Koninklijke Philips Corporation N.V.

The University of Chicago

Amy Perkins

Chi-Hua Tung

Pedro Jorge Da Silva Rodrigues

Patrick Olivier

Andreia Maria Araujo Trindade Rodrigues

Joerg Bredno

Jinghan Ye

Bin Zhang

Sven Prevrhal

Xiaochuan Pan

Manoj Narayanan

Zheng Zhang

In an emission imaging method, emission imaging data are acquired for a subject using an emission imaging scanner () including radiation detectors (). The emission imaging data are reconstructed to generate a reconstructed image by executing a constrained optimization program including a measure of data fidelity between the acquired emission imaging data an a reconstruct-image transformed by a data model of the imaging scanner to emission imaging data. During the reconstructing, each iteration of the constrained optimization program is constrained by an image variability constraint. The reconstructed image is displayed the reconstructed image on a display device. The emission imaging may be positron emission tomography (PET) imaging data, optionally acquired using a sparse detector array. The image variability constraint may be a constraint that an image total variation (image TV) of a latent image defined using a Gaussian blurring matrix be less than a maximum value.

Optimization-based reconstruction with an image-total-variation constraint in PET

A method includes identifying an imaging workflow process and constructing and displaying, in a graphical user interface, a graphical process tree for the imaging workflow process and a plurality of steps thereof. The method further includes identifying a standard of interest and mapping the plurality of steps into the standard of interest in the displayed graphical process tree. The method further includes receiving, via the graphical user interface, an input indicating a potential failure mode information for two or more of the steps, calculating at least one risk priority number for each step, evaluating the numeric assessment of risk based on a risk priority number threshold, and visually highlighting displayed steps corresponding to steps with risk priority numbers that exceed the risk priority number threshold. The method further includes determining a risk management plan to mitigate risk based on the highlighted steps.

Quality management of imaging workflows

A nuclear imaging apparatus () acquires nuclear imaging data comprising events wherein each event records at least spatial localization information and a timestamp for a nuclear decay event. An event-preserving image reconstruction module () reconstructs the nuclear imaging data using an event-preserving reconstruction algorithm to generate an image represented as an event-preserving reconstructed image dataset (I) comprising for each event the timestamp and at least one spatial voxel assignment. One or more structures are identified in the image and independent motion compensation is performed for each structure. In one approach, an events group is identified corresponding to the structure comprising events assigned to the structure by the event-preserving reconstructed image dataset; a time binning of the events of each events group is optimized based on a motion profile for the structure; time bin images are generated; and the structure is spatially registered in the time bin images.

List mode dynamic image reconstruction

A medical system () for normalization correction of an imaging system () includes a detector geometry correction unit (), a crystal efficiency unit (), and a normalization unit (). The detector geometry correction unit () mathematically calculates a detector geometry correction component for a type of scanner () of interest. The crystal efficiency unit () configured to empirically determine a crystal efficiency component for at least one individual scanner (). The normalization unit () generates a normalization data set () which corresponds to a normalization correction factor of the at least one individual scanner () in accordance with the detector geometry correction component and the crystal efficiency component.

Hybrid method based on simulation and experimental data to normalize pet data

A PET scanner () generates a plurality of time stamped lines of response (LORs). A motion detector () detects a motion state, such as motion phase or motion amplitude, of the subject during acquisition of each of the LORs. A sorting module () sorts the LORs by motion state and a reconstruction processor () reconstructs the LORs into high spatial, low temporal resolution images in the corresponding motion states. A motion estimator module () determines a motion transform which transforms the LORs into a common motion state. A reconstruction module () reconstructs the motion corrected LORs into a static image or dynamic images, a series of high temporal resolution, high spatial resolution images.

Growing community of inventors

Philadelphia, PA, United States of America

Amy Perkins