting a scattered signal of an X-ray for an examination object, the apparatus comprising:

• a first obtaining module, operable to scan each phantom in a scanning manner in which a scattering degree of each phantom in a plurality of phantoms is less than a reference scattering degree, so as to obtain first projection data of each phantom, wherein the scanning of each phantom comprises:
• performing segmented scanning on each of the plurality of phantoms with a first width less than a reference scanning width under a plurality of different scanning conditions to collect projection data of each first segment of each phantom with the first width under each scanning condition, wherein the reference scanning width is a width used when standard clinical scanning is performed;
• and
• obtaining the first projection data of each phantom under each scanning condition by connecting the projection data of each first segment of each phantom collected under each scanning condition;
• a second obtaining module, operable to scan each phantom in a scanning manner in which the scattering degree of each phantom in the plurality of phantoms is equal to the reference scattering degree, so as to obtain second projection data of each phantom;
• a subtraction module, operable to obtain a real scattered signal of each phantom by subtracting the first projection data of the phantom from the second projection data of the phantom;
• a training module, operable to train a learning model based on the second projection data of each phantom and the real scattered signal of each phantom to obtain a trained learning model, wherein the training comprises:
• performing pre-calculation processing on the second projection data of each phantom based on a physical model to obtain a calculated scattered signal of each phantom;
• and
• training the learning model by using the calculated scattered signal of each phantom and the real scattered signal of each phantom as training data;
• and
• a prediction module, operable to apply the trained learning model to projection data of the X-ray for the examination object to predict the scattered signal in the projection data of the X-ray.

A method for correcting a scattered beam of an X-ray for an examination object, the method comprising:

• scanning, by a first obtaining module, each phantom in a scanning manner in which a scattering degree of each phantom in a plurality of phantoms is less than a reference scattering degree, so as to obtain first projection data of each phantom, wherein the scanning of each phantom comprises:
• performing segmented scanning on each of the plurality of phantoms with a first width less than a reference scanning width under a plurality of different scanning conditions to collect projection data of each first segment of each phantom with the first width under each scanning condition, wherein the reference scanning width is a width used when standard clinical scanning is performed;
• and
• obtaining the first projection data of each phantom under each scanning condition by connecting the projection data of each first segment of each phantom collected under each scanning condition;
• scanning, by a second obtaining module, each phantom in a scanning manner in which the scattering degree of each phantom in the plurality of phantoms is equal to the reference scattering degree, so as to obtain second projection data of each phantom;
• obtaining, by a subtraction module, a real scattered signal of each phantom by subtracting the first projection data of the phantom from the second projection data of the phantom;
• training, by a training module, a learning model based on the second projection data of each phantom and the real scattered signal of each phantom to obtain a trained learning model, wherein the training comprises:
• performing pre-calculation processing on the second projection data of each phantom based on a physical model to obtain a calculated scattered signal of each phantom;
• and
• training the learning model by using the calculated scattered signal of each phantom and the real scattered signal of each phantom as training data;
• applying, by a prediction module, the trained learning model to projection data of the X-ray for the examination object to predict the scattered signal in the projection data of the X-ray;
• and
• correcting the projection data of the X-ray for the examination object by using the predicted scattered signal to generate scatter-corrected projection data.

A method for reconstructing an image of an X-ray for an examination object, the method comprising:

• scanning, by a first obtaining module, each phantom in a scanning manner in which a scattering degree of each phantom in a plurality of phantoms is less than a reference scattering degree, so as to obtain first projection data of each phantom, wherein the scanning of each phantom comprises:
• performing segmented scanning on each of the plurality of phantoms with a first width less than a reference scanning width under a plurality of different scanning conditions to collect projection data of each first segment of each phantom with the first width under each scanning condition, wherein the reference scanning width is a width used when standard clinical scanning is performed;
• and
• obtaining the first projection data of each phantom under each scanning condition by connecting the projection data of each first segment of each phantom collected under each scanning condition;
• scanning, by a second obtaining module, each phantom in a scanning manner in which the scattering degree of each phantom in the plurality of phantoms is equal to the reference scattering degree, so as to obtain second projection data of each phantom;
• obtaining, by a subtraction module, a real scattered signal of each phantom by subtracting the first projection data of the phantom from the second projection data of the phantom;
• training, by a training module, a learning model based on the second projection data of each phantom and the real scattered signal of each phantom to obtain a trained learning model, wherein the training comprises:
• performing pre-calculation processing on the second projection data of each phantom based on a physical model to obtain a calculated scattered signal of each phantom;
• and
• training the learning model by using the calculated scattered signal of each phantom and the real scattered signal of each phantom as training data;
• applying, by a prediction module, the trained learning model to projection data of the X-ray for the examination object to predict the scattered signal in the projection data of the X-ray;
• correcting the projection data of the X-ray for the examination object by using the predicted scattered signal to generate scatter-corrected projection data;
• and
• reconstructing the scatter-corrected projection data to generate a reconstructed computerized tomography image after scatter correction.