A method for controlling a medical device may be provided. The method may include obtaining, via one or more cameras, first data regarding a first motion of a subject in an examination space of the medical device. The method may include obtaining, via one or more radars, second data regarding a second motion of the subject. The method may further include generating, based on the first data and the second data, a control signal for controlling the medical device to scan at least a part of the subject.

A system includes a storage device storing a set instructions and a processor in communication with the storage device, wherein when executing the set of instructions, the processor is configured to cause the system to obtain raw data. The processor may also be configured to cause the system to determine one or more reconstruction-related algorithms and determine one or more containers for the one or more reconstruction-related algorithms. Each of the one or more containers may correspond to at least one of the one or more reconstruction-related algorithms. The system may also be configured determine a reconstruction flow based on the one or more containers and process the raw data according to the reconstruction flow to generate a target image.

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.

Methods and devices for reconstructing a magnetic resonance image, and a non-transitory machine readable storage medium are provided. In an example, the method includes: obtaining a previous image; for each of channels, collecting k-space data of the channel by a partial sampling technology, generating original k-space data of the channel by mapping the previous image into k-space of the channel, and obtaining residue k-space data of the channel by subtracting the original k-space data of the channel from the k-space data of the channel; reconstructing a residue image with the residue k-space data of each of the channels by taking sparsity of the residue image as a constraint term and a difference between virtual residue k-space data of the channel and the residue k-space data of the channel as a data fidelity term; and obtaining a reconstructed magnetic resonance image by adding the residue image to the previous image.

A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; preprocessing the CT scan volume to output a preprocessed scan volume; converting the CT scan volume to three sets of two-dimensional slices; extracting a region of interest (ROI) by autonomous segmentation of the heart region as outlined by the pericardium, by means of three individually trained ROI extraction convolutional neural networks (CNN), each trained to process a particular one of the three sets of two-dimensional slices to output a mask denoting a heart region as delineated by the pericardium; combining the preprocessed scan volume with the mask to obtain a masked volume; converting the masked volume to three groups of sets of two-dimensional masked slices; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

Disclosed aspects relate to the acquisition and processing of projection data using temporal characteristics of the imaged volume, such as the uptake and clearance of a contrast agent within the volume. Such temporal aspects may be used in the acquisition process, such as to differentially acquire images based on the propagation of the contrast agent. In addition, such temporal aspects may be used in the processing of projection data to generate differential projections (e.g., first or second order subtraction projections), compound projections synthesized using the absolute or relative maximum opacity values observed over time for a region of interest, or interpolated projections synthesized using observed opacity values at known or fixed time intervals and a derived peak opacity time.

Techniques are provided for reconstructing cardiac frequency phenomena from a sequence of angiographic images, i.e., two-dimensional projection images acquired at faster than cardiac rate (greater than two-fold), and analyzed to provide a spatiotemporal reconstruction of moving vascular pulse waves according to that projection. In aspects, a cardiac frequency bandpass filter and/or a Eulerian magnification may be applied to the angiographic data to output the spatiotemporal reconstruction of cardiac frequency angiographic phenomena.

A computed tomography (CT) system includes a rotatable gantry having an opening to receive an object to be scanned, an x-ray tube, a pixelated detector positioned on the rotatable gantry to receive the x-rays from the x-ray tube, and a computer programmed to acquire helical CT data, determine a sunrise (SR) view position for each pixel within a SR index image, and determine a sunset (SS) view position for each pixel within a SS index image, for a given reference image slice, wherein the SR view position is a first angle of an illumination range for a voxel and the SS view position is a last angle of the illumination range for the voxel, for all slices, rotate the SR index image and the SS index image through a projection index, and reconstruct an image based on the rotated SR index image and the SS index 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 low-dose CT imaging system and method that operates according to a pulsed X-ray emission scheme according to a predefined sequence of rotation angles of the X-ray source, along with image reconstruction algorithms to achieve high spatial and temporal resolution for CT scans. The systems and methods involve high speed switching (on the order of milliseconds) to generate pulsed exposure of X-ray radiation to the patient, reducing radiation dose by 4-8 fold, or more.