Abstract: Described here are a system and method for obtaining multiple different images when performing a single scan of a subject with a magnetic resonance imaging (“MRI”) system. The scan includes the application of two or more magnetization preparation radio frequency (“RF”) pulses, such as inversion recovery (“IR”) pulses. Data is acquired after the application of each magnetization preparation RF pulse, thus allowing the acquisition of multiple different images of the subject in a single scan. Using this approach, the same information that used to require multiple different scans of the subject can be acquired in one single scan, and in less time than would be required to perform the multiple scans.

Abstract: Described here are a system and method for obtaining a time series of images that depict a subject using an inversion recovery (“IR”) pulse sequence with a unique data acquisition scheme that allows for the retrospective identification of an image having an optimal tissue contrast. Data acquisition is performed using a radial acquisition scheme such as, preferably, a vastly undersampled isotropic projection reconstruction (“VIPR”) scheme. Using VIPR and IR, combined with a unique projection ordering, a series of three-dimensional, high spatial resolution images with multiple different image contrasts can be obtained.

Abstract: Described here are a system and method for obtaining multiple different images when performing a single scan of a subject with a magnetic resonance imaging (“MRI”) system. The scan includes the application of two or more magnetization preparation radio frequency (“RF”) pulses, such as inversion recovery (“IR”) pulses. Data is acquired after the application of each magnetization preparation RF pulse, thus allowing the acquisition of multiple different images of the subject in a single scan. Using this approach, the same information that used to require multiple different scans of the subject can be acquired in one single scan, and in less time than would be required to perform the multiple scans.

Abstract: Described here are a system and method for obtaining a time series of images that depict a subject using an inversion recovery (“IR”) pulse sequence with a unique data acquisition scheme that allows for the retrospective identification of an image having an optimal tissue contrast. Data acquisition is performed using a radial acquisition scheme such as, preferably, a vastly undersampled isotropic projection reconstruction (“VIPR”) scheme. Using VIPR and IR, combined with a unique projection ordering, a series of three-dimensional, high spatial resolution images with multiple different image contrasts can be obtained.

Abstract: Highly undersampled diffusion weighted image data sets are acquired for a plurality of different directions using a projection reconstruction pulse sequence. The acquired projection views are interleaved and are combined to form a more highly sampled data set that is used to reconstruct a composite image. A DWI image is reconstructed from each undersampled data set for each direction using a highly constrained backprojected method that employs the composite image. Diffusion tensor values are calculated from the DWI images.

Abstract: A method and system are disclosed for generating enhanced images of multiple dimensional data using a depth-buffer segmentation process. The method and system operate in a computer system modify the image by generating a reduced-dimensionality image data set from a multidimensional image by formulating a set of projection paths through image points selected from the multidimensional image, selecting an image point along each projection path, analyzing each image point to determine spatial similarities with at least one other point adjacent to the selected image point in a given dimension, and grouping the image point with the adjacent point or spatial similarities between the points is found thereby defining the data set.

Abstract: A method and system are disclosed for generating enhanced images of multiple dimensional data using a depth-buffer segmentation process. The method and system operate in a computer system modify the image by generating a reduced-dimensionality image data set from a multidimensional image by formulating a set of projection paths through image points selected from the multidimensional image, selecting an image point along each projection path, analyzing each image point to determine spatial similarities with at least one other point adjacent to the selected image point in a given dimension, and grouping the image point with the adjacent point or spatial similarities between the points is found thereby defining the data set.

Abstract: A method and system are disclosed for generating enhanced images of multiple dimensional data using a depth-buffer segmentation process. The method and system operate in a computer system modify the image by generating a reduced-dimensionality image data set from a multidimensional image by formulating a set of projection paths through image points selected from the multidimensional image, selecting an image point along each projection path, analyzing each image point to determine spatial similarities with at least one other point adjacent to the selected image point in a given dimension, and grouping the image point with the adjacent point or spatial similarities between the points is found thereby defining the data set.

Abstract: A method and system are disclosed for generating enhanced images of multiple dimensional data using a depth-buffer segmentation process. The method and system operate in a computer system modify the image by generating a reduced-dimensionality image data set from a multidimensional image by formulating a set of projection paths through image points selected from the multidimensional image, selecting an image point along each projection path, analyzing each image point to determine spatial similarities with at least one other point adjacent to the selected image point in a given dimension, and grouping the image point with the adjacent point or spatial similarities between the points is found thereby defining the data set.