Abstract: A medical image (24) with contrast of one imaging modality is emulated from images with other contrast characteristics. A plurality of input images (20) of a region of interest with the other contrast characteristics is received. A transform (22) which was generated by machine learning is applied to the corresponding plurality of input images (20) to generate a scalar value (38) or a vector value (38?) representing the corresponding voxel (36?) of the medical image (24) to be evaluated.

Abstract: An imaging method and corresponding system (10) account for cascade gammas. Event data describing detected gamma rays emitted from a target volume of a subject are received. The detected gamma rays include cascade gammas emitted from a radionuclide within the target volume. Cascade and annihilation gamma emissions from the target volume and coincidence detection of the imaging system (10) are simulated using a Monte Carlo (MC) simulation technique to generate a cascade dataset comprised of annihilation coincidence events and cascade coincidence events. The event data is reconstructed into an image representation of the target volume with correction of cascade coincidence using the relationship between the annihilation coincidence events and the cascade coincidence events in the cascade data set.

Abstract: A medical image (24) with contrast of one imaging modality is emulated from images with other contrast characteristics. A plurality of input images (20) of a region of interest with the other contrast characteristics is received. A transform (22) which was generated by machine learning is applied to the corresponding plurality of input images (20) to generate a scalar value (38) or a vector value (38?) representing the corresponding voxel (36?) of the medical image (24) to be evaluated.

Abstract: A method for estimating parameter values includes acquiring image data with an imaging apparatus, deriving a parameter model function from the image data, generating a N-dimensional grid, wherein N is a number of values of one or more non-linear terms of the derived model function, pre-calculating the one or more non-linear terms given the parameter model function and the designated values of the non-linear parameters, calculating one or more remaining model terms of the parameter model function, and displaying at least one of the one or more non-linear terms and remaining linear model terms.

Abstract: A magnetic resonance (MR) system (10) generates an attenuation or density map. The system (10) includes a MR scanner (48) defining an examination volume (16) and at least one processor (54). The at least one processor (54) is programmed to control the MR scanner (48) to apply imaging sequences to the examination volume (16). In response to the imaging sequences, MR data sets of the examination volume (16) are received and analyzed to identify different tissue and/or material types found in pixels or voxels of the attenuation or density map. One or more tissue-specific and/or material-specific attenuation or density values are assigned to each pixel or voxel of the attenuation or density map based on the tissue and/or material type(s) identified as being in each pixel or voxel during the analysis of the MR data sets. In one embodiment, the tissue and/or material types are identified on the basis of a time series of MR phase images.

Abstract: An imaging method and corresponding system (10) account for cascade gammas. Event data describing detected gamma rays emitted from a target volume of a subject are received. The detected gamma rays include cascade gammas emitted from a radionuclide within the target volume. Cascade and annihilation gamma emissions from the target volume and coincidence detection of the imaging system (10) are simulated using a Monte Carlo (MC) simulation technique to generate a cascade dataset comprised of annihilation coincidence events and cascade coincidence events. The event data is reconstructed into an image representation of the target volume with correction of cascade coincidence using the relationship between the annihilation coincidence events and the cascade coincidence events in the cascade data set.

Abstract: A magnetic resonance (MR) system (10) generates an attenuation or density map. The system (10) includes a MR scanner (48) defining an examination volume (16) and at least one processor (54). The at least one processor (54) is programmed to control the MR scanner (48) to apply imaging sequences to the examination volume (16). In response to the imaging sequences, MR data sets of the examination volume (16) are received and analyzed to identify different tissue and/or material types found in pixels or voxels of the attenuation or density map. One or more tissue-specific and/or material-specific attenuation or density values are assigned to each pixel or voxel of the attenuation or density map based on the tissue and/or material type(s) identified as being in each pixel or voxel during the analysis of the MR data sets. In one embodiment, the tissue and/or material types are identified on the basis of a time series of MR phase images.