Patents by Inventor Alexander Preuhs
Alexander Preuhs has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20230274439Abstract: Provided are systems and methods for determining a change of an abnormality in an anatomical region of a patient based on medical images of a patient. Thereby, a first medical image is acquired at a first instance of time and depicts at least one abnormality in the anatomical region, and a second medical image of the anatomical region of the patient is being acquired at a second instance of time.Type: ApplicationFiled: February 23, 2023Publication date: August 31, 2023Applicant: Siemens Healthcare GmbHInventors: Alexander Preuhs, Elisabeth Preuhs, Valentin Ziebandt
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Publication number: 20220398729Abstract: A method for evaluation of medical image data comprises: providing medical image data of a patient to be examined; determining, for at least one segment of the medical image data, a respective classification probability value with respect to at least one classification from a list of specified classifications; determining a patient-specific relevance criterion for at least one classification for at least the at least one segment of the medical image data; and determining a clinical relevance of the at least one classification for the at least one segment of the medical image data using the patient-specific relevance criterion, and at least one of based on the classification probability values or based on the at least one segment of the medical image data.Type: ApplicationFiled: June 13, 2022Publication date: December 15, 2022Applicant: Siemens Healthcare GmbHInventors: Christian HUEMMER, Sailesh CONJETI, Alexander PREUHS, Lei WANG
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Publication number: 20220392122Abstract: In a computer-implemented method of training a reconstruction neural network algorithm used to reconstruct a Magnetic Resonance Imaging (MRI) image, a prediction of training MRI image is determined based on training MRI raw data and using the reconstruction neural network algorithm. A prediction of a presence or absence of the object in the training MRI image is determined based on the prediction of the training MRI image and using an object-detection algorithm. A loss value is determined based on a first difference between the ground truth of the training MRI image and the prediction of the training MRI image, and further based on a second difference between the ground truth of the presence or absence of the object and the prediction of the presence or absence of the object. Weights of the reconstruction neural network algorithm are adjusted based on the loss value and using a training process.Type: ApplicationFiled: May 25, 2022Publication date: December 8, 2022Applicant: Siemens Healthcare GmbHInventors: Alexander PREUHS, Mario ORSINI
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Publication number: 20220309675Abstract: Techniques are described to infer 2-D segmentations of a region of interest using a neural network algorithm. Techniques are described to train the neural network algorithm. The 2-D segmentations are determined based on multiple 2-D projection images. For example, x-ray images can be used as an input.Type: ApplicationFiled: March 21, 2022Publication date: September 29, 2022Applicant: Siemens Healthcare GmbHInventors: Sailesh CONJETI, Alexander PREUHS
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Publication number: 20220189013Abstract: A method includes receiving image data of an examination object. A first temporary data record is created by applying a first correction to the image data. A further temporary data record is created by applying a further correction to the image data. The further correction at least partially corresponds to the first correction. A trained function is applied to input data that is based on the first temporary data record and the further temporary data record. A parameter of the trained function is based on an image quality metric. It is determined whether the first temporary data record has a higher image quality compared with the further temporary data record. When a result is positive, the first temporary data record is provided as the corrected medical image data. When the result is negative, the further temporary data record is provided as the image data, and part of the method is repeated.Type: ApplicationFiled: December 16, 2021Publication date: June 16, 2022Inventors: Michael Manhart, Alexander Preuhs
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Patent number: 11308664Abstract: Systems and methods are provided for reconstructing a three-dimensional result image data set from computed tomography from a plurality of two-dimensional images that create an image of an object undergoing examination from a particular imaging angle, The imaging angles of all the images lie within a restricted angular range. A three-dimensional artifact-reduced image data set is provided based on the two-dimensional images using an algorithm for reducing artifacts that are the result of a restriction of the angular range. The result image data set is reconstructed using a reconstruction algorithm that processes both the artifact-reduced image data set and the two-dimensional images as input data.Type: GrantFiled: October 16, 2019Date of Patent: April 19, 2022Assignee: Siemens Healthcare GmbHInventors: Michael Manhart, Yixing Huang, Alexander Preuhs, Günter Lauritsch
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Patent number: 10789741Abstract: A method is provided for determining corrected acquisition geometries of projection images. The method includes providing a projection image dataset that has a plurality of projection images of an object under examination acquired by an acquisition device in different acquisition geometries. The method further includes determining a provisional acquisition geometry for each of the projection images by a first optimization method by minimizing a first cost function by varying the provisional acquisition geometry, wherein the first cost function is contingent on a plurality of consistency measures determined based on the provisional acquisition geometry for a respective pair of projection images.Type: GrantFiled: February 28, 2020Date of Patent: September 29, 2020Assignee: Siemens Healthcare GmbHInventors: Christian Kaethner, Michael Manhart, Alexander Preuhs, Markus Kowarschik
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Publication number: 20200286264Abstract: A method is provided for determining corrected acquisition geometries of projection images. The method includes providing a projection image dataset that has a plurality of projection images of an object under examination acquired by an acquisition device in different acquisition geometries. The method further includes determining a provisional acquisition geometry for each of the projection images by a first optimization method by minimizing a first cost function by varying the provisional acquisition geometry, wherein the first cost function is contingent on a plurality of consistency measures determined based on the provisional acquisition geometry for a respective pair of projection images.Type: ApplicationFiled: February 28, 2020Publication date: September 10, 2020Inventors: Christian Kaethner, Michael Manhart, Alexander Preuhs, Markus Kowarschik
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Patent number: 10769763Abstract: A method and system are provided for at least symbolically reconstructing a reconstruction data set of at least one vessel segment in a vessel tree of a patient. Input data for the reconstruction comprises at least two two-dimensional angiographic projection images taken in different acquisition geometries. At least one first angiographic projection image showing the vessel segment is acquired. An evaluation measure is automatically determined for each first angiographic projection image using three-dimensional preliminary information for the vessel segment. The evaluation measure describes the suitability of the at least one angiographic projection image for reconstructing the reconstruction data set. When a quality criterion evaluating the evaluation measure is not fulfilled, at least one additional acquisition geometry is determined using the three-dimensional preliminary information and/or the evaluation measure.Type: GrantFiled: May 14, 2018Date of Patent: September 8, 2020Assignee: Siemens Healthcare GmbHInventors: Sebastian Bauer, Günter Lauritsch, Alexander Preuhs, Thomas Redel, Martin Berger
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Publication number: 20200126273Abstract: Systems and methods are provided for reconstructing a three-dimensional result image data set from computed tomography from a plurality of two-dimensional images that create an image of an object undergoing examination from a particular imaging angle, The imaging angles of all the images lie within a restricted angular range. A three-dimensional artifact-reduced image data set is provided based on the two-dimensional images using an algorithm for reducing artifacts that are the result of a restriction of the angular range. The result image data set is reconstructed using a reconstruction algorithm that processes both the artifact-reduced image data set and the two-dimensional images as input data.Type: ApplicationFiled: October 16, 2019Publication date: April 23, 2020Inventors: Michael Manhart, Yixing Huang, Alexander Preuhs, Günter Lauritsch
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Publication number: 20180330484Abstract: A method and system are provided for at least symbolically reconstructing a reconstruction data set of at least one vessel segment in a vessel tree of a patient. Input data for the reconstruction comprises at least two two-dimensional angiographic projection images taken in different acquisition geometries. At least one first angiographic projection image showing the vessel segment is acquired. An evaluation measure is automatically determined for each first angiographic projection image using three-dimensional preliminary information for the vessel segment. The evaluation measure describes the suitability of the at least one angiographic projection image for reconstructing the reconstruction data set. When a quality criterion evaluating the evaluation measure is not fulfilled, at least one additional acquisition geometry is determined using the three-dimensional preliminary information and/or the evaluation measure.Type: ApplicationFiled: May 14, 2018Publication date: November 15, 2018Inventors: Sebastian Bauer, Günter Lauritsch, Alexander Preuhs, Thomas Redel, Martin Berger