# US 11,308,660 B2 · Motion compensated cardiac valve reconstruction > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,660 B2 - Document ID: US11308660B2 - Status: Granted - Kind: B2 - Application number: 16/633,843 - Filed: 2018-07-23 - Published: 2021-05-20 - Granted: 2022-04-19 - Earliest claimed: 2017-07-27 - Assignees: Koninklijke Philips N.V. - Applicants: Koninklijke Philips N.V. - Inventors: Tanja Elss; Michael Grass; Rolf Dieter Bippus; Axel Thran - Web page: https://patentgrep.com/patent/US11308660B2 ## CPC - G06T 12/10: Image preprocessing, e.g. calibration, positioning of sources or scatter correction - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/503: {for diagnosis of the heart} - A61B 6/5264: {due to motion} - A61B 6/5288: {involving retrospective matching to a physiological signal} - A61B 6/486: {involving generating temporal series of image data} - A61B 6/5205: {involving processing of raw data to produce diagnostic data} - G06T 2210/41: Medical - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract Motion compensated reconstruction is currently not well-suited for reconstructing the valve, the valve leaflets and the neighboring vascular anatomy of the heart. Blurring of the valve and the valve leaflets occurs. This may lead to wrong diagnosis. A new approach for motion compensated reconstruction of the valve and the related anatomy is presented in which an edge-enhancing step is performed to suppress blurring. ## Claims ### Claim 1 A method for reconstructing an image of an object of interest from a plurality of projection data of said object corresponding to a plurality of time points, the method comprising: retrieving a plurality of volumetric data of the object of interest from the plurality of projection data, each one of the volumetric data corresponding to a respective time point; applying a gradient-based filter on the plurality of volumetric data to obtain a plurality of edge-enhanced image volumes corresponding to the respective time points; weighting of the plurality of edge-enhanced image volumes by weighting edges with a normalized gradient magnitude to obtain a plurality of normalized edge-enhanced image volumes; estimating a plurality of first motion vector fields on the basis of the normalized plurality of edge-enhanced image volumes; and reconstructing a plurality of first motion compensated image volumes of the object from the plurality of projection data using the estimated plurality of first motion vector fields, each of the plurality of first motion compensated image volumes corresponding to a respective time point. ### Claim 2 (depends on 1) The method according to claim 1, wherein the object of interest comprises the valves and the valve leaflets, and wherein the data corresponds to cardiac computed tomography (CT) projection data obtained together with one or more of simultaneously measured electrocardiogram (ECG) data and/or photoplethysmographic (PPG) data. ### Claim 3 (depends on 2) The method according to claim 2, wherein the time points are determined based on the ECG or PPG data, whereby the time points correspond to a cardiac phase of the heart. ### Claim 4 (depends on 1) The method according to claim 1, further comprising: obtaining a noise-reduced image volume from the volumetric data by applying a smoothing filter; determining a gradient and/or gradient magnitude for each of the data points of the noise-reduced image volume; determining a plurality of local maxima of the gradient magnitude for each of the data points and suppressing the data points that do not correspond to said local maxima; determining a first threshold value for the gradient magnitude and a second threshold value for the gradient magnitude, the first threshold value being smaller than the second threshold value; determining, for each data point, whether the gradient magnitude is below or above the second threshold value; determining, for each data point, whether the gradient magnitude is below or above the first threshold value; marking a set of data points for which the gradient magnitude is above the first threshold value and which are connected to data points for which the gradient magnitude is above the second threshold value; and obtaining, from the marked set of data points, the edge enhanced image volume. ### Claim 5 (depends on 4) The method according to claim 4, further comprising applying a Gaussian filter on the volumetric data to obtain the smoothed image volume. ### Claim 6 (depends on 4) The method according to claim 4, wherein determining the gradient for each of the data points is performed using central differences, and wherein determining the gradient magnitude for each of the data points of the smoothed image volume is performed using the Euclidean norm. ### Claim 7 (depends on 4) The method according to claim 4, wherein determining the gradient and/or the gradient magnitude for each of the data points further includes determining a direction of the gradient. ### Claim 8 (depends on 4) The method according to claim 4, wherein weighting the edge-enhanced image volume further comprises: determining a normalized gradient magnitude; and weighting the marked data points using the normalized gradient magnitude. ### Claim 9 (depends on 1) The method according to claim 1, wherein estimating the plurality of motion vector fields comprises: determining a first one of the plurality of edge-enhanced image volumes as a first reference image volume; and estimating the plurality of first motion vector fields from the first reference image volume to the remaining ones of the plurality of edge-enhanced image volumes using the first reference image volume. ### Claim 10 (depends on 1) The method according to claim 1, further comprising: obtaining a plurality of line filtered image volumes from the plurality of first motion compensated image volumes; estimating a plurality of second motion vector fields based on the plurality of line filtered image volumes; and reconstructing a plurality of second motion compensated image volumes of the object from the projection data using the estimated plurality of second motion vector fields. ### Claim 11 (depends on 1) The method according to claim 1, wherein obtaining the plurality of line filtered image volumes comprises: determining a registration transformation for registering a first one of the first motion compensated image volumes to each of the remaining ones of the plurality of first motion compensated image volumes; and obtaining the plurality of line filtered image volumes from the plurality of registered first motion compensated image volumes. ### Claim 12 (depends on 1) The method according to claim 1, further comprising selecting a region of interest within the volumetric data. ### Claim 13 An image processing device for reconstructing an image of an object of interest from a plurality of projection data of said object corresponding to a plurality of time points, comprising: a memory configured to store a plurality of volumetric data of the object of interest retrieved from the plurality of projection data, each one of the volumetric data corresponding to a respective time point; at least one processor configured to: apply a gradient-based filter on the plurality of volumetric data to obtain a plurality of edge-enhanced image volumes corresponding to the respective time points; estimate a plurality of first motion vector fields based on a plurality of normalized edge-enhanced image volumes obtained by weighting the plurality of edge-enhanced image volumes by weighting edges with a normalized gradient magnitude; and reconstruct a plurality of first motion compensated image volumes of the object from the plurality of projection data using the estimated plurality of first motion vector fields, each of the plurality of first motion compensated image volumes corresponding to a respective time point. ### Claim 14 A non-transitory computer-readable medium having one or more executable instructions stored thereon which, when executed by at least one processor, cause the at least one processor to perform a method for reconstructing an image of an object of interest from a plurality of projection data of said object corresponding to a plurality of time points, the method comprising: retrieving a plurality of volumetric data of the object of interest from the plurality of projection data, each one of the volumetric data corresponding to a respective time point; applying a gradient-based filter on the plurality of volumetric data to obtain a plurality of edge-enhanced image volumes corresponding to the respective time points; weighting the plurality of edge-enhanced image volumes by weighting edges with a normalized gradient magnitude to obtain a plurality of normalized edge-enhanced image volumes; estimating a plurality of first motion vector fields on the basis of the normalized plurality of edge-enhanced image volumes; and reconstructing a plurality of first motion compensated image volumes of the object from the plurality of projection data using the estimated plurality of first motion vector fields, each of the plurality of first motion compensated image volumes corresponding to a respective time point.