Abstract: A method for subject-specific assessment of neurological disorders, the method includes receiving 3D image data representative of a subject's brain and identifying subject-specific anatomical structures in the 3D image data. A subject-specific model for electrical dynamics is created based on the 3D image data and the subject-specific anatomical structures and one or more functional indicators of neurological disorder are computed using the subject-specific model for electrical dynamics.

Abstract: Methods for computed tomography data-based cycle estimation and four-dimensional reconstruction are provided. A gated reconstruction is derived from CT data acquired without gating using an added artificial trigger. The resulting images for different slices are used to determine local or slice variations over time. The local variations over time for the various slices are combined to create a respiratory cycle signal. This respiratory cycle signal is used to bin the images for different phases, allowing four-dimensional CT reconstruction.

Abstract: A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L?1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L?1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.

Abstract: Methods for computed tomography data-based cycle estimation and four-dimensional reconstruction are provided. A gated reconstruction is derived from CT data acquired without gating using an added artificial trigger. The resulting images for different slices are used to determine local or slice variations over time. The local variations over time for the various slices are combined to create a respiratory cycle signal. This respiratory cycle signal is used to bin the images for different phases, allowing four-dimensional CT reconstruction.

Abstract: Traumatic brain injury (TBI) in a patient is assessed. A TBI diagnosis for the patient is determined based on features from MRI data, such as anatomical features, functional features, diffusion features, connectivity features from functional MRI, connectivity features from diffusion MRI, and/or network features from the connectivity features. The TBI diagnosis is determined using a trained classifier. The classifier synthesizes the features into a single number (e.g., a confidence in the prediction of the diagnosis) and indicates the features most responsible for the diagnosis. The disease trajectory for a given patient may be predicted using the trained classifier.

Abstract: Resolution is enhanced for diffusion MR imaging. The tensors modeling the underlying water diffusion in brain tissues are used to interpolate other diffusion tensors, providing higher resolution diffusion biomarker images. Each diffusion tensor is represented by a pair of elements, the one in an ‘orientation space’ and another in a ‘shape space.’ The tensors are iteratively interpolated by averaging the aforementioned elements in separate mathematical spaces. The weighted average of the shape components of the diffusion tensors is computed in closed form, which decreases the runtime.

Abstract: A method for acquiring cine images using a magnetic resonance imaging (MRI) system includes selecting an asymmetric radial sampling scheme providing an asymmetric view of k-space corresponding to a desired image resolution. Radial k-space data is acquired using the asymmetric radial sampling scheme, wherein slice-orientation of the radial k-space data is continuously modified while acquiring the radial k-space data. A plurality of cine images are reconstructed from the radial k-space data using a compressed-sensing method.

Abstract: A method of magnetic resonance (MR) imaging of a volume undergoing repetitive motion includes obtaining source slice data indicative of a plurality of source slices during the repetitive motion, and obtaining anchor slice data indicative of an anchor slice during the repetitive motion. The anchor slice intersects the plurality of source slices. The source slice data and the anchor slice data are reconstructed. A three-dimensional image assembly procedure is implemented to generate, for each phase of the repetitive motion, volume data based on a respective subset of the reconstructed source slice data. For each phase of the repetitive motion, the respective subset of slices is selected based on a correlation of the source slice data and the anchor slice data along an intersection between each source slice and the anchor slice. The source slice data of the selected subset is corrected for misalignment with the anchor slice data.

Abstract: A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L?1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L?1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.

Abstract: Traumatic brain injury (TBI) in a patient is assessed. A TBI diagnosis for the patient is determined based on features from MRI data, such as anatomical features, functional features, diffusion features, connectivity features from functional MRI, connectivity features from diffusion MRI, and/or network features from the connectivity features. The TBI diagnosis is determined using a trained classifier. The classifier synthesizes the features into a single number (e.g., a confidence in the prediction of the diagnosis) and indicates the features most responsible for the diagnosis. The disease trajectory for a given patient may be predicted using the trained classifier.