Abstract: Methods for fast magnetic resonance imaging (“MRI”) using a combination of outer volume suppression (“OVS”) and accelerated imaging, which may include simultaneous multislice (“SMS”) imaging, data acquisitions amenable to compressed sensing reconstructions, or combinations thereof. The methods described here do not introduce fold-over artifacts that are otherwise common to reduced field-of-view (“FOV”) techniques.

Abstract: Described here are systems and methods for volumetric excitation in magnetic resonance imaging (“MRI”) using frequency modulated radio frequency (“RF”) pulses. In general, quadratic phase modulation along the slice encoding direction is implemented for additional spatiotemporal encoding, which better distributes signal content in the slice direction and enables higher acceleration rates that are robust to slice-undersampling.

Abstract: Systems and methods for producing quantitative maps of a longitudinal relaxation parameter, such as a longitudinal relaxation time (“T1”), using magnetic resonance imaging (“MRI”) are described. More particularly, a pulse sequence and imaging method for cardiac phase-resolved myocardial T1 mapping are provided.

Abstract: A system and method for obtaining magnetic resonance images are provided. The system is programmed to control the RF system to apply a saturation pulse at a reference frequency that saturates a selected labile spin species of the subject. The system is programmed to control the RF system to apply an inversion pulse after a variable delay. The system is programmed to control the RF system and the plurality of gradient coils to apply a motion sensitized driven equilibrium (MSDE) preparation pulse. The system is programmed to control the plurality of gradient coils to read imaging data during an acquisition time period. The system is programmed to reconstruct a T1 mapping image of the subject with black-blood contrast.

Abstract: Systems and methods for mapping the transmit sensitivity of one or more radio frequency (“RF”) coils for use in magnetic resonance imaging (“MRI”) are described. The transmit RF field (“B1+”) for an RF coil, or an array of RF coils, is mapped using a robust, motion-insensitive technique that implements Bloch-Siegert shifts performed with interleaved positive and negative off-resonance shifts. The motion insensitivity of this technique makes it particularly useful for applications where there is significant motion, such as cardiac imaging, in which previous B1+ mapping techniques are not as accurate or effective.

Abstract: A magnetic resonance imaging (MRI) system and methods are provided for producing images of a subject. In some aspects, a method includes identifying a point in the cardiac cycle, performing an inversion recovery (IR) pulse at a selected time point from the pre-determined point, and sampling a k-space segment at an inversion time from the IR pulse that is substantially coincident with the pre-determined point. The method also includes repeating the IR pulse and k-space sampling for multiple inversion times, and multiple segments of k-space, in an interleaved manner, to generate datasets having T1-weighted contrasts determined by their respective inversion times. The method further includes reconstructing three-dimensional (3D) spatially-aligned images using the datasets, and generating a T1 recovery map by combining the 3D images. In some aspects, a prospective/retrospective scheme may be used to obtain data fully sampled in the center of k-space and randomly undersampled in the outer regions.

Abstract: A magnetic resonance (MR) image processing system includes a data collection unit that acquires image data from a target region of an object. A navigator unit acquires a motion signal indicating motion comprising motion of at least a portion of an object. A data processing unit derives k-space data for a k-space data array from the acquired image data, by acquiring k-space data for a first portion of the k-space from the acquired image data in response to the motion signal indicating motion is within a predetermined range and acquiring k-space data for a second portion of the k-space from the acquired image data irrespective of the predetermined range.

Abstract: An apparatus and method process image data, including motion corrupted data, from a magnetic resonance imaging procedure to obtain and reconstruct images for cardiac, cardiovascular, coronary arterial, and/or pulmonary vein diagnoses in a subject. The apparatus and method include a processor operating predetermined software which receives the image data, classifies the received image data as accepted image data or rejected image data, and applies a predetermined relationship between the accepted image data and the rejected image data to correct for motion of the subject and to generate and output a reconstructed image of the subject corrected for the motion from the image data, with the reconstructed image having a relatively high signal-to-noise ratio.

Abstract: An MRI apparatus includes: a data processor configured to acquire a first set of T2-weighted imaging data and a second set of T2-weighted imaging data; a pulse sequence controller configured to generate a pulse sequence and apply the generated pulse sequence to a gradient coil assembly and RF coil assembly, the generated pulse sequence including: T2-preparation modules and associated imaging modules to acquire the first set of T2-weighted imaging data, and a saturation pulse sequence and an associated saturation imaging module to acquire the second set of T2-weighted imaging data; a curve fitter configured to apply the first and second sets of T2-weighted imaging data to a three-parameter model for T2 decay, to determine a T2 value at a plurality of locations; and an image processor configured to generate a T2 map of the object based on the T2 value determined at the plurality of locations.

Abstract: A magnetic resonance imaging (MRI) system and methods are provided for producing images of a subject. In some aspects, a method includes identifying a point in the cardiac cycle, performing an inversion recovery (IR) pulse at a selected time point from the pre-determined point, and sampling a k-space segment at an inversion time from the IR pulse that is substantially coincident with the pre-determined point. The method also includes repeating the IR pulse and k-space sampling for multiple inversion times, and multiple segments of k-space, in an interleaved manner, to generate datasets having T1-weighted contrasts determined by their respective inversion times. The method further includes reconstructing three-dimensional (3D) spatially-aligned images using the datasets, and generating a T1 recovery map by combining the 3D images. In some aspects, a prospective/retrospective scheme may be used to obtain data fully sampled in the center of k-space and randomly undersampled in the outer regions.

Abstract: A system and method for controlling a magnetic resonance imaging (MRI) system to acquire images of a subject having inconsistencies in a cardiac cycle of the subject. The process includes receiving an identification of a predetermined point in a cardiac cycle of the subject and, thereupon, performing a saturation module configured to dephase magnetization within a region of interest (ROI) from before the predetermined point. The process also includes performing an inversion module configured to invert spins within the ROI and acquiring medical imaging data from the subject. A delay is inserted between the performance of the saturation module and the performance of the inversion module, wherein a duration of the delay is configured, with the saturation module, to control evidence in the medical imaging data of inconsistencies in the cardiac cycle of the subject by controlling a magnetization history of tissue in the ROI.

Abstract: A magnetic resonance (MR) image processing system includes a data collection unit that acquires image data from a target region of an object. A navigator unit acquires a motion signal indicating motion comprising motion of at least a portion of an object. A data processing unit derives k-space data for a k-space data array from the acquired image data, by acquiring k-space data for a first portion of the k-space from the acquired image data in response to the motion signal indicating motion is within a predetermined range and acquiring k-space data for a second portion of the k-space from the acquired image data irrespective of the predetermined range.

Abstract: A method for reconstructing an image of a subject from undersampled image data that is acquired with an imaging system, such as a magnetic resonance imaging system or computed tomography system, is provided. From the acquired undersampled image data, an image of the subject is reconstructed and used to guide further image reconstruction. For example, a low resolution image is reconstructed from a portion of the undersampled image data, such as from a portion corresponding to the center of k-space when MRI is used. From this image, a number of similarity clusters are produced and processed. The processing may be by hard thresholding, Wiener filtering, principal component pursuit, or other similar techniques. These processed similarity clusters are then used to reconstruct a final, target image of the subject using, for example, a weighted average combination of the similarity clusters.

Abstract: A method for reconstructing an image of a subject from undersampled image data that is acquired with an imaging system, such as a magnetic resonance imaging system or computed tomography system, is provided. From the acquired undersampled image data, an image of the subject is reconstructed and used to guide further image reconstruction. For example, a low resolution image is reconstructed from a portion of the undersampled image data, such as from a portion corresponding to the center of k-space when MRI is used. From this image, a number of similarity clusters are produced and processed. The processing may be by hard thresholding, Wiener filtering, principal component pursuit, or other similar techniques. These processed similarity clusters are then used to reconstruct a final, target image of the subject using, for example, a weighted average combination of the similarity clusters.