Koninklijke Philips Corporation N.V.

Stefanie Remmele

Julien Senegas

Eric Cohen-Solal

Jochen Keupp

Christian Stehning

Michael Chun-Chieh Lee

Sebastian Peter Michael Dries

Jens Von Berg

Astrid Ruth Franz

Tim Nielsen

Kay Nehrke

Mariya Ivanova Doneva

Peter Mazurkewitz

Jürgen Erwin Rahmer

Tobias Richard Schaeffter

Torbjorn Vik

Tobias Ratko Voigt

Michael Chun-chieh Lee

Peter Börnert

Matthew J Walker

Michael C Lee

Wei Liu

Ulrike Blume

A computing device () includes a recommender () that evaluates at least one of a user interaction with a displayed image of a scan of an imaging examination protocol or information about the scan in an electronically formatted radiology report, and generates a signal including a recommendation to remove the scan only in response to at least one of the user interaction or the radiology report information satisfying predetermined criteria and a output device () that visually presents the signal, thereby visually presenting the recommendation.

Imaging examination protocol update recommender

A medical imaging system () includes a workstation (), a coarse segmenter (), a fine segmenter (), and an enclosed tissue identification module (). The workstation () includes at least one input device () for receiving a selected location as a seed in a first contrasted tissue type and a display device () which displays a diagnostic image delineating a first segmented region of a first tissue type and a second segmented region of a second contrasted tissue type and identified regions which include regions fully enclosed by the first segmented region as a third tissue type. The coarse segmenter () grows a coarse segmented region of coarse voxels for each contrasted tissue type from the seed location based on a first growing algorithm and a growing fraction for each contrasted tissue type. The seed location for growing the second contrasted tissue type includes the first coarse segmented region and any fully enclosed coarse voxels, and each coarse voxel includes an aggregation of voxels and a maximum and a minimum of the voxel intensities. The fine segmenter () grows a segmented region of voxels for each contrasted tissue type from the seed location and bounded by the second coarse segmented region based on a second growing algorithm and a growing fraction for each contrasted tissue type initially set to the growing fraction for the corresponding region. The seed location for growing the second contrasted tissue type includes the first segmented region and any identified regions. The enclosed tissue identification module () identifies any regions of voxels fully enclosed by the first segmented region as being of the third tissue type. The coarse segmenter, the fine segmenter, and the enclosed tissue identification module are implemented by an electronic data processing device.

Tumor segmentation and tissue classification in 3D multi-contrast

The invention relates to a method of MR imaging of at least a portion of a body () of a patient placed in an examination volume of a MR device (), the method comprising the steps of: &#x2014;subjecting the portion of the body () to a first imaging sequence for acquiring a first signal data set (); &#x2014;subjecting the portion of the body () to a second imaging sequence for acquiring a second signal data set (), wherein the imaging parameters of the second imaging sequence differ from the imaging parameters of the first imaging sequence; &#x2014;reconstructing a MR image from the second signal data set () by means of regularization using the first signal data set () as prior information. Moreover, the invention relates to a MR device () and to a computer program for a MR device ().

Compressed sensing MR image reconstruction using constraint from prior acquisition

A method includes obtaining image data for a patient. The image data corresponds to acquisition data from an imaging acquisition from a set of planned image acquisitions in an examination plan for the patient. The method further includes analyzing the image data with a processor based on an imaging practice guideline and producing electronically formatted data indicative of the analysis. The processor generates a signal indicative of a recommendation of a change to the examination plan based on the data indicative of the analysis.

Image acquisition and/or image related parameter recommender

The invention relates to a magnetic resonance imaging method for simultaneous and dynamic determination of a longitudinal relaxation time Tand a transversal relaxation time Tof the nuclear spin system of an object, in the context of DCE or DSE MRI. In this respect, the invention makes use of a steady-state gradient echo pulse sequence comprising an EPI readout module.

Simultaneous and dynamic determination of longitudinal and transversal relaxation times of a nuclear spin system

A magnetic resonance imaging scan using a MR scanner receives via a user interface a MR imaging protocol categorizable into a MR scan type of a predefined set of MR scan types. Further, a database is queried by providing to the database scan information permitting the database to identify the MR scan type of the MR imaging protocol. Statistical information on the MR scan type which can include statistics on modifications of individual scan parameters of the MR scan type is received from a database, and the statistical information is provided to the user interface. Modifications of the MR imaging protocol can be received from the user interface, resulting in a modified MR imaging protocol, according to which the MR imaging scan can be performed.

Magnetic resonance examination system with preferred settings based on data mining

A medical imaging device () includes a magnetic resonance imaging system () and a memory () containing machine executable instructions () for execution by a processor (). Execution of the instructions causes the processor to receive () a pulse sequence protocol (). Execution of the instructions further causes the processor to determine () a pulse sequence type classification () descriptive of the pulse sequence protocol. Execution of the instructions further cause the processor to determine () a magnetic resonance contrast classification (). The choice of the magnetic resonance contrast classification depends upon the pulse sequence type classification. Execution of the instructions further causes the processor to determine () a pulse sequence protocol classification (). The pulse sequence protocol classification is determined by the pulse sequence type classification and the magnetic resonance contrast classification.

Determination of a magnetic resonance imaging pulse sequence protocol classification

A magnetic resonance system comprises a magnetic resonance scanner () including a main magnet () generating a static magnetic field biasing nuclear spins toward aligning along a direction of the static magnetic field, magnetic field gradient coils (), a radio frequency coil (), and a controller () configured to: (a) drive the radio frequency coil to selectively tip spins predominantly of short T2* out of the direction of the static magnetic field; (b) drive at least one of the magnetic field gradient coils and the radio frequency coil to dephase said spins predominantly of short T2* tipped out of the direction of the static magnetic field; and (c) drive the magnetic field gradient coils and the radio frequency coil to acquire magnetic resonance data that is predominantly T2* weighted due to preceding operations (a) and (b).

Flow insensitive magnetization preparation pulse for T2* contrast

A magnetic resonance (MR) imaging system () for generating information indicative of an MR response to an oxygen breathing challenge, the MRI () system comprising at least one controller () which is configured to define a reference region () within a region of interest (ROI) (A) of reconstructed MR image information () including a plurality of voxels, the reference region () comprising a plurality of reference voxels selected from the plurality of voxels, each of the plurality of voxels having associated &#x394;R* and &#x394;Rvalues; select a cluster of voxels comprising voxels from at least the reference voxels from a multiparametric &#x394;R* and &#x394;Rmapping of the plurality of voxels; determine AR2* and AR\ limit points based upon at least minimum &#x394;R* and &#x394;Rvalues of voxels of the cluster of voxels; and determine outlier voxels () based upon a comparison of &#x394;R* and &#x394;Rof at least one of the plurality of voxels with respective values of the &#x394;R* and &#x394;Rlimit points.

System and method for classifying tissue oxygenation properties based on cluster analysis of the multi-parametric MR response to an oxygen breathing challenge

A method includes obtaining electronically formatted information about previously performed imaging procedures, classifying the information into groups of protocols based on initially selected protocols for the previously performed imaging procedures and generating data indicative thereof, identifying deviations between the classified information and the corresponding initially selected protocols for the previously performed imaging procedures, and generating a signal indicative of the deviations. A method includes recommending at least one of a plurality of protocols for an imaging procedure based on at least one of a score, a probability, or a pre-determined rule, which is based on extracted medical concepts from patient information and extracted medical concepts from previously imaged patient information, and generating a signal indicative of the recommendation.

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Hamburg, Germany

Stefanie Remmele