Mayo Foundation for Medical Education and Research

Joshua D Trzasko

Armando Manduca

Matthew A Bernstein

Shigao Chen

Richard L Ehman

Pengfei Song

Yunhong Shu

John Huston, Iii

Shengzhen Tao

Arvin Forghanian-Arani

Philip A Araoz

Yi Sui

Matthew C Murphy

Jonathan M Scott

Uten Yarach

James Fowler Greenleaf

Cynthia H McCollough

Lifeng Yu

Matthew W Urban

Shuai Leng

Joel G Fletcher

Kevin J Glaser

Ping Gong

Daniel C Mellema

Juan C Ramirez Giraldo

Paul T Weavers

Chengwu Huang

Shanshan Tang

Myung-Ho In

Iii John Huston

Matthew Lowerison

John Huston Iii

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.

Systems and methods for removing noise-induced bias in ultrasound blood flow imaging

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.

Continuous three-dimensional imaging for magnetic resonance elastography

Systems and methods for estimating acoustic properties in a region-of-interest in a subject or other object being imaged using a reference frequency method ('RFM') is described. Using this RFM technique, ultrasound data acquired at a given frequency are normalized by ultrasound data acquired at different frequencies (e.g., adjacent frequencies or otherwise) in order to provide estimations of the acoustic properties (e.g., attenuation coefficient, a backscatter coefficient, or both) that are independent of the ultrasound system used to acquire the underlying data. For instance, the amplitude of each frequency component can be normalized by a different frequency in the power spectrum to cancel out the system-dependent effects. Because the methods described in the present disclosure are system independent, they can be applied to any transducer geometry (e.g., linear or curved arrays) and using any beam pattern (e.g., focused or unfocused).

Methods for ultrasound system independent attenuation coefficient estimation

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.

Model-based iterative reconstruction for magnetic resonance imaging with echo planar readout

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.

Model-based Nyquist ghost correction for reverse readout echo planar imaging

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.

Systems, methods, and media for estimating a mechanical property based on a transformation of magnetic resonance elastography data using a trained artificial neural network

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.

Systems and methods for Kalman filter-based microvessel inpainting for super-resolution imaging

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.

Shear wave elastography with ultrasound probe oscillation

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.

Methods for super-resolution ultrasound imaging of microvessels

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.

Methods for estimating mechanical properties from magnetic resonance elastography data using artificial neural networks

Described here are systems and methods for imaging blood flow in a subject's vasculature, which may include small blood vessels, using ultrasound without the need for a contrast agent. A locally implemented low-rank matrix decomposition is used together with adaptive cutoff values to provide noninvasive ultrasound blood flow imaging capable of imaging the subject's vasculature with very high spatial and temporal resolution, and without the administration of contrast agents such as microbubble contrast agents. Thus, in some instances the systems and methods can be used to image blood flow in small vessels and tissue microvasculature with high spatial and temporal resolution.

Ultrasound blood flow imaging

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.

Methods for iterative reconstruction of medical images using primal-dual optimization with stochastic dual variable updating

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.

Methods for correcting motion-encoding gradient nonlinearities in magnetic resonance elastography

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.

System and method for model-based reconstruction of quantitative images

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.

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Rochester, MN, United States of America

Joshua D Trzasko