Abstract: A method for creating an energy series of images acquired using a multi-energy computed tomography (CT) imaging system having a plurality of energy bins includes acquiring, with the multi-energy CT imaging system, a series of energy data sets, where each energy data set is associated with at least one of the energy bins. The method includes producing a conglomerate image using at least a plurality of the energy data sets and, using the conglomerate image, reconstructing an energy series of images, each image in the energy series of images corresponding to at least one of the energy data sets.

Abstract: A method for creating an energy series of images acquired using a multi-energy computed tomography (CT) imaging system having a plurality of energy bins includes acquiring, with the multi-energy CT imaging system, a series of energy data sets, where each energy data set is associated with at least one of the energy bins. The method includes producing a conglomerate image using at least a plurality of the energy data sets and, using the conglomerate image, reconstructing an energy series of images, each image in the energy series of images corresponding to at least one of the energy data sets.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquiring image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image. Selective combination typically involves registering frames of the time-series of 2D images with the 3D image, projecting pixel values from the 2D image frames “into” the 3D image, and weighting the 3D image with the projected pixel values for each frame of the time-series of 2D images.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquiring image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquiring image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image. Selective combination typically involves registering frames of the time-series of 2D images with the 3D image, projecting pixel values from the 2D image frames “into” the 3D image, and weighting the 3D image with the projected pixel values for each frame of the time-series of 2D images.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquiring image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquired image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image. Selective combination typically involves registering frames of the time-series of 2D images with the 3D image, projecting pixel values from the 2D image frames “into” the 3D image, and weighting the 3D image with the projected pixel values for each frame of the time-series of 2D images.

Abstract: A night vision device and method for filtering a series of image frames that depict a moving subject, which thereby improves the signal-to-noise ratio of each image frame, is provided. A composite image is formed for each image frame by combining pixel values in a current image frame with pixel values in composite images corresponding to image frames acquired before the current image frame. Additionally, pixels values in image frames acquired subsequent to the acquisition of the current image frame are included when forming the composite image. A bi-directional recursive filter is used to weight the contributions from the previous composite images and subsequent image frames with a decay constant. Motion of the imaging system is optionally compensated for by establishing a moving reference frame and shifting the image frames to account for this motion; thus, registering the image frames before filtering the current image frame.

Abstract: A composite image is produced for use in HYPR processing a current image frame. The amount of a priori data used to form the composite is determined by the amount of subject motion. The current composite image may be spatially registered with the current image frame to offset subject motion before being used to form an updated composite image. Subject motion may be analyzed on a frame-by-frame basis, a region-by-region basis or a pixel-by-pixel basis to optimize the SNR of the updated composite image.

Abstract: A method is disclosed of providing time dependent three dimensional imaging of a region of a patient comprising blood vessels in a perfusion bed.

Abstract: A method is disclosed for producing a computed tomographic image of a subject, the method including: using a radiation source and detector, obtaining radiation transmission information relating to a region of interest in the subject; using the source and detector; obtaining a series of projection images of the region of interest. Each projection image is obtained by: directing an imaging beam of radiation from the source through the region of interest onto the detector along a respective direction; the detector having a detection area.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquiring image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquiring image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquired image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image. Selective combination typically involves registering frames of the time-series of 2D images with the 3D image, projecting pixel values from the 2D image frames “into” the 3D image, and weighting the 3D image with the projected pixel values for each frame of the time-series of 2D images.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquiring image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image. Selective combination typically involves registering frames of the time-series of 2D images with the 3D image, projecting pixel values from the 2D image frames “into” the 3D image, and weighting the 3D image with the projected pixel values for each frame of the time-series of 2D images.

Abstract: A method for generating time-resolved 3D medical images of a subject by imparting temporal information from a time-series of 2D medical images into 3D images of the subject. Generally speaking, this is achieved by acquired image data using a medical imaging system, generating a time-series of 2D images of a ROI from at least a portion of the acquired image data, reconstructing a 3D image substantially without temporal resolution from the acquired image data, and selectively combining the time series of 2D images with the 3D image. Selective combination typically involves registering frames of the time-series of 2D images with the 3D image, projecting pixel values from the 2D image frames “into” the 3D image, and weighting the 3D image with the projected pixel values for each frame of the time-series of 2D images.

Abstract: A system and method for generating time-resolved 3D medical images of a subject includes acquiring a time series of two-dimensional (2D) data sets from a portion of the subject using a magnetic resonance imaging (MRI) system and reconstructing the time series of 2D data sets into a 2D time series of images of the subject having a given frame rate. The process also includes acquiring a time-independent, 3D volume of the portion of the subject and combining the 2D time series of images of the subject with the time-independent 3D volume of the subject to generate a set of time-dependent 3D volume images of the portion of the subject at the given frame rate.

Abstract: A night vision device and method for filtering a series of image frames that depict a moving subject, which thereby improves the signal-to-noise ratio of each image frame, is provided. A composite image is formed for each image frame by combining pixel values in a current image frame with pixel values in composite images corresponding to image frames acquired before the current image frame. Additionally, pixels values in image frames acquired subsequent to the acquisition of the current image frame are included when forming the composite image. A bi-directional recursive filter is used to weight the contributions from the previous composite images and subsequent image frames with a decay constant. Motion of the imaging system is optionally compensated for by establishing a moving reference frame and shifting the image frames to account for this motion; thus, registering the image frames before filtering the current image frame.

Abstract: A night vision device and method for filtering a series of image frames that depict a moving subject, which thereby improves the signal-to-noise ratio of each image frame, is provided. A composite image is formed for each image frame by combining pixel values in a current image frame with pixel values in composite images corresponding to image frames acquired before the current image frame. Additionally, pixels values in image frames acquired subsequent to the acquisition of the current image frame are included when forming the composite image. A bi-directional recursive filter is used to weight the contributions from the previous composite images and subsequent image frames with a decay constant. Motion of the imaging system is optionally compensated for by establishing a moving reference frame and shifting the image frames to account for this motion; thus, registering the image frames before filtering the current image frame.

Abstract: A method for creating an energy series of images acquired using a multi-energy computed tomography (CT) imaging system having a plurality of energy bins includes acquiring, with the multi-energy CT imaging system, a series of energy data sets, where each energy data set is associated with at least one of the energy bins. The method includes producing a conglomerate image using at least a plurality of the energy data sets and, using the conglomerate image, reconstructing an energy series of images, each image in the energy series of images corresponding to at least one of the energy data sets.