Patents by Inventor Chris Schwemmer
Chris Schwemmer 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: 20230097267Abstract: A computer-implemented method for evaluating an image data set of an imaged region comprises: determining, from the image data set, at least two processed data sets having different image data content; applying a first sub-algorithm, of an evaluation algorithm, to a first of at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets; applying a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets; determining quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, wherein the third sub-algorithm uses both the first intermediate result and the second intermediate result as input data.Type: ApplicationFiled: September 20, 2022Publication date: March 30, 2023Applicant: Siemens Healthcare GmbHInventors: Chris SCHWEMMER, Thomas ALLMENDINGER, Rainer GRIMMER
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Publication number: 20220351832Abstract: At least one example embodiment provides an improved handling of image data sets of patients.Type: ApplicationFiled: April 27, 2022Publication date: November 3, 2022Applicant: Siemens Healthcare GmbHInventors: Piotr BIALECKI, Max SCHOEBINGER, Susanne SCHMOLKE, Chris SCHWEMMER
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Publication number: 20220351833Abstract: At least one example embodiment provides a computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined.Type: ApplicationFiled: April 27, 2022Publication date: November 3, 2022Applicant: Siemens Healthcare GmbHInventors: Max SCHOEBINGER, Michael WELS, Chris SCHWEMMER, Mehmet Akif GULSUN, Serkan CIMEN, Felix LADES, Christian HOPFGARTNER, Suha AYMAN, Rumman KHAN, Ashish JAISWAL
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Publication number: 20210219935Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.Type: ApplicationFiled: March 9, 2021Publication date: July 22, 2021Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 11051780Abstract: An embodiment of a method includes providing a first result list indicating a plurality of first anatomic structures and indicating, for each respective first anatomic structure of the plurality of first anatomic structures, a corresponding first severity indicator; providing a second result list indicating, for each respective second anatomic structure of the plurality of the second anatomic structures, a corresponding second severity indicator; providing a relationship matrix indicating a level of interrelatedness between the first anatomic structures and the second anatomic structures; and generating, based on the first result list provided, on the second result list and on the relationship matrix provided, a concordance visualization indicating a respective level of concordance between at least one of the first anatomic structures and the corresponding first severity indicator, and indicating a respective level of concordance between at least one of the second anatomic structures and the corresponding secType: GrantFiled: December 13, 2018Date of Patent: July 6, 2021Assignee: SIEMENS HEALTHCARE GMBHInventors: Puneet Sharma, Ulrich Hartung, Chris Schwemmer, Ruth J. Soenius, Dominik Neumann
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Patent number: 10993687Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.Type: GrantFiled: September 28, 2018Date of Patent: May 4, 2021Assignee: Siemens Healthcare GmbHInventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 10937227Abstract: A method, for two-dimensional mapping of anatomical structures of a patient, includes acquiring three-dimensional image data of anatomical structures of a patient; adapting a virtual network structure to a spatial course of the anatomical structures; defining a user-defined map projection for projection of two-dimensional pixel positions of an image to be output onto a geometric figure around a center of the anatomical structures for which mapping onto a two-dimensional space is defined; ascertaining points of intersection of radially extending half lines assigned to the two-dimensional pixel positions of the image to be output with the virtual network structure; and ascertaining the image to be output based upon image intensity values assigned to the points of intersection ascertained. A method for two-dimensional mapping of the tree-like elongated structure of the patient; a method for simultaneous mapping of a tree-like elongated structure; and corresponding apparatuses are also described.Type: GrantFiled: August 17, 2018Date of Patent: March 2, 2021Assignee: SIEMENS HEALTHCARE GMBHInventors: Xinyun Li, Rainer Kaergel, Michael Suehling, Chris Schwemmer, Max Schoebinger
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Patent number: 10733787Abstract: A method for interactively generating a geometric model of a volume object on the basis of three-dimensional image data of an examination region of interest of an examination subject is described. According to an embodiment, a representation of the volume object is determined on the basis of three-dimensional image data and a two-dimensional representation is determined on the basis of the determined representation using a preferably non-linear planar reformation of the three-dimensional object. Subsequently, boundary indicators which define the surface profile of the volume object are edited in the two-dimensional representation. Following the editing, a three-dimensional representation of the edited boundary indicators is generated by back-transforming the edited boundary indicators into three-dimensional space. Finally, a model-based representation of the volume object is generated in three-dimensional space on the basis of the edited boundary indicators. A volume object modeling device is also described.Type: GrantFiled: March 2, 2017Date of Patent: August 4, 2020Assignee: SIEMENS HEALTHCARE GMBHInventors: Christian Hopfgartner, Felix Lades, Chris Schwemmer, Michael Suehling, Michael Wels
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Publication number: 20200029926Abstract: An embodiment of a method includes providing a first result list indicating a plurality of first anatomic structures and indicating, for each respective first anatomic structure of the plurality of first anatomic structures, a corresponding first severity indicator; providing a second result list indicating, for each respective second anatomic structure of the plurality of the second anatomic structures, a corresponding second severity indicator; providing a relationship matrix indicating a level of interrelatedness between the first anatomic structures and the second anatomic structures; and generating, based on the first result list provided, on the second result list and on the relationship matrix provided, a concordance visualization indicating a respective level of concordance between at least one of the first anatomic structures and the corresponding first severity indicator, and indicating a respective level of concordance between at least one of the second anatomic structures and the corresponding secType: ApplicationFiled: December 13, 2018Publication date: January 30, 2020Applicant: Siemens Healthcare GmbHInventors: Puneet Sharma, Ulrich Hartung, Chris Schwemmer, Ruth J. Soenius, Dominik Neumann
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Patent number: 10463336Abstract: A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.Type: GrantFiled: November 16, 2015Date of Patent: November 5, 2019Assignee: Siemens Healthcare GmbHInventors: Lucian Mihai Itu, Puneet Sharma, Saikiran Rapaka, Tiziano Passerini, Max Schöbinger, Chris Schwemmer, Dorin Comaniciu, Thomas Redel
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Publication number: 20190057541Abstract: A method, for two-dimensional mapping of anatomical structures of a patient, includes acquiring three-dimensional image data of anatomical structures of a patient; adapting a virtual network structure to a spatial course of the anatomical structures; defining a user-defined map projection for projection of two-dimensional pixel positions of an image to be output onto a geometric figure around a center of the anatomical structures for which mapping onto a two-dimensional space is defined; ascertaining points of intersection of radially extending half lines assigned to the two-dimensional pixel positions of the image to be output with the virtual network structure; and ascertaining the image to be output based upon image intensity values assigned to the points of intersection ascertained. A method for two-dimensional mapping of the tree-like elongated structure of the patient; a method for simultaneous mapping of a tree-like elongated structure; and corresponding apparatuses are also described.Type: ApplicationFiled: August 17, 2018Publication date: February 21, 2019Applicant: Siemens Healthcare GmbHInventors: Xinyun Li, Rainer Kaergel, Michael Suehling, Chris Schwemmer, Max Schoebinger
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Publication number: 20190038249Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.Type: ApplicationFiled: September 28, 2018Publication date: February 7, 2019Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 10111636Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.Type: GrantFiled: February 6, 2018Date of Patent: October 30, 2018Assignee: Siemens Healthcare GmbHInventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 10079071Abstract: A method and apparatus for whole body bone removal and vasculature visualization in medical image data, such as computed tomography angiography (CTA) scans, is disclosed. Bone structures are segmented in the a 3D medical image, resulting in a bone mask of the 3D medical image. Vessel structures are segmented in the 3D medical image, resulting in a vessel mask of the 3D medical image. The bone mask and the vessel mask are refined by fusing information from the bone mask and the vessel mask. Bone voxels are removed from the 3D medical image using the refined bone mask, in order to generate a visualization of the vessel structures in the 3D medical image.Type: GrantFiled: June 28, 2018Date of Patent: September 18, 2018Assignee: Siemens Healthcare GmbHInventors: Nathan Lay, David Liu, Shaohua Kevin Zhou, Bernhard Geiger, Li Zhang, Vincent Ordy, Daguang Xu, Chris Schwemmer, Philipp Wolber, Noha Youssry El-Zehiry
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Patent number: 10037603Abstract: A method and apparatus for whole body bone removal and vasculature visualization in medical image data, such as computed tomography angiography (CTA) scans, is disclosed. Bone structures are segmented in the a 3D medical image, resulting in a bone mask of the 3D medical image. Vessel structures are segmented in the 3D medical image, resulting in a vessel mask of the 3D medical image. The bone mask and the vessel mask are refined by fusing information from the bone mask and the vessel mask. Bone voxels are removed from the 3D medical image using the refined bone mask, in order to generate a visualization of the vessel structures in the 3D medical image.Type: GrantFiled: May 4, 2015Date of Patent: July 31, 2018Assignee: Siemens Healthcare GmbHInventors: Nathan Lay, David Liu, Shaohua Kevin Zhou, Bernhard Geiger, Li Zhang, Vincent Ordy, Daguang Xu, Chris Schwemmer, Philipp Wolber, Noha Youssry El-Zehiry
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Publication number: 20180153495Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.Type: ApplicationFiled: February 6, 2018Publication date: June 7, 2018Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Patent number: 9931790Abstract: A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.Type: GrantFiled: April 16, 2015Date of Patent: April 3, 2018Assignee: Siemens Healthcare GmbHInventors: Sasa Grbic, Razvan Ionasec, Tommaso Mansi, Ingmar Voigt, Dominik Neumann, Julian Krebs, Chris Schwemmer, Max Schoebinger, Helene C. Houle, Dorin Comaniciu, Joel Mancina
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Patent number: 9918690Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.Type: GrantFiled: October 7, 2015Date of Patent: March 20, 2018Assignee: Siemens Healthcare GmbHInventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
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Publication number: 20170270705Abstract: A method for interactively generating a geometric model of a volume object on the basis of three-dimensional image data of an examination region of interest of an examination subject is described. According to an embodiment, a representation of the volume object is determined on the basis of three-dimensional image data and a two-dimensional representation is determined on the basis of the determined representation using a preferably non-linear planar reformation of the three-dimensional object. Subsequently, boundary indicators which define the surface profile of the volume object are edited in the two-dimensional representation. Following the editing, a three-dimensional representation of the edited boundary indicators is generated by back-transforming the edited boundary indicators into three-dimensional space. Finally, a model-based representation of the volume object is generated in three-dimensional space on the basis of the edited boundary indicators. A volume object modeling device is also described.Type: ApplicationFiled: March 2, 2017Publication date: September 21, 2017Applicant: Siemens Healthcare GmbHInventors: Christian HOPFGARTNER, Felix LADES, Chris SCHWEMMER, Michael SUEHLING, Michael WELS
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Publication number: 20170245821Abstract: A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.Type: ApplicationFiled: November 16, 2015Publication date: August 31, 2017Applicant: Siemens Healthcare GmbHInventors: Lucian Mihai ITU, Puneet SHARMA, Saikiran RAPAKA, Tiziano PASSERINI, Max SCHÖBINGER, Chris SCHWEMMER, Dorin COMANICIU, Thomas REDEL