The invention relates to the field of magnetic resonance (MR) imaging. It concerns an oscillation applicator for MR rheology. It is an object of the invention to provide an oscillation applicator without restrictions regarding the usability for certain body regions. According to the invention, the oscillation applicator comprises at least one transducer which generates a reciprocating motion at a given frequency and a belt (19) mechanically coupled to the transducer, which belt (19) is designed to be wrapped around a patient's body (10). Moreover, the invention relates to a MR device (1) and to a method of MR imaging.

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.

In Magnetic Resonance Acoustic Radiation Force Imaging (MR-ARFI), an MR imaging device (10) performs gradient echo imaging including successive MR dynamics with opposite encoding of displacement to generate MR-ARFI data of a subject comprising successive image frames with opposite displacement encoding. An ultrasound device (12) applies sonication to the subject during the gradient echo imaging. An electronic processor (22) performs MR-ARFI data processing applied to image elements at image frames of the MR-ARFI data. A displacement is computed (30) for the image element at the image frame as proportional to a phase difference between the image element in the image frame and the image element in a succeeding or preceding image frame with opposite displacement encoding. The computed displacement is corrected (32) for a temperature change between the image frame and the succeeding or preceding image frame. The temperature change is determined using the MR-ARFI data.

This shear wave imaging method, for collecting information on a target region (R) of a soft solid (S), comprises at least the steps a) of generating at least one shear wave (SW) in the target region, and b) of detecting a propagation pattern of the shear wave in the target region. Step a) is realized by applying to particles of the target region (R) some Lorentz forces resulting from an electric field (E) and from a magnetic field (B). At least one of the electric field (E) and the magnetic field (B) is variable in time, with a central frequency (f.sub.o) between 1 Hz and 10 kHz. Alternatively, both the electric and magnetic fields (E, B) are variable in time, with a central difference frequency (f.sub.o) between 1 Hz and 10 kHz. The shear wave imaging installation comprises a first system (4, 7) for generating at least one shear wave (SW) in the target region (R) and a second system (10) for detecting a propagation pattern of the shear wave. The first system includes first means (4) to apply an electric field (E) through the target region (R) and second means (7) to apply a magnetic field (B) through the target region. The first and second means are configured to apply to particles of the target region some Lorentz forces resulting from the electric field (E) and the magnetic field (B), where at least one of these fields is a quantity variable in time, with a central frequency (f.sub.o) between 1 Hz and 10 kHz, or both fields are quantities variable in time, with a central difference frequency (f.sub.o) between 1 Hz and 10 kHz.

A medical apparatus (200, 300, 400, 500) determines the concentration distribution of sonically dispersive elements (606, 2001) within a subject (306, 604, 1004), wherein the medical apparatus comprises: a memory (212) for storing machine executable instructions (224, 226, 228, 230, 232, 318) and a processor (206) for executing the machine executable instructions. Execution of the instructions cause the processor to: receive (100) shear wave data (214) descriptive of the propagation of shear waves (310, 608, 1118) within the subject for at least two frequencies; determine (102) a mechanical property (316, 618, 620) of the subject using the shear wave data at each of the at least two frequencies; determine (104) a power law relationship (218, 702) between the at least two frequencies and the mechanical property; and determine (106) the concentration distribution of the sonically dispersive elements within the subject using the power law relationship and calibration data (222, 704, 800).

During operation, a system may apply a polarizing field and an excitation sequence to a sample. Then, the system may measure a signal associated with the sample for a time duration that is less than a magnitude of a relaxation time associated with the sample. Next, the system may calculate the relaxation time based on a difference between the measured signal and a predicted signal of the sample, where the predicted signal is based on a forward model, the polarizing field and the excitation sequence. After modifying at least one of the polarizing field and the excitation sequence, the aforementioned operations may be repeated until a magnitude of the difference is less than a convergence criterion. Note that the calculations may be performed concurrently with the measurements and may not involve performing a Fourier transform on the measured signal.

A method for computing global longitudinal strain from cine magnetic resonance (MR) images includes automatically detecting landmark points in each MR long axis image frame included in a cine MR image sequence. A deformation field is determined between every pair of frames based on the landmark points. Myocardial pixels in the frames are labeled using a deep learning framework to yield myocardium masks. These myocardium masks are propagated to each frame using the deformation fields. A polar transformation is performed on each of the masked frames. The contours of the myocardium in each transformed frame are computed using a shortest path algorithm. Next, longitudinal strain is calculated at every pixel in the myocardium for the polar frames using the contours of the myocardium. Then, global longitudinal strain is computed by averaging the longitudinal strain at every pixel in the myocardium of the transformed frames.

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.

During operation, a system may apply an external magnetic field and an RF pulse sequence to a sample. Then, the system may measure at least a component of a magnetization associated with the sample, such as MR signals of one or more types of nuclei in the sample. Moreover, the system may calculate at least a predicted component of the magnetization for voxels associated with the sample based on the measured component of the magnetization, a forward model, the external magnetic field and the RF pulse sequence. Next, the system may solve an inverse problem by iteratively modifying the parameters associated with the voxels in the forward model until a difference between the predicted component of the magnetization and the measured component of the magnetization is less than a predefined value. Note that the calculations may be performed concurrently with the measurements and may not involve performing a Fourier transform.

A method for computing global longitudinal strain from cine magnetic resonance (MR) images includes automatically detecting landmark points in each MR long axis image frame included in a cine MR image sequence. A deformation field is determined between every pair of frames based on the landmark points. Myocardial pixels in the frames are labeled using a deep learning framework to yield myocardium masks. These myocardium masks are propagated to each frame using the deformation fields. A polar transformation is performed on each of the masked frames. The contours of the myocardium in each transformed frame are computed using a shortest path algorithm. Next, longitudinal strain is calculated at every pixel in the myocardium for the polar frames using the contours of the myocardium. Then, global longitudinal strain is computed by averaging the longitudinal strain at every pixel in the myocardium of the transformed frames.