Koninklijke Philips Corporation N.V.

Philips Intellectual Property & Standards Gmbh

Ralph Brinks

Marc Busch

Carsten Meyer

Alexander Fischer

Jens-Christoph Georgi

Daniel Gagnon

Thomas Leroy Laurence

Jerome John Griesmer

Andreas Thon

Bernd Schweizer

Manoj Narayanan

Carsten Degenhardt

Jeffrey Allan Kolthammer

Michael Perkuhn

Frank O Thiele

Timo Paulus

Eike Gerhard Gegenmantel

Ana Belen Martin Recuero

A CT imaging system () generates structural data of a first FOV which is reconstructed by a CT reconstruction processor () into a CT image representation. A nuclear imaging system acquires functional data from a second FOV which is smaller than the first FOV. A first PET reconstruction processor () reconstructs the functional data into a PET image representation. A fusion processor () combines the PET image representation with a map extracted from the CT image representation to generate an extended FOV image representation. A spill-over correction unit () and a backscatter correction unit () derive spill-over correction data and backscatter correction data from the extended FOV image representation. A reconstruction processor () generates a spill-over and backscatter corrected functional image representation based on the spill-over correction data, the backscatter correction data, and the functional data.

Model-based extension of field-of-view in nuclear imaging

A time of flight positron emission tomography apparatus () includes a detector (), a data acquisition system (), a coincidence system () and a reconstructor (). Various elements of an imaging chain influence the temporal resolution of the system () so that positron data collected along different lines of response is characterized by different temporal resolutions. The different temporal resolutions are used to estimate the positions of detected events along their respective lines of response.

Method and system for improved TOF PET reconstruction

A method includes generating a kinetic parameter value for a VOI in a functional image of a subject based on motion corrected projection data using an iterative algorithm, including determining a motion correction for projection data corresponding to the VOI based on the VOI, motion correcting the projection data corresponding to the VOI to generate the motion corrected projection data, and estimating the at least one kinetic parameter value based on the motion corrected projection data or image data generated with the motion corrected projection data. In another embodiment, a method includes registering functional image data indicative of tracer uptake in a scanned patient with image data from a different imaging modality, identifying a VOI in the image based on the registered images, generating at least one kinetic parameter for the VOI, and generating a feature vector including the at least one generated kinetic parameter and at least one bio-marker.

Functional imaging

An apparatus includes a diagnostic scanner () and a treatment planner (). The treatment planner () plans a treatment to be applied to an object. A treatment device () treats the object according to the treatment plan. A treatment scanner () scans the object during a treatment session. A motion modeler () uses information from the treatment scan to model a motion of the object. A motion compensated quantitative data generator () uses data from the diagnostic () or other scanner, as well as feature geometry () and feature motion () information, to generate motion compensated quantitative data indicative of a feature of the object.

Motion compensation in quantitative data analysis and therapy

A radiation detection apparatus () acquires projection data of an object that is subject to motion during the acquisition. The apparatus includes a motion modeler () and a motion compensator () that cooperate to compensate for a motion of the object during the acquisition. In one example, the projection data includes list mode positron emission tomography data and the apparatus compensates for cardiac motion.

Motion correction in nuclear imaging

An apparatus includes a scanner () and a scanning motion monitor (). A motion modeler () uses data from the scanning motion monitor () and the scanner () to generate a motion model which describes motion of a region of interest of an object. A treatment planner () uses image data from the scanner () to establish a treatment plan for the object. A treatment device, which operates in conjunction with a treatment motion monitor (), uses the motion model to compensate for motion of the object during application of the treatment.

Motion estimation in treatment planning

A radiological imaging method comprises: acquiring radiological lines of response (LOR's) from a subject; grouping the acquired LOR's into time intervals such that each group of LOR's () was acquired during a selected time interval; identifying a region of interest () for each time interval based on LOR's grouped into that time interval; for each time interval, determining a positional characteristic () of the region of interest identified for that time interval based on LOR's grouped into that time interval; for each time interval, spatially adjusting LOR's grouped into that time interval based on the positional characteristic of the region of interest identified for that time interval; and reconstructing at least the spatially adjusted LOR's to generate a motion compensated reconstructed image.

Radiological imaging incorporating local motion monitoring, correction, and assessment

A method for locally correcting motion in an image reconstructed by a reconstruction system () of an imaging system () with raw data includes estimating a characteristic feature of a region of interest within the reconstructed image from the raw data, correcting the raw data associated with the region of interest for motion with the estimated region characteristic feature, and reconstructing a motion-corrected image corresponding to the region of interest with the corrected raw data.

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Hagen, Germany

Ralph Brinks