The General Hospital Corporation

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Daniel Polak

Stephen Farman Cauley

Kawin Setsompop

Daniel Nicolas Splitthoff

Esther Raithel

Lawrence Leroy Wald

Thomas Beck

Berkin Bilgic

Bryan Clifford

Julian Hossbach

Borjan Aleksandar Gagoski

Huihui Ye

Yen Mei Lisa Chuah

Wei-Ching Lo

A method for acquiring a magnetic resonance image dataset of an object includes using an imaging protocol in which a number of k-space lines are acquired in one shot. The imaging protocol includes a plurality of shots. A plurality of additional k-space lines are acquired in at least a subset of the shots, such that movement of the object is detected throughout the imaging protocol. A method for generating a motion-corrected magnetic resonance image dataset from the dataset thus acquired, a magnetic resonance imaging apparatus, and a computer program are also provided.

Methods for acquiring a magnetic resonance image dataset and for generating a motion-corrected image dataset

A method is provided for acquiring a magnetic resonance image dataset of an object, using an imaging protocol in which several k-space lines are acquired in one echo train, wherein the echo train may include one or several sub echo trains, and wherein the imaging protocol includes a plurality of echo trains. Within the method, a set of additional k-space lines within a central region of k-space is acquired a number of times per echo train, wherein the number is greater than 1, in order to detect movement of the object during the echo train. The sets of additional k-space lines are acquired at pre-determined positions within the echo trains. The disclosure is also directed to a method for generating a motion-corrected magnetic resonance image dataset from the dataset thus acquired, a magnetic resonance imaging apparatus, and a computer program.

Method for acquiring a magnetic resonance image dataset and generating a motion-corrected image dataset

A method for reconstructing a motion-corrected magnetic resonance image of a subject includes providing k-space magnetic resonance data including a plurality of shots, wherein each shot corresponds to an individual motion state of the subject. The method further includes providing motion parameters related to each motion state, determining redundancies across the motion states of the plurality of shots based on the motion parameters, compressing the plurality of motion states based on the determined redundancies across the motion states, and reconstructing the magnetic resonance image from the k-space magnetic resonance data based on the compressed plurality of motion states.

Method for reconstructing a motion-corrected magnetic resonance image of a subject

In a method and system for reducing motion artifacts in magnetic resonance image data, a scout scan of the region of the patient is performed, a magnetic resonance (MR) measurement of the region of the patient is performed to acquire MR image data of the region of the patient, and motion correction is performed on the acquired MR image data based on the scout scan to generate corrected MR image data. The motion correction technique advantageously reduces an influence of a patient motion on the magnetic resonance image data.

Scout acquisition enables rapid motion estimation and reduction (SAMER) systems and methods for retrospective motion mitigation

A method for reconstructing a MRI image may include: receiving MRI measurement data sets fto f, each data set being acquired from an examination object based on a different MRI protocol of an MRI system; receiving MRI images uto ucorresponding to the MRI measurement data sets fto f; performing one or more translation and rotation transformations on the MRI images uto u; applying one or more trained functions: to the transformed MRI images uto u, using a neural network, and to the MRI images uto u, using a forward-sampling operator; performing one or more inverse translation and rotation transformations on an output of the neural network; and generating at least one output MRI image ubased on an output of the forward-sampling operator, the inversely transformed output of the neural network, and the input MRI images uto u.

Multi-contrast MRI image reconstruction using machine learning

A three-dimensional magnetic resonance image dataset of an object is acquired using a multi-shot imaging protocol in which several k-space lines are acquired in one shot. The three-dimensional k-space includes a central region and a periphery, wherein the sampling order of k-space lines differs between the central region and the periphery. At least one k-space line from each shot passes through the central region, whereas the periphery includes regions, which are sampled by k-space lines from a subset of the plurality of shots.

Method for acquiring a three-dimensional magnetic resonance image dataset and for generating a motion-corrected image dataset

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.

Coil mixing error matrix and deep learning for prospective motion assessment

Techniques are disclosed related to the compensation of phase variations introduced into k-space lines, which cause imaging artifacts. The techniques utilize the detection of motion via an encoding plus motion model, which does not require the use of additional prospective or retrospective motion detection techniques. The techniques described herein use the encoding plus motion model to reconstruct an initial image from a set of motion states, and then calculate phase information from images that are projected form the initial reconstructed image using a projection onto convex sets (POCS). The phase information is incorporated into the encoding plus motion model over several iterations to minimize data consistency error, thereby generating a refined image that compensates for patient motion over the set of motion states.

Phase estimation for retrospective motion correction

In a medical imaging auto-calibrated reconstruction method, an imaging scan is performed using a data acquisition scanner to generate image data, calibration data having a uniform sampling is determined, a point-spread function is determined based on the calibration data, and an image is reconstructed from the image data based on the point-spread function. A central region of k-space may have uniform sampling. The calibration data may be determined by extracting a uniformly-sampled central region of k-space from the image data. An outer region of k-space may have non-uniform sampling. A calibration scan may be performed to generate the calibration data.

Systems and methods for extending reconstructions to non-uniform k-space sampling

In a method and system for reducing motion artifacts in magnetic resonance image data, a scout scan (e.g. a three-dimensional (3D) scout scan) of the region of the patient is performed, a magnetic resonance (MR) measurement of the region of the patient is performed to acquire two-dimensional (2D) MR image data of the region of the patient, and motion correction is performed on the acquired 2D MR image data based on the scout scan to generate corrected MR image data. The motion correction technique advantageously reduces an influence of a patient motion on the magnetic resonance image data.

Systems and methods for separable motion estimation and correction using rapid three-dimensional (3D) volumetric scout acquisition

A method includes determining an initial magnetic resonance imaging, MRI, dataset () in image domain based on an initial reconstruction of MRI measurement data obtained using an undersampling scheme (); and determining patches (-) of the initial MRI dataset () in accordance with a patching scheme, the patching scheme depending on the undersampling scheme (); and, for each one of the patches (-): applying a machine-learned algorithm to obtain a respective patch (-) of a reconstructed MRI dataset, the machine-learned algorithm depending on the undersampling scheme (); and combining the patches (-) of the reconstructed MRI dataset.

Reconstruction of magnetic-resonance datasets using machine learning

Techniques are disclosed related to the compensation of phase offsets introduced into k-space lines as a result of encoding of blip gradients due when motion is present, which may be used for parallel magnetic resonance imaging (MRI) techniques such as blipped SMS or blipped CAIPIRINHA. The compensation of these additional phase offsets may prevent artifacts that would otherwise be present in the reconstructed images as a result of motion during the MRI scanning procedure. The additional phase offsets may be accounted for during the image acquisition phase of the MRI scan or, alternatively, during the image reconstruction phase.

Motion corrected blipped CAIPIRINHA and SMS

A computer-implemented method for reconstructing a MRI image, including: receiving a plurality of MRI measurement data sets &#x192;to &#x192;, wherein each data set is acquired from an examination object based on a different MRI protocol of an MRI system; receiving MRI images uto ucorresponding to the MRI measurement data sets &#x192;to &#x192;; applying, in at least a first step GD, trained functions to the MRI images uto u, using a neural network and a forward-sampling operator, wherein at least one output MRI image uis generated; and providing the at least one output MRI image u, wherein the forward-sampling operator determines an agreement between at least one MRI image uto uand the corresponding MRI measurement data set &#x192;to &#x192;.

Methods for reducing scan time in magnetic resonance imaging ('MRI'), particularly when imaging three-dimensional image volumes, using a simultaneous time-interleaved multislice ('STIMS') acquisition are described. The unused time in each repetition time (&#x201c;TR&#x201d;) period is exploited to provide an additional reduction in encoding time for a three-dimensional acquisition (e.g., a 3D whole brain coverage). Groups of spatially interleaved slices are excited in a single TR, with the excitation and acquisition of the groups of slices being interleaved in time.

Method for simultaneous time-interleaved multislice magnetic resonance imaging

Method for MR imaging of an acquisition region during a patient examination. In order to determine a point spread function, in a prior measurement for each of additional gradient output directions, the method includes choosing, in the acquisition region, a slice lying outside of an isocenter of the MR device, which slice extends in a plane perpendicular to the additional gradient output direction under consideration; following a respective slice-selective excitation of the selected slice, acquiring first calibration data using the additional gradient pulse of the additional gradient output direction under consideration, and acquiring second calibration data omitting the additional gradient pulse in each case along a k-space line, wherein a same timing sequence of additional gradient pulse and readout time window is used as in the MR sequence; and calculating, from the first and second calibration data, the point spread function for the additional gradient output direction under consideration.

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Blankenbach, Germany

Daniel Polak