A computer-implemented method for modifying X-ray projection images of a subject region includes: generating a set of combined two-dimensional (2D) projections of a subject region, wherein each combined 2D projection includes one or more mask-bordering pixels and one or more mask-edge pixels; forming a three-dimensional (3D) matrix of the set of combined 2D projections; based on the 3D matrix, generating a linear algebraic system for determining pixel values for pixels indicated in a set of 2D projection metal masks, wherein a first change in slope of pixel value associated with a mask-edge pixel of a combined 2D projection is constrained to equal a second change in slope of pixel value associated with a mask-bordering pixel of a combined 2D projection; determining values for a variable vector of the linear algebraic system; and generating a set of inpainted 2D projections by modifying initial 2D projections with values for the variable vector.

A computer-implemented method of segmenting a reconstructed volume of a region of patient anatomy includes: determining an anatomical region associated with the reconstructed volume; detecting one or more metal objects disposed in an initial 3D metal object mask associated with the reconstructed volume; for each of the one or more metal objects disposed in the initial 3D metal object mask, determining a volume associated with the metal object; determining a value for at least one segmentation parameter based on the anatomical region and on the volume associated with the one or more metal objects; and generating a final 3D metal object mask associated with the reconstructed digital volume using the value for the segmentation parameter.

Provided in embodiments of the present application are a medical image processing method and apparatus and a medical device. The medical image processing method includes acquiring raw projection data obtained after a subject to be examined is scanned, and performing reconstruction to obtain a raw medical image, performing multivalued processing on the raw medical image to obtain a multivalued image, according to a correspondence between a degree of contribution, relating to a ray absorption amount, of each tissue on a ray path that does not pass through a specific material site in the multivalued image and a raw projection value corresponding to the ray path in the raw projection data, determining a predicted projection value of a path corresponding to the specific material site in the raw projection data, and obtaining a first medical image according to the predicted projection value and the raw projection data.

A system and method for acquiring in vivo tomographic X-ray scatter data for tissue discrimination of breast tissue. The system includes a coded aperture for spatially encoding X-ray scatter originating from within a patient's breast. Detectors record the modulated scatter signal, which is used to reconstruct a spatially resolved estimate of the X-ray scatter spectra which can then be used to generate spatially resolved tissue type estimates for the user.

Systems and methods include segmentation of a first image of a subject to identify locations of anatomical structures of the subject, determination of a region of interest of the subject based on the locations of anatomical structures, determination of a coil-mixing matrix based on the region of interest, control of an MR scanner to acquire radial trajectory k-space data of the subject from each of a plurality of RF coils of the MR scanner, application of the coil-mixing matrix to the radial trajectory k-space data of the subject acquired from each of the plurality of RF coils to generate first radial trajectory k-space data, reconstruction of a second image of the subject based on the first radial trajectory k-space data, and display of the second image.

A method for performing image enhancement using a neural network is provided. The method includes acquiring an image of an imaging object, and applying the acquired image to a trained neural network to generate an image-enhanced image of the imaging object. The neural network was trained by receiving a training image pair including a first image and a second image, performing an image intensity preprocessing on the received image pair to generate a preprocessed first image and a preprocessed second image, such that the preprocessed first image has a first intensity range covering a portion of an intensity range of the first image, and the preprocessed second image has a second intensity range covering a portion of an intensity range of the second image, and training the neural network using the preprocessed first image as an input image and the preprocessed second image as a target image.

A method for removing spatially varying artifacts such laminographic artifacts and/or high-angle cone beam artifacts for 3D computed tomography (CT) involves thresholding current reconstructions to create thresholded reconstructions and then creating simulated reconstructions from the thresholded reconstructions. These simulated reconstructions are subtracted from the current reconstructions to create the current reconstructions for a next iteration. A final reconstruction is then created by summing the thresholded reconstructions. This approach can progressively remove the artifacts. In addition, the method can be used to generate high quality training data to further improve the speed and robustness. These methods will work for other non-Orlov complete computed tomography in general, such as high cone angle, missing views.

A method for reducing metal artifacts in CT images, including the following steps: a material decomposition (MD) calibration step, using multiple MD calibration phantoms with known characteristics and multiple spectral CT data corresponding thereto to construct a system characteristic model of spectral CT; and a MD testing step, including: the following steps: imaging multiple testing objects with a different unknown material and thickness to obtain projection-based multiple spectral CT imaging data of different energy bins; obtaining corresponding multiple basis material images of different materials based on projection data according to the spectral CT imaging data and the system characteristic model of spectral CT; and combining the basis material images and a photon energy information to be recombined with each other to obtain multiple virtual monoenergetic images.

A system and method for improving image quality of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging include acquiring a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner from a coil during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data includes a plurality of parallel phase encoding lines sampled in a phase encoding order. The system and method also include utilizing a deep learning-based multi-level denoising network to denoise each blade of the plurality of blades in an image domain to generate a plurality of denoised blades, to utilize a PROPELLER reconstruction algorithm to generate a denoised-gridded image from the plurality of denoised blades, and to remove individual-based denoising-induced artifacts from the denoised-gridded image to generate a denoised, artifact-free gridded image.

Disclosed in the present invention are a weighted analytic filtered back projection reconstruction method and system for asymmetric cone angle artifacts. The method comprises the following steps: dividing a reconstruction area into a plurality of weight regions on the basis of relative positions of a ray source ring and a detector ring; acquiring the projection data volume of voxel points in each weight area irradiated by X-rays; according to the projection data volume of the voxel points in each weight area irradiated by the X-rays, assigning a different initial weight to each weight area; performing smooth transition on the initial weight of each weight area by means of a transition weight to form a final weight assigned to each weight area; and according to different final weights of the weight regions, performing final weighted analytic reconstruction on projection data p (, , ) to acquire a back projection image.