Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

Systems/techniques that facilitate AI-based region-of-interest masks for improved data reconstructions are provided. In various embodiments, a system can access a set of two-dimensional medical scan projections. In various aspects, the system can generate a set of two-dimensional region-of-interest masks respectively corresponding to the set of two-dimensional medical scan projections. In various instances, the system can generate a region-of-interest visualization based on the set of two-dimensional region-of-interest masks and the set of two-dimensional medical scan projections. In various cases, the system can generate the set of two-dimensional region-of-interest masks by executing a machine learning segmentation model on the set of two-dimensional medical scan projections.

Various methods and systems are provided for stationary CT imaging. In one embodiment, a method for an imaging system includes activating a plurality of emitters of a stationary distributed x-ray source unit to emit x-ray beams toward an object within an imaging volume, where the x-ray source unit does not rotate around the imaging volume, receiving attenuated x-ray beams with one or more detector arrays to form a sparse view projection dataset, where each attenuated x-ray beam generates a different view, and reconstructing an image from the sparse view projection dataset using a sparse view reconstruction method.

An image processing device determines whether each tumor candidate regions detected from a plurality of tomographic images indicating a plurality of tomographic planes of an object is a tumor or a local mass of a mammary gland, selects a first tomographic image group from the plurality of tomographic images in a first region determined to be the tumor, selects a second tomographic image group from the plurality of tomographic images in a second region determined to be the local mass of the mammary gland, selects a third tomographic image group from the plurality of tomographic images in a third region other than the first region and the second region, and generates a composite two-dimensional image using the tomographic image groups selected for each of the first region, the second region, and the third region.

Apparatus and method for performing computed tomography scans of elongate objects, wherein the object is irradiated with X-rays emitted by a plurality of X-ray emitters which are offset relative to a forward movement direction transversal to the main axis of the object, wherein a rotation device rotates each object about its own main axis of extension while the object is irradiated by one or more beams of X-rays, wherein electronic identifying means estimate the instantaneous position and orientation of the axial portions of the object which are irradiated during the rotation, and wherein an electronic processing and control unit is programmed for combining sets of radiographic data acquired for each axial portion of the object at different detecting moments during the rotation, for processing a three-dimensional tomography reconstruction of the object while taking into account corresponding information about the position and the orientation of each axial portion at each moment.

6Examples described herein provide a method that includes obtaining, by a processing device, three-dimensional (3D) voxel data. The method further includes performing, by the processing device, gray value thresholding based at least in part on the 3D voxel data and assigning a classification value to at least one voxel of the 3D voxel data. The method further includes defining, by the processing device, segments based on the classification value. The method further includes filtering, by the processing device, the segments based on the classification value. The method further includes evaluating, by the processing device, the segments to identify a surface voxel per segment. The method further includes determining, by the processing device, a position of a surface point within the surface voxel.

Images are reconstructed from data acquired using an ultra-fast-pitch acquisition with a CT system. As an example, an ultra-fast-pitch acquisition mode in single-source helical CT (p≥1.5) can be used to acquire data. A trained machine learning algorithm, such as a neural network, is used to reconstruct images in which artifacts associated with insufficient data acquired in the ultra-fast-pitch mode are reduced. An example neural network can include customized functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the ultra-fast-pitch mode. The machine learning algorithm can be trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss.

The medical image diagnostic apparatus is configured to generate a medical image based on data collected from a subject when an operator sequentially performs a plurality of steps. A display unit has a display screen. An operation unit receives an operation of an operator on the display screen. A control unit controls the display unit based on the operation received by the operation unit. The display unit displays a plurality of setting screens respectively corresponding to the plurality of steps on a display screen in a superimposed manner. The control unit is configured to switch the setting screen of the uppermost layer between the plurality of setting screens based on the operation received by the operation unit. The display unit further displays, in each of the plurality of setting screens, a plurality of wizard screens for sequentially inputting a plurality of pieces of information in a wizard format.

Provided is a method for determining tube electrical parameters. The method includes: acquiring target projection data of an imaging device in scanning a target object at a first scan angle; acquiring target noise data corresponding to the target object; determining current noise data corresponding to the target projection data; and determining, based on the target noise data and the current noise data, the tube electrical parameters of the imaging device in scanning the target object at a second scan angle.

The present disclosure includes systems, methods and media for rendering objects translucent and for recovery of anatomical information blocked by the objects in medical images.