Siemens Healthcare Gmbh

King's College London

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Centre National De La Recherche Scientifique

The United States of America, as Represented by the Secretary, Department of Health and Human Services

Institut National De La Sante Et De La Recherche Medicale

Centre National De La Recherche Scientifique-Cnrs

Universite Paris Cité

Institut National De La Sante Et De La Recherche Medicale (Inserm)

Universite Paris Xiii Paris-Nord

Université Paris Xiii Paris-Nord Villetaneuse

Radhouene Neji

Karl-Philipp Kunze

Rene Botnar

Claudia Prieto

Sebastien Roujol

Li Huang

David Andrew Porter

Ralph Sinkus

Omar Darwish

Raphael Tomi-Tricot

Aurelien Bustin

Martin Requardt

Christoph Forman

Stephan Kannengießer

Ahmed M Gharib

Amedeo Chiribiri

Christian Schuster

Iulius Dragonu

Giulio Ferrazzi

Sarah McElroy

Giorgia Milotta

Teresa Correia

Kuberan Pushparajah

Belinda Ding Yuan

Nashiely Sofia Pineda Alonso

Ali Aghaeifar

Reza Razavi

Grzegorz Kowalik

Giulia Ginami

Donovan Tripp

One or more example embodiments discloses a computer-implemented method of reconstructing a dynamic series of magnetic resonance images of a patient, comprising acquiring first and second k-space data of the patient; reconstructing at least one proton density weighted image based on the first k-space data; generating a dynamic series of processing images based on the second k-space data and temporal regularization; applying a motion correction to processing image based on an estimated motion; registering the proton density weighted image to the motion corrected image; and applying a signal intensity correction to the dynamic series of motion corrected images based on the proton density weighted image.

Computer-implemented method, computer program and processing apparatus for reconstructing a dynamic series of magnetic resonance images

Techniques are described for processing measurement information to select a respective label for multiple voxels or pixels of a medical image dataset. Reference information is described comprising a respective reference dataset for each one of the possible labels, and each label is associated with a material parameter. A respective label for the respective voxel or pixel is selected by using a discrete optimization algorithm to optimize a cost function comprising a first partial cost function and a second partial cost function, and the incorporation of the second partial cost function into the cost function introduces a coupling between the labels at different voxels or pixels.

Processing measurement information to select labels for voxels or pixels of a medical image dataset

One or more example embodiments of the present invention relates to a method for generating MR images of the heart, the method comprising acquiring and reconstructing a first set of 2D reference images of the heart during a reference heartbeat, the first set comprising a first reference image acquired along a first reference plane position thorough the heart and acquiring a second set of 2D images of the heart during a second heartbeat.

Prospective slice tracking through interleaved perpendicular acquisitions of dynamic 2D cardiovascular MRI

A method for optimization of an RF pulse that is multi-band. The RF pulse is a spokes RF pulse including a train of sub-pulses. The method includes using a starting k-space position as a current k-space position, and for each of slices to be excited by the RF pulse, performing: calculating a sub-pulse based on the current k-space position and calculating an expected magnetization after that sub-pulse; calculating an inverse Fourier transform of a difference between an expected magnetization and a target magnetization; and determining an optimal k-space position for a next spoke for this slice to be at a position where an absolute value of the inverse Fourier transform has a maximum. A next k-space position is determined for all slices together based on the optimal k-space positions determined for each slice individually. A multi-band RF pulse is determined based on the determined k-space positions.

Method for subject-specific optimization of a multi-band RF pulse

Cardiac MR data sets are acquired over cardiac cycles and based on a trigger event, with each cardiac MR data set having a corresponding time stamp. A series of time differences is determined between acquisitions of the cardiac MR data sets based on the time stamps, and a default time difference representing the time difference between two consecutive cardiac MR data sets is determined, which were acquired using correct trigger events. A pair of incorrect time differences is determined, each incorrect time difference representing a time difference between two consecutive MR data sets in which at least one MR data set was acquired using an incorrect trigger event. Approved MR data sets are determined comprising cardiac MR data sets from the cardiac MR data sets which were acquired using the correct trigger event, based on the pair of incorrect time differences, and the approved MR data sets are further processed.

Detection of mis-triggering in heart MRI

A method and a magnetic resonance tomography unit for tracking a medical instrument are disclosed. In the method, a tracking volume is defined enclosing a predefined trajectory of the medical instrument. Two-dimensional (2D) projection images of the tracking volume in presence of the medical instrument are acquired with the magnetic resonance tomography unit from two different view angles. Reference 2D images for the two view angles are determined and a background of the 2D projection images is suppressed. Two sets of 2D coordinates of the medical instrument are extracted from the two 2D projection images and combined to a 3D coordinate set and output on a display.

Device and method for 3D real-time medical instrument tracking

A Computer-implemented method of reconstructing a dynamic series of motion-compensated magnetic resonance images of a patient is provided. Images of a patient are acquired over time, at least partially in free-breathing, at a first image resolution and on a frame-by-frame basis. Each frame of the k-space data includes a first subset of data points having a first sample density and a second subset of data points having a second sample density. For each frame, a sub-group of the first subset and the second subset of the k-space data is selected, and an image is reconstructed at a second image resolution. The motion between the second image resolution images is estimated in the form of motion fields. The motion information is incorporated into a final reconstruction of a dynamic series of motion-compensated magnetic resonance images of the patient at a third image resolution.

Method of reconstructing a dynamic series of motion-compensated magnetic resonance images

A computer-implemented magnetic resonance image optimisation method is disclosed. An image of an object is mapped using a static magnetic field and divided into a plurality of voxels. Each voxel is represented in a Euclidean n-dimensional space, where n≥3, and clustered by grouping together voxels having similar characteristics to create homogenous clusters. The centre or centroid of each cluster is determined, and used, or the voxel closest to either the centre or the centroid is used, as a super-voxel in an optimisation procedure. An optimised diagnostic image of the object is then generated.

Computer-implemented magnetic resonance image optimisation method

The present disclosure is directed to techniques for synchronizing a rotational eccentric mass of a gravitational transducer used for a magnetic resonance elastography acquisition with a corresponding magnetic resonance elastography scan carried out by a magnetic resonance imaging system, wherein the rotation of the eccentric mass is driven by a shaft. The method includes starting the rotation of the eccentric mass at a set vibration frequency and the magnetic resonance elastography scan at a set acquisition frequency; determining the rotational position of the shaft; defining the rotational position as first reference position; calculating further reference positions. At the start time of each subsequent acquisition period, determining the current rotational position of the shaft; comparing the determined current rotational position with the theoretically expected reference position and decreasing or increasing the rotational speed of the rotational eccentric mass based on the comparison.

Method and system for synchronizing a rotational eccentric mass with a magnetic resonance elastography scan

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.

Image reconstruction method

A method and system for performing three-dimensional, 3D, magnetic resonance elastography, MRE, using a multi-slice gradient echo, GRE, imaging sequence. Four scans typically required to be performed during MRE, and during four breath-holds, are combined into a single measurement that can be performed during a single breath-hold.

Method and system for magnetic resonance elastography

A method and apparatus for generating a Tor Tmap for a three-dimensional (3D) image volume of a subject. The method includes acquiring first, second, and third 3D images of the image volume of the subject. Signal evolutions of voxels through the first to third 3D images by comparing voxel intensity levels of corresponding voxel locations in the first, second, and third 3D images. A simulation dictionary representing the signal evolutions for a number of different tissue parameter combinations is obtained. The Tor Tmap is generated by comparing the determined signal evolutions to entries in the dictionary and by finding, for each of the determined signal evolutions, the entry in the dictionary that best matches the determined signal evolution.

Method and apparatus for generating a T1/T2 map

The present techniques relate to a techniques for performing cardiac perfusion imaging in order to detect perfusion defects in the myocardium. The present techniques relate to methods for performing cardiac perfusion imaging by performing at least two image acquisitions using different, customizable saturation delay times, which improves the ability to detect defects.

Method and system for double contrast perfusion imaging

In a method of performing magnetic resonance (MR) imaging, an MR apparatus, and a computer-readable medium during a first cardiac cycle of a subject, a first imaging sequence is generated for application to a subject. The first imaging sequence has a preparatory pulse and an inversion recovery pulse following the preparatory pulse. First signals emitted from the subject in response to the first imaging sequence are detected, and first image data are generated based on the first signals. During a second cardiac cycle following the first cardiac cycle, a second imaging sequence is generated for application to the subject. The second imaging sequence has a preparatory pulse. Second signals emitted from the subject in response to the second imaging sequence are detected, and second image data are generated based on the second signals.

Method of performing magnetic resonance imaging and a magnetic resonance apparatus

In a method for reconstructing magnetic resonance (MR) image data from k-space data, k-space data of an image region of a subject are provided to a computer that is also provided with multiple navigator signals for the image region of the subject. The computer sorts the k-space data into multiple bins, the multiple bins representing different motion states of the subject. For each of the multiple bins, the computer executes a compressed sensing procedure to reconstruct the MR image data from the k-space data in the respective bin. Execution of the compressed sensing procedure includes solving an optimization problem comprising a data consistency component and a transform sparsity component. Motion information is incorporated by the computer into at least one of the data consistency component and the transform sparsity component of the optimization problem.

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Radhouene Neji