Abstract: In accordance with some embodiments, systems, methods, and media for estimating a mechanical property based on a transformation of magnetic resonance elastography (MRE) data using a trained artificial neural network are provided. In some embodiments, a system is provided, the system comprising: a hardware processor programmed to: receive displacement data of tissue in vivo; provide the displacement data to a trained ANN that was trained using noisy input datasets as training data, and derivative datasets corresponding to the noisy input datasets to evaluate performance during training, such that the trained ANN provides an output dataset corresponding to an analytical solution to a derivative of a function represented in an unlabeled input dataset thereby transforming the unlabeled input dataset into its derivative; receive, from the trained ANN, an output dataset indicative of a derivative of the displacement data; and estimate stiffness of the tissue based on the derivative.

Abstract: Described here are systems and methods for super-resolution imaging with ultrasound in which a Kalman filter-based microvessel inpainting technique is used to facilitate robust super-resolution imaging with limited or otherwise missing microbubble signals. The systems and methods described in the present disclosure can be combined with both local and global microbubble tracking methods.

Abstract: Methods for processing data acquired using ultrasound elastography, in which shear waves are generated in a subject using continuous vibration of the ultrasound transducer, are described. The described methods can effectively separate shear wave signals from signals corresponding to residual motion artifacts associated with vibration of the ultrasound transducer. The systems and methods described here also provide for real-time visualization of shear waves propagating in the subject.

Abstract: Described here are systems and methods for a robust magnetic resonance elastography (“MRE”) imaging platform for rapid dynamic 3D MRE imaging. The imaging platform includes an MRE pulse sequence and advanced image reconstruction framework that work synergistically in order to greatly expand the domains where MRE can be deployed successfully.

Abstract: Systems and methods for super-resolution ultrasound imaging of microvessels in a subject are described. Ultrasound data are acquired from a region-of-interest in a subject who has been administered a microbubble contrast agent. The ultrasound data are acquired while the microbubbles are moving through, or otherwise present in, the region-of-interest. The region-of-interest may include, for instance, microvessels or other microvascuiature in the subject. By isolating, localizing, tracking, and accumulating the microbubbles in the ultrasound data, super-resolution images of the microvessels can be generated.

Abstract: Magnetic resonance elastography (“MRE”), or other imaging-based elastography techniques, generate estimates of the mechanical properties, such as stiffness and damping ratio, of tissues in a subject. A machine learning approach, such as an artificial neural network, is implemented to perform an inversion of displacement data in order to generate the estimates of the mechanical properties.

Abstract: Nyquist ghost artifacts in echo planar imaging (“EPI”) are mitigated, reduced, or otherwise eliminated by implementing robust Nyquist ghost correction (“NGC”) directly from two reversed readout EPI acquisitions. As one advantage, these techniques do not require explicit reference scanning A model-based process is used for directly estimating statistically optimal NGC coefficients from multi-channel k-space data.

Abstract: Methods for reconstructing images of a subject from data acquired with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system, are described. In general, the image reconstructions implement a primal-dual optimization strategy that, in each iteration, stochastically updates a dual variable.

Abstract: Super-resolution ultrasound imaging of microvessels in a subject is described. Ultrasound data are acquired from a region-of-interest in a subject who has been administered a microbubble contrast agent. The ultrasound data are acquired while the microbubbles are moving through, or otherwise present in, the region-of-interest. Microbubble signals are isolated from the ultrasound data and are separated into subsets of data based on properties of the microbubbles, such as spatial-temporal hemodynamics. By localizing, tracking, and accumulating the microbubbles in each subset of data, super-resolution images of the microvessels can be generated for each subset, such that each of these images represents a sparse subset of microbubble signals. These images are combined to generate a super-resolution microvessel image.

Abstract: Images are reconstructed from k-space data using a model-based image reconstruction that prospectively and simultaneously accounts for multiple non-idealities in accelerated single-shot-EPI acquisitions. In some implementations, nonlinear regularization (e.g., sparsity regularization) is also incorporated to mitigate noise amplification. The reconstructed images have reduced distortions and noise amplification effects relative to those images that are processed using conventional post-reconstruction techniques to correct for non-idealities.

Abstract: Described here are systems and methods for correcting motion-encoding gradient nonlinearities in magnetic resonance elastography (“MRE”). In general, the systems and methods described in the present disclosure compute gradient nonlinearity corrected displacement data based on information about the motion-encoding gradients used when acquiring magnetic resonance data.

Abstract: Described here are systems and methods for super-resolution imaging with ultrasound in which a Kalman filter-based microvessel inpainting technique is used to facilitate robust super-resolution imaging with limited or otherwise missing microbubble signals. The systems and methods described in the present disclosure can be combined with both local and global microbubble tracking methods.

Abstract: Methods for processing data acquired using ultrasound elastography, in which shear waves are generated in a subject using continuous vibration of the ultrasound transducer, are described. The described methods can effectively separate shear wave signals from signals corresponding to residual motion artifacts associated with vibration of the ultrasound transducer. The systems and methods described here also provide for real-time visualization of shear waves propagating in the subject.

Abstract: Systems and methods for removing the bias induced by noise from power Doppler images to achieve improvements of microvessel image contrast are provided. In one example, the noise-induced bias can be suppressed by utilizing the characteristics of uncorrelated noise in the ultrasound image from data acquired or compounded at different transmitting angles. In another example, the noise-induced bias can be suppressed due to the lack of correlation between adjacent ultrasound images. These example implementations may also be combined, as will be described below.

Abstract: The present disclosure addresses the challenges of in vivo phosphorus imaging by providing a clinically useful phosphorus MRI (PMRI) system and method that may be performed on a standard MRI system in a clinically reasonable scan time using specifically tuned coils, a phosphorus pulse sequence, and improved reconstruction and post processing algorithms.

Abstract: Described here are systems and methods for correcting motion-encoding gradient nonlinearities in magnetic resonance elastography (“MRE”). In general, the systems and methods RF described in the present disclosure compute gradient nonlinearity corrected displacement data based on information about the motion-encoding gradients used when acquiring magnetic resonance data.

Abstract: Methods for reconstructing images of a subject from data acquired with a medical imaging system, such as a magnetic resonance imaging (“MRI”) system, are described. In general, the image reconstructions implement a primal-dual optimization strategy that, in each iteration, stochastically updates a dual variable.

Abstract: Described here are systems and methods for magnetic resonance elastography (“MRE”), or other imaging-based elastography techniques, in which a machine learning approach, such as an artificial neural network, is implemented to perform an inversion of displacement data in order to generate estimates of the mechanical properties, such as stiffness and damping ratio, of tissues in a subject.

Abstract: 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.

Abstract: Systems and methods for performing concomitant field corrections in magnetic resonance imaging (“MRI”) systems that implement asymmetric magnetic field gradients are provided, in general, the systems and methods described here can correct for the effects of concomitant fields of multiple orders, such as zeroth order, first order, and second order concomitant fields.