A medical image processing apparatus and a medical image processing method that can reduce the noise bias of a corrected image in which high absorber artifacts included in a reconstructed image are corrected. The medical image processing apparatus has an arithmetic unit for correcting high absorber artifacts. The arithmetic unit comprises: a projection data generating section that generates projection data corresponding to a high absorber area in a reconstructed image with high absorber artifacts; a noise image generating section that generates a noise image using the projection data; and a weighted combining section that makes a weighted combining of the noise image with a corrected image in which high absorber artifacts are corrected.

A medical image processing apparatus and a medical image processing method that can reduce the noise bias of a corrected image in which high absorber artifacts included in a reconstructed image are corrected. The medical image processing apparatus has an arithmetic unit for correcting high absorber artifacts. The arithmetic unit comprises: a projection data generating section that generates projection data corresponding to a high absorber area in a reconstructed image with high absorber artifacts; a noise image generating section that generates a noise image using the projection data; and a weighted combining section that makes a weighted combining of the noise image with a corrected image in which high absorber artifacts are corrected.

A method of generating a trained model uses a first generator configured using a three-dimensional convolutional neural network that receives an input of a three-dimensional image of a first domain and that outputs a three-dimensional generated image of a second domain different from the first domain, and a first discriminator configured using a two-dimensional convolutional neural network that receives an input of a two-dimensional image indicating a cross section image in a first slice plane direction cut out from the three-dimensional generated image of the second domain and that discriminates authenticity of the input two-dimensional image. The method includes performing, by a computer, training the first generator and the first discriminator in an adversarial manner based on training data including a three-dimensional image captured under a first imaging condition and a three-dimensional image captured under a second imaging condition different from the first imaging condition.

The present disclosure provides a system for image reconstruction. The system may obtain an initial image of a subject. The initial image may be generated based on scan data of the subject that is collected by an imaging device. The system may also generate a gradient image associated with the initial image. The system may further generate a target image of the subject by applying an image reconstruction model based on the initial image and the gradient image. The target image may have a higher image quality than the initial image.

A system includes a spatial mismatch correction module configured to receive functional emission data, anatomical image data, and functional image data reconstructed based on the functional emission data and attenuation corrected based on the anatomical image data. The system further includes a data set provider configured to provide a first data set and a second data set, which are spatially mismatched. The system further includes a voxel of interest identifier configured to identify voxels or regions of reconstruction inconsistency due to a spatial mismatch between true attenuation values and attenuation values derived from the anatomical image data based on relations between the first and second data sets. The system further includes an image data generator configured to morph the functional image data and generate corrected functional image data based on the identified voxels or regions, independent of functional-anatomical structural correlation, while maintaining an image quality of the functional image data.

An information processing apparatus includes a processor, in which the processor acquires first projection data output from a detector that has detected radiation of a first energy transmitted through a subject and second projection data output from the detector that has detected radiation of a second energy transmitted through the subject, the second energy being different from the first energy, and performs a correction process of correcting artifacts in a region in which an amount of change between the first projection data and the second projection data is equal to or greater than a threshold value.

One or more example embodiments relates to a method for providing an item of comparison information based on an item of output information for a medical imaging apparatus. In addition, one or more example embodiments relates to a computing unit, a medical imaging apparatus, a computer program product and a computer storage medium. The computer-implemented method for providing an item of comparison information based on an item of output information for a medical imaging apparatus, comprises generating expected measurement data via a trained function based on the item of output information, providing measurement data of a medical imaging examination based on the items of output information, ascertaining an item of comparison information based on the expected measurement data and the measurement data, and providing the item of comparison information.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Systems and methods include acquisition of a computed tomography image of an object, determination of a linear attenuation coefficient map based on the computed tomography image, acquisition of positron emission tomography (PET) data of the object, determination of a histoimage of the object based on the PET data, determination of a scatter histoimage based on the histoimage and the linear attenuation coefficient map, determination of a scatter-corrected histoimage based on the histoimage and the scatter histoimage, and input of the computed tomography image and the scatter-corrected histoimage to a trained neural network to generate a PET image.

Systems and methods include acquisition of a computed tomography image of an object, determination of a linear attenuation coefficient map based on the computed tomography image, acquisition of positron emission tomography (PET) data of the object, determination of a histoimage of the object based on the PET data, determination of a scatter histoimage based on the histoimage and the linear attenuation coefficient map, determination of a scatter-corrected histoimage based on the histoimage and the scatter histoimage, and input of the computed tomography image and the scatter-corrected histoimage to a trained neural network to generate a PET image.