Described herein are methods and systems for extracting or determining subject motion from multi-channel electrical coupling in imaging of the subject, in particular in magnetic resonance (MR) imaging of the subject. The motion can be of a region of interest of the subject (such as an organ or specific tissue). Changes in the position of the subject and the subjects organs can be monitored by measuring how external coils, such as RF coils, couple to the subject and to one another and change the scattering of the RF coils, for example scattering of RF pulses transmitted by the coils. Changes in position influence this coupling and the scattering and can be detrimental to the quality of the imaging The present methods and systems address and overcome this problem.

In a method for diffusion-weighted MR-imaging of an object, which undergoes a cyclic motion, a first sub-period type of the cyclic motion is predicted for a first acquisition timeframe, where the first sub-period type corresponds to one of two or more predefined characteristic types of sub-periods of the cyclic motion. A first amount of diffusion-weighting may be selected based on the first sub-period type. A first MR-acquisition may be carried out during the first acquisition timeframe, where a diffusion-weighting according to the first amount of diffusion-weighting is applied. An MR-image of the object is generated based on MR-data including a first MR-dataset obtained as a result of the first MR-acquisition.

Embodiments provide a computer-implemented method for selecting thermal images for generating a temperature difference map through proton resonance frequency (PRF) thermometry, including: acquiring a set of baseline images prior to a thermal treatment of an organ of interest; identifying a subset of baseline images in a most stable motion state from the set of baseline images; averaging the subset of baseline images to generate a template image; determining an acceptance threshold based on an image similarity measure (ISM) between each of the set of baseline images and the template image; acquiring a set of thermal images during the thermal treatment; and selecting a subset of thermal images from the set of thermal images, wherein each of the subset of thermal images has the image similarity measure above the acceptance threshold.

A computer-implemented method of reconstructing a motion-compensated magnetic resonance image uses raw k-space data acquired at a first resolution over successive respiratory and/or cardiac cycles of a patient. After binning data based on corresponding motion states derived from these cycles, the resolution of the binned K-space data in each bin is reduced. This is done by selecting a sub-group of binned k-space data. Bin images are reconstructed from the reduced-resolution data, and histogram-equalised versions of the reconstructed reduced-resolution bin image generated for each bin. Motion fields are estimated and interpolated to the first resolution such that motion data can be incorporated into a final reconstruction of a motion compensated image.

The invention provides for a magnetic resonance imaging system (100, 200) comprising a memory (148) for storing machine executable instructions (150) and pulse sequence commands (152). The pulse sequence commands are configured for acquiring a four dimensional magnetic resonance data set (162) from an imaging region of interest (109). The four dimensional magnetic resonance data set is at least divided into three dimensional data magnetic resonance data sets (400, 402, 404, 406, 408) indexed by a repetitive motion phase of the subject. The three dimensional data magnetic resonance data sets are further at least divided into and indexed by k-space portions (410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436). The magnetic resonance imaging system further comprises a processor (144) for controlling the magnetic resonance imaging system. Execution of the machine executable instructions causes the processor during a first operational portion (310) to iteratively: receive (300) a motion signal (156) descriptive of the repetitive motion phase; acquire (302) an initial k-space portion using the pulse sequence commands, wherein the initial k-space portion is selected from the k-space portions; store (304) the motion signal and the initial k-space portion in a buffer (158) for each iteration of the first operational portion; at least partially construct (306) a motion phase mapping (160) between the motion signal and the repetitive motion phase; and continue (308) the first operational portion until the motion phase mapping is complete. Execution of the machine executable instructions causes the processor to assign (312) the initial k-space portion for each iteration of the first operational portion in the temporary buffer to the four dimensional magnetic resonance data set using the motion phase mapping. Execution of the machine executable instructions causes the processor during a second operational portion (332) to iteratively: receive (314) the motion signal; determine (316) a predicted next motion phase using the motion signal and the motion phase mapping; select (318) a subsequent k-space portion (154) from the k-space portions of the four dimensional magnetic resonance data set using the predicted next motion phase; acquire (320) the subsequent k-space portion using the pulse sequence commands; rereceive (322) the motion signal; determine (324) a current motion phase using the re-received motion signal and the motion phase mapping; assign (326) the

Systems and Methods that identify the effect of motion during a medical imaging procedure. A neural network is trained to translate motion induced deviations of a coil-mixing matrix relative to a reference acquisition into a motion score. This score can be used for the prospective detection of the most corrupted echo trains for removal or triggering a replacement by reacquisition.

A method for adapting, per cardiac cycle, the parameters governing interpolation of varying and non-interpolation of fixed fractions of each individual cardiac cycle is provided. A time series of data values associated with a cardiac cycle is received, and the time series is scaled to a reference cardiac cycle, wherein the scaling includes applying a model to the time series to generate a scaled time series of data values associated with the first cardiac cycle. The model is trained using the scaled time series.

A medical image diagnosis apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to derive a subject-specific regression model that indicates a relationship among a cardiac cycle, systole, and diastole of the subject. The processing circuitry is configured to derive timing of a data acquisition in a synchronization imaging performed in synchronization with heartbeats of the heart of the subject, by using the derived regression model and electrocardiographic information of the subject obtained during an image taking process. The processing circuitry is configured to control the synchronization imaging so that the data acquisition is performed with the derived timing.

Systems and Methods that identify the effect of motion during a medical imaging procedure. A neural network is trained to translate motion induced deviations of a coil-mixing matrix relative to a reference acquisition into a motion score. This score can be used for the prospective detection of the most corrupted echo trains for removal or triggering a replacement by reacquisition.

One aspect of the present subject matter provides an imaging method including: receiving a trigger signal; after a period substantially equal to a trigger delay minus an inversion delay, applying a non-selective inversion radiofrequency pulse to a region of interest followed by a slice-selective reinversion radiofrequency pulse to a slice of the region of interest of a subject; and after lapse of the trigger delay commenced at the cardiac cycle signal, acquiring a plurality of time-resolved images of the slice of the region of interest from an imaging device.