摘要:
A method for reconstructing a time series of images from data acquired with a medical imaging system is provided. Data is acquired with the medical imaging system, and a set of image blocks that defines the location and size of each of a plurality of image blocks in the image domain is then selected. The acquired data and selected image block set are then used to jointly estimate a plurality of images that form a time series of images while promoting locally-low rank structure in the images.
摘要:
A system and method for estimating a physiological parameter from data acquired with a medical imaging system includes acquiring data with the medical imaging system. A physiological parameter is estimated from the acquired data using an iterative estimation in which a model of the medical imaging system is decoupled from a physics-based model of the acquired data.
摘要:
Systems and methods for efficiently generating MR images are provided. The method comprises acquiring k-space MR data, reconstructing an MR image from the k-space MR data, and generating the MR image. The MR image is reconstructed using an alternative-direction-method-of-multiplier (ADMM) strategy that decomposes an optimization problem into subproblems, and at least one of the subproblems is further decomposed into small problems. The further decomposition is based on Woodbury matrix identity and uses a diagonal preconditioner based on non-Toeplitz models.
摘要:
A method for reconstructing a time series of images from data acquired with a medical imaging system is provided. Data is acquired with the medical imaging system, and a set of image blocks that defines the location and size of each of a plurality of image blocks in the image domain is then selected. The acquired data and selected image block set are then used to jointly estimate a plurality of images that form a time series of images while promoting locally-low rank structure in the images.
摘要:
A system and method for generating a spatial map of parameters that describe the mechanically-induced harmonic motion information present within a magnetic resonance elastography (MRE) data set is provided. A first temporal harmonic signal is estimated using a graph-cut based optimization strategy, and can subsequently be used to generate a the spatial map of mechanical parameters. The MRE data set is used to estimate the harmonic. The spatial map is of a mechanical parameter derived from the estimated harmonic.
摘要:
A method for image reconstruction that utilizes the benefits of compressed sensing (“CS”) while incorporating a priori knowledge of object spatial support into the image reconstruction is provided. Image data is acquired from a subject, for example, with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system or a computed tomography (“CT”) system. An estimate of the spatial support of the subject is produced, for example, using a low resolution image of the subject, or an image reconstructed from undersampled image data in a traditional sense. An estimate image of the subject is also produced by using traditional image reconstruction methods on the acquired image data. An image of the subject is then reconstructed using the produced estimate image and produced spatial support estimate. This method allows for the reconstruction of quality images from undersampled image data in a computationally efficient manner.
摘要:
A method for image reconstruction that utilizes a generalization of compressed sensing is provided. More particularly, a method for homotopic l0 minimization is provided, in which a series of subproblems that asymptotically approach a solution to the l0 minimization are iteratively solved. These subproblems include utilizing concave metric prior functionals in the traditional compressed sensing framework. Substantially undersampled image data is acquired from a subject, for example, with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system or a computed tomography (“CT”) system. Using the provided method, undersampling on the order of around 96 percent can be achieved while still producing clinically acceptable images.
摘要:
A system and method for generating a spatial map of parameters that describe the mechanically-induced harmonic motion information present within a magnetic resonance elastography (MRE) data set is provided. A first temporal harmonic signal is estimated using a graph-cut based optimization strategy, and can subsequently be used to generate a spatial map of mechanical parameters. The MRE data set is used to estimate the harmonic. The spatial map is of a mechanical parameter derived from the estimated harmonic.
摘要:
Systems and methods for efficiently generating MR images are provided. The method comprises acquiring k-space MR data, reconstructing an MR image from the k-space MR data, and generating the MR image. The MR image is reconstructed using an alternative-direction-method-of-multiplier (ADMM) strategy that decomposes an optimization problem into subproblems, and at least one of the subproblems is further decomposed into small problems. The further decomposition is based on Woodbury matrix identity and uses a diagonal preconditioner based on non-Toeplitz models.
摘要:
A method for image reconstruction that utilizes the benefits of compressed sensing (“CS”) while incorporating a priori knowledge of object spatial support into the image reconstruction is provided. Image data is acquired from a subject, for example, with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system or a computed tomography (“CT”) system. An estimate of the spatial support of the subject is produced, for example, using a low resolution image of the subject, or an image reconstructed from undersampled image data in a traditional sense. An estimate image of the subject is also produced by using traditional image reconstruction methods on the acquired image data. An image of the subject is then reconstructed using the produced estimate image and produced spatial support estimate. This method allows for the reconstruction of quality images from undersampled image data in a computationally efficient manner.