The present disclosure relates to systems and methods for image reconstruction. The systems and methods may obtain an initial image to be processed. The systems and methods may also generate a reconstructed image by performing a plurality of iteration steps on the initial image. At least one of the plurality of iteration steps may include a first optimization operation and a second optimization operation. The first optimization operation may include receiving an image to be processed in the iteration step and determining an updated image by preliminarily optimizing the image to be processed. The second optimization operation may include determining, using an optimizing model, an optimized image based on the updated image and designating the optimized image as a next image to be processed in a next iteration step or designating the optimized image as the reconstructed image.

An X-ray CT apparatus according to an embodiment includes a photon counting detector that outputs a signal that enables measurement of an energy value of an X-ray photon incident thereon, and processing circuitry configured to determine a time to make a transition from a prescan to a main scan by estimating, from projection data, an amount of a contrast agent present on an X-ray path including a monitored region set for a subject in the prescan, the projection data having been generated by detection of X-rays by the photon counting detector, the X-rays having been transmitted through the monitored region.

Methods and systems for medical imaging are provided. A method may include: obtaining radiation events; determining first response information based on the radiation events, the first response information including first time information of the radiation events; determining second response information based on the radiation events, the second response information including response information corresponding to an anomaly detection unit and lacking time information; and generating an image based on the first response information and the second response information.

This disclosure relates to the field of X-ray based medical imaging technology, providing a method, device, and CT scanning imaging system for spectral CT imaging, which may improve the efficiency of reconstructed images. In this disclosure, after obtaining multiple data frames collected by the detector, each data frame is cached in energy segments and stored in memory; Read the data frames required for image reconstruction from memory or disk; Obtain reconstructed images using the read data frames.

A method for predicting a scattered signal of an X-ray for an examination object includes: scanning 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; scanning 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 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 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; and applying the trained learning model to projection data of the X-ray for the examination object to predict the scattered signal of the projection data of the X-ray.

A method for predicting a scattered signal of an X-ray for an examination object includes: scanning 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; scanning 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 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 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; and applying the trained learning model to projection data of the X-ray for the examination object to predict the scattered signal of the projection data of the X-ray.

A method for registering a two-dimensional image data set of a body of interest with a three-dimensional image data set of the body of interest is discloses herein. The method includes the following steps: generating a first reconstructed image from the three-dimensional image data set with a first spatial parameter; calculating a reference similarity value according to the first reconstructed image and the two-dimensional image data set; generating a second reconstructed image from the three-dimensional image data set with a second spatial parameter; calculating a comparison similarity value according to the second reconstructed image and the two-dimensional image data set; comparing the comparison similarity value with the reference similarity value; and registering the two-dimensional image data set to the three-dimensional image data set if the comparison similarity value is not greater than the reference similarity value.

Methods and data structures provide protection for a set of digital image files from loss, wherein the set of digital image files comprises a plurality of digital image files of a same size. Methods include encoding each digital image file in the plurality thereof, encoding the resulting check blocks by computing a final check symbol, and embedding the check symbols and final check symbols into the plurality of digital image files in a manner distributed approximately evenly over the plurality of digital image files. The digital image files may be transmitted to a receiver, and decoded, including detaching the check symbols and final check symbols, creating a plurality of error correction blocks from the same, and using these to recover or repair any errors in the received digital image files.

A processor acquires training data including a learning tomographic image that includes a high-attenuation substance and artifacts caused by the high-attenuation substance, and a ground truth tomographic image that does not include the high-attenuation substance and the artifacts caused by the high-attenuation substance, derives a normalized learning tomographic image and a normalized ground truth tomographic image by normalizing at least one of sharpness, contrast, or noise of the learning tomographic image and the ground truth tomographic image, and constructs a derivation model through machine learning using the normalized learning tomographic image and the normalized ground truth tomographic image, the derivation model deriving a removed tomographic image in which the high-attenuation substance and the artifacts caused by the high-attenuation substance included in a target tomographic image have been removed, in a case where the target tomographic image including the high-attenuation substance and the artifacts is input.

A processor is provided, and the processor specifies a high-attenuation substance region in a projection image acquired by imaging a subject including a high-attenuation substance using a CT apparatus, and derives a corrected projection image by performing correction on the high-attenuation substance region in the projection image to suppress a difference in image quality between the high-attenuation substance region and other regions outside the high-attenuation substance region.