Siemens Healthcare Gmbh

King's College London

Siemens Healthineers Ag

Rene Botnar

Radhouene Neji

Claudia Prieto

Aurelien Bustin

Christoph Forman

Karl-Philipp Kunze

Sebastien Roujol

Teresa Correia

Giulia Ginami

Giorgia Milotta

Donovan Tripp

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

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

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.

Method and apparatus for reconstructing magnetic resonance image data

A method and system for imaging a body using a magnetic resonance imaging (MRI) apparatus, including motion tracking of a target object of the body using MRI by generating an MRI image of a region of interest of the body by performing a weighted combination of a signal received by each coil of an MRI apparatus during an MRI scan.

Method and system for magnetic resonance imaging

A method of reconstructing magnetic resonance (MR) image data from k-space data. The method includes obtaining k-space data of an image region of a subject; and reconstructing, using a sparse image coding procedure, the MR image data from the k-space data by performing an iterative optimization method. The optimization method includes a data consistency iteration step and a denoising iteration step applied to MR image data generated by the data consistency iteration step. The denoising iteration step incorporates a sparsifying operation to provide a sparse representation of the MR image data for the imaged region as an input to the data consistency iteration step.

Method of reconstructing magnetic resonance image data

A plurality of sets of k-space data each of the same image region of a subject but having different contrasts are obtained. A sparse image coding procedure is performed to reconstruct a plurality of MR images each corresponding to one of the sets of k-space data. This involves solving an optimization problem comprising a data consistency iteration step used to generate the reconstructed MR images; and a denoising iteration step applied to the reconstructed MR images generated during the data consistency iteration step. The denoising iteration step includes performing a 2D/3D block matching operation to identify similar patches across the reconstructed MR images, and using the similar patches across the reconstructed MR images in a sparsifying operation to provide sparse representations of the reconstructed MR images. The sparse representations are used as an input to the data consistency iteration step.

In a method of performing magnetic resonance imaging and a magnetic resonance apparatus, first MR data are acquired of a region of interest of a subject in the absence of a B1 field. Second MR data are acquired of the region of interest in the presence of a B1 field, and within a short time interval after generation of the B1 field. The first and second MR data are processed to determine a B1 field map, and a T1 map is generated using the B1 field map. The T1 map is a B1 corrected T1 map. The first and second MR datamay be acquired as part of a T1 mapping sequence, such as a MOLLI or SASHA type cardiac T1 mapping sequence.

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London, United Kingdom

Rene Botnar