Selected artifacts, which may be based on distortions or selected attenuation features, may be reduced or removed from a reconstructed image. Various artifacts may occur due to the presence of a metal object in a field of view. The metal object may be identified and removed from a data that is used to generate a reconstruction.

A method and a system for providing calibration for a polychromatic photon counting detector forward counting model. Measurements with multiple materials and known path lengths are used to calibrate the photon counting detector counting response of the forward model. The flux independent weighted bin response function is estimated using the expectation maximization method, and then used to estimate the pileup correction terms at plural tube voltage settings for each detector pixel. The beam hardening corrections are then applied to the measured projection data sinogram, and the corrected sinogram is reconstructed to the counting image at the selected single energy.

A method is for metal artifact reduction in CT image data, the CT image data including multiple 2D projection images acquired using different projection geometries and suitable to reconstruct a 3D image data set of a volume of an imaged object. In an embodiment, the method includes a metal artifact reduction process including at least, acquiring, using a multi-energy CT technique, energy-resolved CT image data associated with multiple energy ranges. At least one result of the multi-energy technique is used in at least one aspect of the metal artifact reduction process.

A low-dose imaging method includes receiving a sparse image set of a portion of a patient's anatomy; up-sampling the sparse image set, in the sinogram domain and using a first neural network, to yield an up-sampled sinogram; generating, from the up-sampled sinogram, an initial reconstruction; and removing, from the initial reconstruction and using a second neural network, one or more artifacts in the initial reconstruction to yield a final output volume.

A method of imaging a region of patient anatomy having a target volume includes performing an autosegmentation of a high-contrast portion of a first reconstructed volume of the region to generate a three-dimensional (3D) representation of the high-contrast portion disposed within the region and generating a set of two-dimensional (2D) mask projections of the region by performing a forward projection process on the 3D representation, wherein each 2D mask projection in the set of 2D mask projections includes location information indicating pixels that are blocked by the high-contrast portion during the forward projection process performed on the 3D representation. Based on a set of 2D acquired projections and the location information, the method further includes generating a set of 2D corrected projections of the region in which the high-contrast portion is removed from each of the corrected projections and generating a second reconstructed volume of the region based on the 2D corrected projections, wherein the second reconstructed volume does not include the high-contrast portion.

A medical image processing apparatus and a medical image processing method are provided which are capable of reducing metal artifacts and preserving the image quality even in a region less affected by metal artifacts. The medical image processing apparatus includes an arithmetic section that reconstructs a tomographic image from projection data of an object under examination including a metal. The arithmetic section acquires a machine learning output image that is output when the tomographic image is input to a machine learning engine that machine-learns to reduce metal artifacts, and the arithmetic section composites the machine learning output image and the tomographic image to generate a composite image.

A deep-learning-based method for metal artifact reduction in CT images includes providing a dataset and a cGAN. The dataset includes CT image pairs, randomly partitioned into a training set, a validation set, and a testing set. Each Pre-CT and Post-CT image pairs is respectively acquired in a region before and after an implant is implanted. The Pre-CT and Post-CT images of each pair are artifact-free CT and artifact-affected CT images, respectively. The cGAN is conditioned on the Post-CT images, includes a generator and a discriminator that operably compete with each other, and is characterized with a training objective that is a sum of an adversarial loss and a reconstruction loss. The method also includes training the cGAN with the dataset; inputting the post-operatively acquired CT image to the trained cGAN; and generating an artifact-corrected image by the trained cGAN, where metal artifacts are removed in the artifact-corrected image.

A computer-implemented method is for artifact correction during a reconstruction of at least one slice image from at least one projection image. The at least one projection image includes a plurality of pixels, each pixel including a pixel value. The method includes determining at least one corrected projection image based on the at least one projection image via a computing circuit; reconstructing the at least one slice image based on the at least one corrected projection image; and providing the at least one slice image. The determining includes determining an average pixel value in at least one subarea of the at least one projection image, a correction value by multiplying the average pixel value by a scatter factor, and a plurality of corrected pixel values by subtracting the correction value from the plurality of pixel values. The corrected projection image includes pixels including the plurality of corrected pixel values.

Disclosed herein are methods for reducing beam-hardening artifacts in CT imaging using a mapping operator that comprises a hybrid spectral model that incorporates air scan X-ray intensity data acquired at two different effective mean energies. In one variation, the air scan X-ray intensity data acquired during a calibration session is combined with an ideal spectral model for each X-ray detector to derive the hybrid spectral mode. A mapping operator based on the hybrid spectral model is used to correct beam-hardening artifacts in the acquired CT projection data. In some variations, the mapping operator is a lookup table of monochromatic (corrected) projection values, and the acquired CT projection data is used to calculate the index of the lookup table entry that contains the corrected projection value that corresponds with the acquired CT projection data.

Selected artifacts, which may be based on distortions or selected attenuation features, may be reduced or removed from a reconstructed image. Various artifacts may occur due to the presence of a metal object in a field of view. The metal object may be identified and removed from a data that is used to generate a reconstruction.