Abstract: A system and method for reconstructing a series of images of a subject includes acquiring medical image data from the subject with a medical imaging system and reconstructing a series of images of the subject from the acquired medical image data set. The reconstructing includes enforcing general adherence to a non-patient-specific signal model that describes a dependency of image intensity values on at least one variable that is associated with a physical or physiological property by constraining reconstruction of individual images in the series of images using the non-patient-specific model. The reconstructing also includes preserving information in the series of images that deviate from the non-patient-specific model by controlling a requirement of consistency with the non-patient-specific model.

Abstract: A system and method is provided for producing a map of a static magnetic field (B0) of a magnetic resonance imaging system. The method includes forming a first dataset by acquiring, with the MRI system, a first plurality of different echo signals occurring at a respective plurality of different echo times. The method also includes forming a second dataset by acquiring, with the MRI system, a second plurality of different echo signals occurring at a respective plurality of different echo times. The second dataset includes signals resulting from a magnetization transfer (MT) between free water and bound molecules. The method further includes generating MT-weighted maps using the first dataset and the second dataset, determining, using the MT-weighted maps, a phase difference between the first plurality of different echo signals, and using the phase differences, generate a corrected map of the static magnetic field (B0) of the MRI system.

Abstract: A system and method for reconstructing a series of images of a subject includes acquiring medical image data from the subject with a medical imaging system and reconstructing a series of images of the subject from the acquired medical image data set. The reconstructing includes enforcing general adherence to a non-patient-specific signal model that describes a dependency of image intensity values on at least one variable that is associated with a physical or physiological property by constraining reconstruction of individual images in the series of images using the non-patient-specific model. The reconstructing also includes preserving information in the series of images that deviate from the non-patient-specific model by controlling a requirement of consistency with the non-patient-specific model.

Abstract: A system and method is provided for producing a map of a static magnetic field (B0) of a magnetic resonance imaging system. The method includes forming a first dataset by acquiring, with the MRI system, a first plurality of different echo signals occurring at a respective plurality of different echo times. The method also includes forming a second dataset by acquiring, with the MRI system, a second plurality of different echo signals occurring at a respective plurality of different echo times. The second dataset includes signals resulting from a magnetization transfer (MT) between free water and bound molecules. The method further includes generating MT-weighted maps using the first dataset and the second dataset, determining, using the MT-weighted maps, a phase difference between the first plurality of different echo signals, and using the phase differences, generate a corrected map of the static magnetic field (B0) of the MRI system.

Abstract: A system and method for reconstructing a series of images of a subject includes acquiring medical image data from the subject with a medical imaging system and reconstructing a series of images of the subject from the acquired medical image data set. The reconstructing includes enforcing general adherence to a non-patient-specific signal model that describes a dependency of image intensity values on at least one variable that is associated with a physical or physiological property by constraining reconstruction of individual images in the series of images using the non-patient-specific model. The reconstructing also includes preserving information in the series of images that deviate from the non-patient-specific model by controlling a requirement of consistency with the non-patient-specific model.

Abstract: A method of generating a suggested navigation route with a navigation device includes storing in a memory of the navigation device a map database containing names and locations of roads in a road network and off-road trail data containing locations of at least one off-road trail provided by a user of the navigation device, and merging off-road segments of the off-road trail with the roads of the road network to create a combined map database containing off-road segments and road segments. The method further includes receiving a request from the user for generating navigation directions to a destination location input by the user, generating the suggested navigation route using data stored in the combined map database, the suggested navigation route containing a combination of off-road segments and road segments, and providing navigation assistance to the user for guiding the user along the suggested navigation route.

Abstract: A method of generating a suggested navigation route with a navigation device includes storing in a memory of the navigation device a map database containing names and locations of roads in a road network and off-road trail data containing locations of at least one off-road trail provided by a user of the navigation device, and merging off-road segments of the off-road trail with the roads of the road network to create a combined map database containing off-road segments and road segments. The method further includes receiving a request from the user for generating navigation directions to a destination location input by the user, generating the suggested navigation route using data stored in the combined map database, the suggested navigation route containing a combination of off-road segments and road segments, and providing navigation assistance to the user for guiding the user along the suggested navigation route.

Abstract: A method for reconstructing an image of a subject with a medical imaging system, such as a magnetic resonance imaging system, is provided. Medical image data is acquired from the subject with the medical imaging system, and one or more images of the subject are reconstructed from the medical image data while constraining the one or more images to be consistent with a signal model that relates image intensity values in the image to a free parameter that is associated with a physical property of the subject. The signal model may be an analytical signal model or an approximate signal model learned from acquired medical image data. The model consistency condition may be enforced using an operator that projects an image estimate onto the space of all functions satisfying the signal model.

Abstract: A system and method for reconstructing a series of images of a subject includes acquiring medical image data from the subject with a medical imaging system and reconstructing a series of images of the subject from the acquired medical image data set. The reconstructing includes enforcing general adherence to a non-patient-specific signal model that describes a dependency of image intensity values on at least one variable that is associated with a physical or physiological property by constraining reconstruction of individual images in the series of images using the non-patient-specific model. The reconstructing also includes preserving information in the series of images that deviate from the non-patient-specific model by controlling a requirement of consistency with the non-patient-specific model.

Abstract: A method for reconstructing an image of a subject with a medical imaging system, such as a magnetic resonance imaging system, is provided. Medical image data is acquired from the subject with the medical imaging system, and one or more images of the subject are reconstructed from the medical image data while constraining the one or more images to be consistent with a signal model that relates image intensity values in the image to a free parameter that is associated with a physical property of the subject. The signal model may be an analytical signal model or an approximate signal model learned from acquired medical image data. The model consistency condition may be enforced using an operator that projects an image estimate onto the space of all functions satisfying the signal model.

Abstract: An image reconstruction method applicable to a number of different imaging modalities including magnetic resonance imaging (MRI), x-ray computed tomography (CT), positron emission tomography (PET), and single photon emission computed tomography (SPECT) is disclosed. A sparsifying image is reconstructed from a series of acquired undersampled data to provide a priori knowledge of a subject being imaged. An iterative reconstruction process is further employed to iteratively determine a correction image for a given image frame that, when subtracted from the sparsifying image, produces a quality image for the image frame.

Abstract: A method for reconstructing a motion-compensated image depicting a subject with a magnetic resonance imaging (MRI) system is provided. An MRI system is used to acquire a time series of k-space data from the subject by sampling k-space along non-Cartesian trajectories, such as radial, spiral, or other trajectories at a plurality of time frames. Those time frames at which motion occurred are identified and this information used to segment the time series into a plurality of k-space data subsets. For example, the k-space data subsets contain k-space data acquired at temporally adjacent time frames that occur between those identified time frames at which motion occurred. Motion correction parameters are determined from the k-space data subsets. Using the determined motion correction parameters, the k-space data subsets are corrected for motion. The corrected data subsets are combined to form a corrected k-space data set, from which a motion-compensated image is reconstructed.

Abstract: A method for reconstructing a motion-compensated image depicting a subject with a magnetic resonance imaging (MRI) system is provided. An MRI system is used to acquire a time series of k-space data from the subject by sampling k-space along non-Cartesian trajectories, such as radial, spiral, or other trajectories at a plurality of time frames. Those time frames at which motion occurred are identified and this information used to segment the time series into a plurality of k-space data subsets. For example, the k-space data subsets contain k-space data acquired at temporally adjacent time frames that occur between those identified time frames at which motion occurred. Motion correction parameters are determined from the k-space data subsets. Using the determined motion correction parameters, the k-space data subsets are corrected for motion. The corrected data subsets are combined to form a corrected k-space data set, from which a motion-compensated image is reconstructed.

Abstract: A method for magnitude constrained phase contrast magnetic resonance imaging (MRI) is provided. The method utilizes an assumption that the image magnitude is shared across a series of images reconstructed from a set of phase contrast enhanced k-space data. In this manner, one common magnitude image and a plurality of phase images are reconstructed substantially contemporaneously from the acquired image data. The method is further applicable to other phase contrast MRI methods, such as phase contract velocimetry. Moreover, simultaneous phase contrast velocimetry and chemical shift imaging, in which water and fat signal separation is achieved, is provided.

Abstract: A method for magnitude constrained phase contrast magnetic resonance imaging (MRI) is provided. The method utilizes an assumption that the image magnitude is shared across a series of images reconstructed from a set of phase contrast enhanced k-space data. In this manner, one common magnitude image and a plurality of phase images are reconstructed substantially contemporaneously from the acquired image data. The method is further applicable to other phase contrast MRI methods, such as phase contract velocimetry. Moreover, simultaneous phase contrast velocimetry and chemical shift imaging, in which water and fat signal separation is achieved, is provided.

Abstract: An image reconstruction method applicable to a number of different imaging modalities including magnetic resonance imaging (MRI), x-ray computed tomography (CT), positron emission tomography (PET), and single photon emission computed tomography (SPECT) is disclosed. A sparsifying image is reconstructed from a series of acquired undersampled data to provide a priori knowledge of a subject being imaged. An iterative reconstruction process is further employed to iteratively determine a correction image for a given image frame that, when subtracted from the sparsifying image, produces a quality image for the image frame.

Abstract: A fast and efficient method for reconstructing an image from undersampled, parallel MRI data sets acquired with non-Cartesian trajectories includes the calculation of unsampled k-space data from the acquired k-space data and sets of calculated reconstruction coefficients. To reduce the computation time, only a few reference reconstruction coefficients are calculated using a matrix inversion step and the remaining reconstruction coefficients are produced by interpolating between the reference reconstruction coefficients.

Abstract: A method for reconstructing an image from undersampled, parallel MRI data sets acquired with a pulse sequence that samples k-space along radial trajectories includes the synthesis of training k-space data from the acquired data. The training k-space data is produced by reconstructing training images from the fully sampled, central k-space region of the acquired MRI data sets, and the training k-space data is used in a radial GRAPPA image reconstruction method to produce an image frame.

Abstract: A method for reconstructing an image from undersampled, parallel MRI data sets acquired with a pulse sequence that samples k-space along radial trajectories includes the synthesis of training k-space data from the acquired data. The training k-space data is produced by reconstructing training images from the fully sampled, central k-space region of the acquired MRI data sets, and the training k-space data is used in a radial GRAPPA image reconstruction method to produce an image frame.

Abstract: A fast and efficient method for reconstructing an image from undersampled, parallel MRI data sets acquired with non-Cartesian trajectories includes the calculation of unsampled k-space data from the acquired k-space data and sets of calculated reconstruction coefficients. To reduce the computation time, only a few reference reconstruction coefficients are calculated using a matrix inversion step and the remaining reconstruction coefficients are produced by interpolating between the reference reconstruction coefficients.