Presented are concepts for initialising a model for model-based segmentation of an image which use specific landmarks (e.g. detected using other techniques) to initialize the segmentation mesh. Using such an approach, embodiments need not be limited to predefined model transformations, but can initialise a segmentation mesh with arbitrary shape. In this way, embodiments may provide for an image segmentation algorithm that not only delivers a robust surface-based segmentation result but also does so for strongly varying target structure variations (in terms of shape).

Systems and methods for predicting outcome of radiation therapy is described herein. An example method includes receiving respective values for tumor volume of a target patients tumor at first and second time points, and calculating a change in tumor volume between the first and second time points. The method also includes estimating a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume. Additionally, the method includes predicting a volume of the target patient's tumor at a future time point during radiation treatment based, at least in part, on a historical carrying capacity reduction fraction distribution and the patient-specific carrying capacity. The method further includes predicting a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patients tumor at the future time point.

A computer-implemented method is provided for improving live video quality. The method comprises: acquiring, using a medical imaging apparatus, a stream of consecutive image frames of a subject, and the stream of consecutive image frames are acquired with reduced amount of radiation dose; applying a deep learning network model to the stream of consecutive image frames to generate an image frame with improved quality; and displaying the image frame with improved quality in real-time on a display.

A learning-based active surface model for medical image segmentation uses a method including: (a) data generation: obtaining medical images and associated ground truths, and splitting the sample images into a training set and a testing set; (b) raw segmentation: constructing a surface initialization network, parameters of the network trained by images and labels in the training set; (c) surface initialization: segmenting the images by the surface initialization network, and generating the point cloud data as the initial surface from the segmentation; (d) fine segmentation: constructing the surface evolution network, the parameters of the network trained by the initial surface obtained in step (c); (e) surface evolution: deforming the initial surface points along the offsets to obtain the predicted surface, the offsets presenting the prediction of the surface evolution network; (f) surface reconstruction: reconstructing the 3D volumes from the set of predicted surface points set to obtain the final segmentation results.

A system and method are provided for creating an image including quantified flow within vessels of a subject. The method includes providing a single-sweep, three-dimensional (3D) image volume acquired from a subject during a single pass of a computed tomography (CT) imaging system as the subject receives a dose of a contrast agent and determining a phase shift corresponding to pulsatile contrast in vessels within the single-sweep, 3D image volume. The method further includes quantifying a flow through the vessels within the single-sweep, 3D image volume using the phase shift and generating a report including indicating flow through the vessels within the 3D image volume.

The present disclosure provides systems and methods for hybrid imaging. The systems and methods may obtain a first magnetic resonance (MR) image of a target object. The first MR image may be acquired by a magnetic resonance imaging (MRI) device using a first imaging sequence. The systems and methods may also obtain a second MR image of the target object. The second MR image may be acquired by the MRI device using a second imaging sequence. The second MR image may correspond to a target respiratory phase of the target object. The systems and methods may also obtain a target emission computed tomography ECT) image of the target object. The target ECT image may correspond to the target respiratory phase. The systems and methods may further fuse, based on the second MR image, the first MR image and the target ECT image.

An image processing apparatus selects one or a plurality of examinations to which a medical image belongs, determines image processing candidate examinations based on the selected one or plurality of examinations, displays medical images belonging to the determined image processing candidate examinations on a display unit, and executes image processing using, of the displayed medical images, a plurality of medical images selected by a user, wherein, when the one examination is selected, the selected one examination and one or a plurality of examinations obtained by a search based on the selected one examination are determined as the image processing candidate examinations, and when the plurality of examinations are selected, in the determining, the selected plurality of examinations are determined as the image processing candidate examinations.

Systems and methods are disclosed for treating teeth to correct for malocclusions. This may be accomplished by applying a series of labels to a digital dental model and applying a rolling ball process to identify tooth boundaries separating one tooth from a neighboring tooth and to also determine the crown/gum margin. The user may further assign regions to the dental model to indicate hard regions and soft regions. With the dental model labeled and defined, the user may then generate a treatment plan for moving the labeled and defined tooth or teeth relative to one another to correct for any malocclusions. Upon approval of the treatment plan, a series of 3D printed dental appliances or aligners to be worn in series by the patient may be fabricated to ultimately move the tooth or teeth to a desired position.

A system and method for estimating vascular flow using CT imaging include a computer readable storage medium having stored thereon a computer program comprising instructions, which, when executed by a computer, cause the computer to acquire a first set of data comprising anatomical information of an imaging subject, the anatomical information comprises information of at least one vessel. The instructions further cause the computer to process the anatomical information to generate an image volume comprising the at least one vessel, generate hemodynamic information based on the image volume, and acquire a second set of data of the imaging subject. The computer is also caused to generate an image comprising the hemodynamic information in combination with a visualization based on the second set of data.

The systems and method for processing scanning data of a scanning object are provided. The method may include acquiring, in a scanning process, at least two target phases of a motion of the scanning object, wherein the scanning process involves multiple data acquisition time points each of which corresponds to a scanning data set; identifying at least two first time periods during the scanning process, each first time period corresponding to one of the two target phases; determining a second time period that encloses the at least two first time periods; and retrieving once, from the multiple scanning data sets, second scanning data sets for reconstructing phase images each of which corresponds to one target phase, the second scanning data sets being acquired at second data acquisition time points of the multiple data acquisition time points within the second time period.