Koninklijke Philips Corporation N.v.

Sunnybrook Research Institute

Thomas Erik Amthor

Peter Koken

Mariya Ivanova Doneva

Falk Uhlemann

Peter Boernert

Sascha Krueger

Peter Forthmann

Karsten Sommer

Joachim Schmidt

Steffen Weiss

Jörn Borgert

Johannes Adrianus Overweg

Daniel Wirtz

Jan Jakob Meineke

Shyam Bharat

Jochen Kruecker

Thomas Netsch

Jochen Keupp

Ehsan Dehghan Marvast

Cynthia Ming-fu Kung

Ronaldus Frederik Johannes Holthuizen

Ananth Ravi

Michael Günter Helle

Christoph G Leussler

Eberhard Sebastian Hansis

Kay Nehrke

Bernd R David

Ingmar Graesslin

Tanja Nordhoff

Miha Fuderer

Julien Senegas

Fabian Wenzel

Erkki Tapani Vahala

George Randall Duensing

Peter Bornert

Philippe Abel Menteur

Gerardus Bernardus Jozef Mulder

Michael Gunter Helle

Joanne Henriette Desiree Monique Westerink

Jouke Smink

Privender Kaur Saini

Raymond Van Ee

Edwin Heijman

Jeffrey Edward Leach

Wim Crooijmans

Yajing Zhang

Chenguang Zhao

Liqin Wang

Christopher Günther Leussler

Sanne Nauts

Mariya Ivanova Donevea

Jerry Alden

Joseph C Testa

Annerieke Huevelink-Marck

An apparatus for monitoring patients undergoing a Magnetic Resonance Image (MRI) scan provides an input, a processor, and an output. The input provides the processor with at least one sensor data of a patient undergoing an MRI scan by an MRI scanner. The input provides the processor with at least one scan parameter of the MRI scanner for the MRI scan. The input also provides the processor with at least one characteristic of the patient. The processor predicts a stress level of the patient and/or a motion state of the patient, the prediction or predictions utilize at least one sensor data of the patient, the at least one scan parameter of the MRI scanner, and the at least one characteristic of the patient. The output outputs information relating to the predicted stress level of the patient and/or the predicted motion state of the patient.

Apparatus for monitoring of a patient undergoing a magnetic resonance image scan

A non-transitory computer readable medium () stores instructions readable and executable by at least one electronic processor () to perform a method () of maintaining a machine log () for a medical imaging device (). The method includes: extracting event annotations () from video () acquired by at least one video camera () positioned to image an examination region of the medical imaging device and/or an imaging bay () containing the medical imaging device, the event annotations representing activities occurring during a medical imaging examination performed using the medical imaging device; and recording machine log entries () in the machine log of the medical imaging device wherein the machine log entries are generated by the medical imaging device. The recording includes matching the extracted event annotations with machine log entries based on timestamps () of the event annotations and timestamps () of the machine log entries and annotating the machine log entries with the matched event annotations.

Context-enhanced magnetic resonance (MR) workflow analysis and image reading

A method () of controlling a user interface (UI) provided by a remote operator workstation of a remote expert (RE) providing assistance to local operators (LO) of respective medical imaging devices () during a medical imaging examination includes: analyzing signal feeds () received by a feed aggregator () to detect events occurring in workspaces of the local operators; identifying a highest priority event from amongst the detected events using an event prioritization mapping; identifying a highest priority local operator as the local operator in whose workspace the highest priority event occurred; selecting a highest priority UI view corresponding to the highest priority event using the event-to-UI view mapping; and presenting information derived from the signal feeds from the workspace of the highest priority local operator in accordance with the highest priority UI view.

Event-controlled view selection

When acquiring detailed utilization information from imaging equipment in a cross-vendor approach, one or more sensors () are positioned within a data security zone () in which an imaging procedure is performed. Sensor data is pre-processed on an isolated processing unit () to remove any sensitive information and keep a selection of features only. The resultant feature pattern is transmitted outside of the data security zone to a processing unit () where pattern recognition is performed on feature pattern to identify the type of imaging modality, scan, etc. being performed as well as to determine whether the scan is being performed according to schedule.

Cross-vendor cross-modality imaging workflow analysis

Disclosed herein is a medical system () comprising a memory () storing machine executable instructions () and an MRF scoring module (). The MRF scoring module is configured for outputting an MRF quality score () in response to receiving MRF data () as input. The medical system further comprises a computational system () configured for controlling the medical system, wherein execution of the machine executable instructions causes the computational system to: receive () the MRF data; receive () the MRF quality score in response to inputting the MRF data into an MRF scoring module; append () the MRF quality score to the MRF data if the MRF quality score is within a predetermined range (); and provide () a signal () if the MRF quality score is outside of the predetermined range.

Magnetic resonance fingerprinting quality assurance

A non-transitory storage medium stores instructions readable and executable by a first computer () to perform an image processing method (). The method includes: encrypting image data portions to generate encrypted image data portions; transmitting the encrypted image data portions from the first computer to a second server () different from the first computer; decrypting encrypted processed image data portions received at the first computer from the second server to produce processed image data portions and generating a processed image from the processed image data portions; and controlling a display device () to display the processed image or storing the processed image in a database ().

Secure remote image analysis based on randomized data transformation

A non-transitory computer readable medium () stores instructions executable by at least one electronic processor () to perform a medical process or workflow compliance monitoring method (). The method includes: monitoring () progress of an in-progress instance of a medical process or workflow using a Business Process Model (BPM) that includes a number of defined roles in the medical process or workflow; extracting () a non-compliance vector (c) from the BPM during the monitoring of the in-progress instance, the non-compliance vector comprising vector elements storing values of non-compliance metrics for the in-progress instance; converting () the non-compliance vector to a role assignments vector (a) whose vector elements store values indicative of role assignments for remediating non-compliance of the in-progress instance; and generating () one or more alerts directed to one or more roles on the basis of the vector elements of the role assignments vector.

Role-specific process compliance alert system

Some embodiments are directed to a container builder () for building a container image for providing an individualized network service based on sensitive data () in a database (). The container builder () retrieves the sensitive data () from the database (), builds the container image (), and provides it for deployment to a cloud service provider (). The container image () comprises the sensitive data () and instructions that, when deployed as a container, cause the container to provide the individualized network service based on the sensitive data () comprised in the container image ().

Container builder for individualized network services

Disclosed herein is a magnetic resonance imaging system () controlled by a processor (). The execution of the machine executable instructions causes the processor to sort () multiple preparatory scan commands () into fixed duration preparatory scan commands () and indeterminate duration preparatory scan commands (). The execution of the machine executable instructions further causes the processor to first control () the magnetic resonance imaging system with the indeterminate duration preparatory scan commands and then () with the fixed duration preparatory scan commands. The execution of the machine executable instructions further causes the processor to calculate () a gradient pulse starting time (). The execution of the machine executable instructions further causes the processor to provide () the warning signal at a predetermined time () before the gradient pulse starting time. The execution of the machine executable instructions further causes the processor to control () the magnetic resonance imaging system with pulse sequence commands to acquire the k-space data such that the execution of the gradient coil pulse commands begins at the pulse starting time.

Magnetic resonance imaging system with acoustic warning signal

The invention provides for a magnetic resonance imaging system component. The magnetic resonance imaging system component comprises an acoustic shield () for a magnetic resonance imaging cylindrical magnet assembly (). The acoustic shield comprises a cylindrical portion () configured for being inserted into a bore () of the magnetic resonance imaging cylindrical magnet assembly and for completely covering the bore of the magnetic resonance imaging system. The cylindrical portion comprises a smooth exposed surface () configured for facing away from the magnetic resonance imaging cylindrical magnet assembly. The cylindrical portion further comprises an attachment surface (). The acoustic shield further comprises an acoustic metamaterial layer () attached to the attachment surface.

Acoustic shield for a magnetic resonance imaging magnet assembly

The invention provides for a magnetic resonance imaging system () for acquiring magnetic resonance data () from a subject () within a measurement zone (). The magnetic resonance imaging system () comprises: a processor () for controlling the magnetic resonance imaging system () and a memory () storing machine executable instructions (), pulse sequence commands () and a dictionary (). The pulse sequence commands () are configured for controlling the magnetic resonance imaging system () to acquire the magnetic resonance data () of multiple steady state free precession (SSFP) states per repetition time. The pulse sequence commands () are further configured for controlling the magnetic resonance imaging system () to acquire the magnetic resonance data () of the multiple steady state free precession (SSFP) states according to a magnetic resonance fingerprinting protocol. The dictionary () comprises a plurality of tissue parameter sets. Each tissue parameter set is assigned with signal evolution data pre-calculated for multiple SSFP states.

Multi-state magnetic resonance fingerprinting

The present disclosure relates to a medical imaging method for enabling magnetic resonance imaging of a subject () using a set of imaging parameters of imaging protocols, the method comprising: receiving information related to the subject; using a predefined machine learning model for suggesting at least one imaging protocol for the received information, wherein the imaging protocol comprises at least part of the set of imaging parameters and associated values; providing the imaging protocol.

Method for enabling magnetic resonance imaging of a subject

An interventional therapy system may include at least one catheter configured for insertion within an object of interest (OOI); and at least one controller which configured to: obtain a reference image dataset including a plurality of image slices which form a three-dimensional image of the OOI; define restricted areas (RAs) within the reference image dataset; determine location constraints for the at least one catheter in accordance with at least one of planned catheter intersection points, a peripheral boundary of the OOI and the RAs defined in the reference dataset; determine at least one of a position and an orientation of the distal end of the at least one catheter; and/or determine a planned trajectory for the at least one catheter in accordance with the determined at least one position and orientation for the at least one catheter and the location constraints.

System for real-time organ segmentation and tool navigation during tool insertion in interventional therapy and method of operation thereof

A magnetic field z-gradient coil is manufactured by inserting elements () into openings () on an outside of an insulating carrier (), wrapping an electrical conductor turn () around the outside of the insulating carrier with one side of the wrapped electrical conductor alongside elements inserted into openings on the outside of the insulating carrier, removing the elements alongside the one side of the wrapped electrical conductor from the openings, and repeating to wrap conductor turns of a z-gradient coil () around the electrically insulating carrier. A transverse magnetic field gradient coil is manufactured by laying electrical conductor () onto a mold () with a keying feature () extending along the conductor engaging a mating keying feature () of the mold that defines a winding pattern (), attaching an insulating back plate () to the resulting coil section opposite from the mold, and removing the mold.

Magnetic field gradient coils with closely packed windings and methods of manufacturing same

The invention provides for a medical imaging system comprising: a memory for storing machine executable instructions; a processor for controlling the medical instrument. Execution of the machine executable instructions causes the processor to: receive MRF magnetic resonance data acquired according to an MRF magnetic resonance imaging protocol of a region of interest; reconstruct an MRF vector for each voxel of a set of voxels descriptive of the region of interest using the MRF magnetic resonance data according to the MRF magnetic resonance imaging protocol; calculate a preprocessed MRF vector () for each of the set of voxels by applying a predetermined preprocessing routine to the MRF vector for each voxel, wherein the predetermined preprocessing routine comprises normalizing the preprocessed MRF vector for each voxel; calculate an outlier map for the set of voxels by assigning an outlier score to the preprocessed MRF vector using a machine learning algorithm.

Anomaly detection using magnetic resonance fingerprinting

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Hamburg, Germany

Thomas Erik Amthor