A method for evaluating image quality is provided. The method may include: obtaining an image, the image including a plurality of elements, each element of the plurality of elements being a pixel or voxel, each element having a gray level; determining, based on a maximum gray level of the plurality of elements, one or more thresholds for segmenting the image; determining one or more sub-images of a region of interest by segmenting, based on the one or more thresholds, the image; and determining, based on the one or more sub-images of the region of interest, a quality index for the image.

A computer implemented method for determining a two dimensional DRR referred to as dynamic DRR based on a 4D-CT, the 4D-CT describing a sequence of three dimensional medical computer tomographic images of an anatomical body part of a patient, the images being referred to as sequence CTs, the 4D-CT representing the anatomical body part at different points in time, the anatomical body part comprising at least one primary anatomical element and secondary anatomical elements, the computer implemented method comprising the following steps: acquiring the 4D-CT; acquiring a planning CT, the planning CT being a three dimensional image used for planning of a treatment of the patient, the planning CT being acquired based on at least one of the sequence CTs or independently from the 4D-CT, acquiring a three dimensional image, referred to as undynamic CT, from the 4D-CT, the undynamic CT comprising at least one first image element representing the at least one primary anatomical element and second image elements representing the secondary anatomical elements; acquiring at least one trajectory, referred to as primary trajectory, based on the 4D-CT, the at least one primary trajectory describing a path of the at least one first image element as a function of time; acquiring trajectories of the second image elements, referred to as secondary trajectories, based on the 4D-CT; for the image elements of the undynamic CT, determining trajectory similarity values based on the at least one primary trajectory and the secondary trajectories, the trajectory similarity values respectively describing a measure of similarity between a respective one of the secondary trajectories and the at least one primary trajectory; determining the dynamic DRR by using the determined trajectory similarity values, and, in case the planning CT is acquired independently from the 4D-CT, further using a transformation referred to as planning transformation from the undynamic CT to the planning CT, at least a part of image values of image elements of the dynamic DRR being determined by using the trajectory similarity values.

A method includes receiving image data of an examination object. A first temporary data record is created by applying a first correction to the image data. A further temporary data record is created by applying a further correction to the image data. The further correction at least partially corresponds to the first correction. A trained function is applied to input data that is based on the first temporary data record and the further temporary data record. A parameter of the trained function is based on an image quality metric. It is determined whether the first temporary data record has a higher image quality compared with the further temporary data record. When a result is positive, the first temporary data record is provided as the corrected medical image data. When the result is negative, the further temporary data record is provided as the image data, and part of the method is repeated.

Systems and methods of enhanced display and viewing of three dimensional (3D) tomographic data acquired in tomosynthesis or tomography. A set of projection data is acquired with an image acquisition system and used to reconstruct enhanced 3D volume renderings that are viewed with motion, advanced image processing or stereotactically to assist in medical diagnosis. Various enhancements are provided for further processing the images, thereby providing additional features and benefits during image viewing.

An x-ray imaging apparatus and associated methods are provided to execute multi-pass imaging scans for improved quality and workflow. An imaging scan can be segmented into multiple passes that are faster than the full imaging scan. Data received by an initial scan pass can be utilized early in the workflow and of sufficient quality for treatment setup, including while the another scan pass is executed to generate data needed for higher quality images, which may be needed for treatment planning. In one embodiment, a data acquisition and reconstruction technique is used when the detector is offset in the channel and/or axial direction for a large FOV during multiple passes.

An imaging system (702) includes a reconstructor (716) configured to reconstruct obtained cone beam projection data with a voxel-dependent redundancy weighting such that low frequency components of the cone beam projection data are reconstructed with more redundant data than high frequency components of the cone beam projection data to produce volumetric image data. A method includes reconstructing obtained cone beam projection data with a voxel-dependent redundancy weighting such that low frequency components are reconstructed with more redundant data than high frequency components to produce volumetric image data. A computer-readable storage medium, storing computer executable instructions, which when executed by a processor of a computer cause the processor to: obtain cone beam projection data and reconstruct the cone beam projection data with a voxel-dependent redundancy weighting such that low frequency components are reconstructed with more redundant data than high frequency components to produce volumetric image data.

Computer-implemented method for determining nuclear medical image data sets in dynamic nuclear medical imaging. The method includes using a trained function to determine at least one further nuclear medical image data set for at least one frame if a basis raw data set is taken from one single frame, wherein input data of the trained function includes at least one of the nuclear medical raw data set of the respective frame or a preliminary reconstructed image reconstructed therefrom, and an already determined nuclear medical image data set.

The invention provides, in some aspects, a system for implementing a rule derived basis to display volumetric image sets. In various embodiments of the invention, the selection of the images to be displayed, the generation of the 3-D volumetric image from measured 2-D images including the rendering parameters and styles, the choice of viewing directions and 2-D projection images based on the viewing directions, the layout of the projection images, and the formation of a video can be determined using a rule derived basis. In an embodiment of the present invention, the user is presented with sequential images making up a video displayed based on their preferences without having to first manually adjust parameters. The present invention allows for novel ways of viewing such images to detect microcalcifications and obstructions when reviewing Digital Breast Tomosynthesis and other volumetric mammography images.

Devices, systems, and methods for generating a medical image obtain scan data that were generated by scanning a scanned region, wherein the scan data include groups of scan data that were captured at respective angles; generate partial reconstructions of at least a part of the scanned region, wherein each partial reconstruction of the partial reconstructions is generated based on a respective one or more groups of the groups of scan data, and wherein a collective scanning range of the respective one or more groups is less than the angular scanning range; input the partial reconstructions into a machine-learning model, which generates one or more motion-compensated reconstructions of the at least part of the scanned region based on the partial reconstructions; calculate a respective edge entropy of each of the one or more motion-compensated reconstructions of the at least part of the scanned region; and adjust the machine-learning model based on the respective edge entropies.

A system includes a storage device storing a set of instructions and at least one processor in communication with the storage device, wherein when executing the instructions, the at least one processor is configured to cause the system to determine a first scan area on a scanning object. The system may also acquire raw data generated by scanning the first scan area on the scanning object and generate a positioning image based on the raw data. The system may also generate a pixel value distribution curve based on the positioning image, and determine a second scan area on the scanning object based on the pixel value distribution curve. The system may also scan the second scan area on the scanning object.