Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Christopher Rohkohl

Markus Kowarschik

Günter Lauritsch

Sebastian Schafer

Kevin Royalty

Sonja Gehrisch

Jan Boese

Kerstin Mueller

Dorin Comaniciu

Herbert Bruder

Yefeng Zheng

Gareth Funka-Lea

Bernhard Scholz

Christian Kaethner

Benno Heigl

Mingqing Chen

Jurgen Endres

Thomas Flohr

Thomas Redel

John Baumgart

Michael Scheuering

Thomas Allmendinger

Michael Manhart

Joachim Hornegger

Chris Schwemmer

Klaus Klingenbeck

Annette Birkhold

Marcus Prümmer

Wolfgang Holub

Abhishek Bafna

The disclosure relates to a method and an imaging device for generating a motion-compensated image of a target object. The disclosure further relates to a corresponding computer program and a computer-readable storage medium. In the method, a reference image is generated from projection images of a target object. Furthermore, a motion field which characterizes a motion of the target object shown is determined iteratively. In each case, after a predetermined number of iterative acts, the existing reference image is replaced by a provisional motion-compensated image, which is then used for the further iteration. The initial reference image is generated without using a synchronization or gating-signal that characterizes a motion of the target object.

Method and imaging device for generating a motion-compensated image, computer program, and storage medium

Described herein are technologies for facilitating reconstruction of flow data. In accordance with one aspect, the framework receives a four-dimensional projection image dataset and registers one or more pairs of temporally adjacent projection images in the image dataset. Two-dimensional flow maps may be determined based on the registered pairs. The framework may then sort the two-dimensional flow maps according to heart phases, and reconstruct a three-dimensional flow map based on the sorted two-dimensional flow maps.

Reconstruction of flow data

A solution for determination of a three-dimensional difference image dataset of an examination volume. Here two-dimensional real image datasets relating to the examination volume are received via an interface, each of the two-dimensional real image datasets including a two-dimensional x-ray projection of the examination volume in relation to a projection direction. Furthermore, the first difference image dataset is determined based on the two-dimensional real image datasets and based on a first trained function via a processing unit. Here the first difference image dataset is at least two-dimensional, in particular at least three-dimensional, in particular the first difference image dataset is three-dimensional or four-dimensional. The determination of the first difference image dataset based on the two-dimensional real image datasets and based on a trained function enables mask recordings of the examination volume to be dispensed with, and thus the x-ray load of the examination volume to be reduced.

Determining a difference image dataset of an examination volume

A framework for quantitative evaluation of time-varying data. In accordance with one aspect, the framework delineates a volume of interest in a four-dimensional (4D) Digital Subtraction Angiography (DSA) dataset (). The framework then extracts a centerline of the volume of interest (). In response to receiving one or more user-selected points along the centerline (), the framework determines at least one blood dynamics measure associated with the one or more user-selected points (), and generates a visualization based on the blood dynamics measure ().

Quantitative evaluation of time-varying data

Systems and methods are provided for image forecasting of image processing. A trained image forecaster may be used to generate a virtual image based on prior actual images. The virtual image may be preprocessed to generate an intermediate image. The intermediate image may then be used to process the next actual image to generate a final image.

Forecasting images for image processing

The invention relates to a tomography system (TA) comprising a first (QD) and a second (QD) beam source-detector pair for capturing one series (A, A) of projection image data sets (PB, PB) each from one projection angle (W, W) each and a volume image production system (VE) for producing a series (AV) of volume images (VB) of a vascular system (GS) while taking into account first confidence values (VW) of the first pixel values (PW) and/or while taking into account second confidence values (VW) of the second pixel values (PW). The confidence value (VW, VW) of a pixel value (PW, PW) depends on a pixel-specific traversing length (L) that a projection beam (PS, PS) traverses on a path through parts (Gi) of the vascular system (GS) from the first (Q) or the second (Q) beam source to a pixel-specific sensor element (S) of the associated first (D) or second (D) detector. The invention further relates to a corresponding method () for producing a series (AV) of volume images (VB) of a vascular system (GS).

Tomography system and method for producing a series of volume images of a vascular system

Vessel overlap artifacts are reduced in a four-dimensional angiography data set a blood vessel system of a patient with a contrast medium. A three-dimensional vessel data set of the blood vessel system is reconstructed from two-dimensional projection images of digital subtraction angiography showing the blood vessel system, determined by multiplicative back projection of the projection images into the vessel data set or a base data set of the four-dimensional angiography data set derived vessel data set. A plausibility check is performed with vessel sections displayed as filled with contrast medium in partial image data sets of the angiography data set assigned to all individual, and different instants of the covered period are checked against a plausibility check criterion checking for a contrast medium-filled connection to an admissible source point, after which a corrected partial image data set is determined containing only vessel sections satisfying the plausibility check criterion.

Method, image processing device, computer program and electronically readable data carrier for reducing vessel overlap artifacts in a four-dimensional angiography data set

A method calculates a four-dimensional DSA dataset from x-ray datasets. Each of the x-ray datasets contains a two-dimensional x-ray projection of an examination volume in relation to a direction of projection and a recording time. A first three-dimensional DSA dataset of a first reconstruction volume is determined based on the x-ray datasets. The first reconstruction volume is a part of the examination volume. A second three-dimensional DSA dataset of a second reconstruction volume is determined based on the x-ray datasets. The second reconstruction volume is a part of the first reconstruction volume. The second three-dimensional DSA dataset is segmented. The x-ray datasets are normalized based on the first three-dimensional DSA dataset. A four-dimensional DSA dataset is calculated by back projection of the normalized x-ray datasets onto the segmented second three-dimensional DSA dataset. The four-dimensional DSA dataset contains a number of third three-dimensional DSA datasets and associated time information.

Calculating a four dimensional DSA dataset with variable spatial resolution

A method is provided for image processing an angiography data set of a capture region of interest of a patient's vascular system. The method includes establishing a static time parameter set from the angiography data set, wherein the static time parameter set includes time parameters and characterizes the time profile of the contrast agent concentration for picture elements of the capture region described in the image data subsets; establishing a series of mask data sets by picture element-by-picture element application of a window function having a window width of greater than zero; selecting a subinterval in the parameter space covered by the time parameters for each instant of the series to the static time parameter set; and establishing a series of static display data sets by applying the mask data sets to a static vessel data set, which shows a vascular system perfused by the contrast agent in the capture region and which underlies or is derived from the angiography data set.

Image processing of four-dimensional angiography data set

A three-dimensional model dataset of a blood vessel system of a patient including at least one the vessel system is determined from a number of projection images, which have been recorded from different recording angles, of the blood vessel system The projection images are divided up into image areas each containing at least one pixel. A feature vector is determined for each of the image areas. Classification information, which describes how the respective image area belongs or does not belong to a vessel segment of the blood vessel system defined in accordance with anatomical specification data, is defined for each of the image areas by applying a classification function to the feature vector assigned to the image area. The classification function has been trained by training data records annotated with classification information obtained from at least one person other than the patient. The blood vessel system in the projection images is segmented by grouping image areas with the same classification information. The three-dimensional model dataset is calculated as a function of the segmented projection images and the classification information.

Determining a three-dimensional model dataset of a blood vessel system with at least one vessel segment

Systems and methods are provided for refined data reconstruction. In accordance with one aspect, the framework performs a first four-dimensional reconstruction of time-varying data to generate a four-dimensional Digital Subtraction Angiography (DSA) dataset of an object of interest. The framework extracts a volume of interest from the four-dimensional DSA dataset to generate a volume array. The volume of interest may be refined based on the volume array to generate a refined dataset. A second four-dimensional reconstruction may then be performed based on the refined dataset to generate a zoomed-in four-dimensional representation of the volume of interest.

Refined reconstruction of time-varying data

Systems and methods are provided for data reconstruction. In accordance with one aspect, data interpolation is performed on a time-varying three-dimensional (3D) image dataset of one or more vessel-like structures to generate at least one interpolated voxel value. The interpolated voxel value is used to correct at least one value of a vessel voxel representing the one or more vessel-like structures in the time-varying 3D dataset.

Reconstruction of time-varying data

A method, a computer program, a computer program product and a computed tomography system are disclosed. The image data is a spatially three-dimensional reconstruction. At least one value for an image metric of the image data is determined. A motion field for motion compensation of the image data is then determined on the basis of image data as a function of the image metric. Essentially, partial image data is determined, wherein the partial image data corresponds in each case to the spatially three-dimensional reconstruction from scan data of an angular sub-range. The motion field of the image data is determined at the control points via an optimization method as a function of the image metric, so that, thereafter, the partial image data is transformed in accordance with the motion of the motion field. New image data is then produced by merging the partial image data.

Image-based motion compensation of image data

The left ventricle epicardium is estimated in medical diagnostic imaging. C-arm x-ray data is used to detect an endocardium at different phases. The detected endocardium at the different phases is compared to sample endocardiums at different phases. The sample endocardiums have corresponding sample epicardiums. The transformation between the most similar sample endocardium or endocardiums over time and the detected endocardium over time is applied to the corresponding sample epicardium or epicardiums. The transformed sample epicardium over time is the estimated epicardium over time for the C-arm x-ray data.

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Bochum, Germany

Christopher Rohkohl