Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Sebastian Schafer

Kevin Royalty

Markus Kowarschik

Christopher Rohkohl

Sonja Gehrisch

Benno Heigl

Jurgen Endres

John Baumgart

Christian Kaethner

Andreas Meyer

Klaus Klingenbeck

Olivier Pauly

Maddalena Strumia

Randolph M Setser

Gunter Lauritsch

Abhishek Bafna

An inverse visualization of a time-resolved angiographic image data set of a vascular system of a patient that was recorded by a medical imager during the flow of a contrast medium through the vascular system is provided. The time-resolved angiographic image data set of the vascular system has a temporal sequence of frames of the vascular system corresponding to the contrast medium filling process. A data set from bolus arrival times for each pixel or voxel is determined. The bolus arrival time corresponds to the time in the temporal sequence at which a predetermined contrast enhancement due to the contrast medium filling first occurs. A data set of temporally inverted bolus arrival times with respect to the contrast medium filling is determined for each pixel or voxel, resulting in a temporally inverted sequence of frames with respect to the contrast medium filling. The time-resolved angiographic image data set in the temporally inverted sequence is visualized.

Visualization method and apparatus

Systems and methods for image-based guidance for facilitating navigation of tubular networks. A region of interest in three-dimensional image data may first be segmented. An endoscopic instrument may be detected in two-dimensional intraoperative image data of the region of interest. A centerline of the detected endoscopic instrument may then be determined. The endoscopic instrument and the centerline may be backprojected to generate a three-dimensional backprojected volume. A device path of the endoscopic instrument may be generated based at least in part on the three-dimensional backprojected volume and the three-dimensional image data.

Image-based guidance for device path planning based on penalty function values and distances between ROI centerline and backprojected instrument centerline

For a particularly comprehensive identification of hollow organ systems, a method is provided for producing a digital subtraction angiography of a hollow organ system of a patient. The method includes: providing mask image data recorded by an X-ray device; providing at least first fill image data recorded by the X-ray device, which has been recorded during an at least partial filling of the hollow organ system with a contrast agent; starting from a first intravenous and a second intraarterial contrast agent injection following in time; ascertaining at least first subtraction image data by subtracting the mask image data from the at least first fill image data; ascertaining final subtraction image data from the at least first subtraction image data; and segmenting the final subtraction image data and assigning the pixels or voxels of the final subtraction image data to at least two different intensity classes based on their respective intensity value.

Method for producing a digital subtraction angiography and apparatus

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 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

A framework for defective pixel correction using adversarial networks. In accordance with one aspect, the framework receives first and second training image datasets. The framework performs adversarial training of a corrector and a classifier with the first and second training image datasets respectively. The corrector may be trained to correct a first input image and the classifier may be trained to recognize whether a second input image is real or generated by the corrector. The framework applies the trained corrector to a current image to correct any defective pixels and generate a corrected image. The corrected image may then be presented.

Defective pixel correction using adversarial networks

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

A framework for defective pixel identification is described herein. In accordance with one aspect, the framework performs a machine learning technique to train a classifier using a training image dataset. The trained classifier is applied to a current image to identify one or more defective pixels, which may then be corrected.

Defective pixel identification using machine learning

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

In order to increase the accuracy of registration between a world coordinate system and an image coordinate system such that a tracked medical tool may be moved forward in the body of a patient without any additional or with limited live imaging, a method for registration of the tracked medical tool with an X-ray system is provided. The method includes receiving image data from the X-ray system at a plurality of time points, receiving tracking data from a tracking device of the tracked medical tool at the plurality of time points, and registering the world coordinate system and the image coordinate system based on the received image data and the received tracking data at the plurality of time points.

Registration for tracked medical tools and X-ray systems

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

Described herein are technologies for facilitating three-dimensional imaging based on prior image data. In accordance with one aspect, deformable registration is performed to align three-dimensional (3D) image data to a sparse set of two-dimensional (2D) projection image data of at least one structure of interest. An iterative reconstruction scheme may then be performed to minimize a difference between the aligned 3D image data and the 2D image data.

Prior image based three dimensional imaging

A framework for image-based reconstruction is provided herein. In accordance with one aspect, the framework receives three-dimensional projection image data of an aneurysm wall in a vascular structure of interest and electrocardiogram signal data, wherein the three-dimensional projection image data is correlated to phases of the electrocardiogram signal data. The framework may sort the three-dimensional projection image data into phase-binned image data according to the phases, and reconstruct three-dimensional layouts of the aneurysm wall in the vascular structure of interest using the phased-binned image data. The framework may then determine the motion of the aneurysm wall between the phases based on the reconstructed three-dimensional layouts.

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Madison, WI, United States of America

Sebastian Schafer