Patents by Inventor Hasan Ertan Cetingul
Hasan Ertan Cetingul 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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Patent number: 10794977Abstract: A system and method including receiving magnetic resonance (MR) imaging data from a first MR scanner device, the MR imaging data including data for a plurality of MR scans of different structural or anatomical regions; generating, based on the MR imaging data, normalized reference data including statistical information for each MR scan; learning a transformation, based on the normalized reference data, to correlate a set of input MR imaging data to the normalized reference data; and storing a record of the transformed imaging data.Type: GrantFiled: June 22, 2017Date of Patent: October 6, 2020Assignee: Siemens Healthcare GmbHInventors: Benjamin L. Odry, Hasan Ertan Cetingul, Boris Mailhe, Mariappan S. Nadar, Xiao Chen
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Patent number: 10751017Abstract: A computer-implemented method for generating an assessment of traumatic brain injury (TBI) includes a TBI assessment computer receiving structural imaging data acquired by performing a structural imaging scan on an individual and generating a structural imaging score based on the structural imaging data. The TBI assessment computer receives functional imaging data acquired by performing a functional imaging scan on the individual and generates a functional imaging score based on the functional imaging data. The TBI assessment computer also receives diffusion imaging data acquired by performing a diffusion imaging scan on the individual and generates a diffusion imaging score based on the diffusion imaging data. Based on the structural imaging score, the functional imaging score, and the diffusion imaging score, the TBI assessment computer generates a TBI assessment score.Type: GrantFiled: June 17, 2015Date of Patent: August 25, 2020Assignee: Siemens Heatlhcare GmbHInventors: Benjamin L. Odry, Hasan Ertan Cetingul
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Patent number: 10753998Abstract: Resolution is enhanced for diffusion MR imaging. The tensors modeling the underlying water diffusion in brain tissues are used to interpolate other diffusion tensors, providing higher resolution diffusion biomarker images. Each diffusion tensor is represented by a pair of elements, the one in an ‘orientation space’ and another in a ‘shape space.’ The tensors are iteratively interpolated by averaging the aforementioned elements in separate mathematical spaces. The weighted average of the shape components of the diffusion tensors is computed in closed form, which decreases the runtime.Type: GrantFiled: February 4, 2019Date of Patent: August 25, 2020Assignee: Siemens Healthcare GmbHInventor: Hasan Ertan Cetingul
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Patent number: 10521911Abstract: A method of reviewing neural scans includes receiving at least one landmark corresponding to an anatomical region. A plurality of images of tissue including the anatomical region is received and a neural network configured to differentiate between healthy tissue and unhealthy tissue within the anatomical region is generated. The neural network is generated by a machine learning process configured to receive the plurality of images of tissue and generate a plurality of weighting factors configured to differentiate between healthy tissue and unhealthy tissue. At least one patient image of tissue including the anatomical region is received and a determination is made by the neural network whether the at least one patient image of tissue includes healthy or unhealthy tissue.Type: GrantFiled: December 5, 2017Date of Patent: December 31, 2019Assignee: Siemens Healtchare GmbHInventors: Benjamin L. Odry, Hasan Ertan Cetingul, Mariappan S. Nadar, Puneet Sharma, Shaohua Kevin Zhou, Dorin Comaniciu
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Patent number: 10387765Abstract: For correction of an image from an imaging system, a deep-learnt generative model is used as a regularlizer in an inverse solution with a physics model of the degradation behavior of the imaging system. The prior model is based on the generative model, allowing for correction of an image without application specific balancing. The generative model is trained from good images, so difficulty gathering problem-specific training data may be avoided or reduced.Type: GrantFiled: May 16, 2017Date of Patent: August 20, 2019Assignee: Siemens Healthcare GmbHInventors: Boris Mailhe, Hasan Ertan Cetingul, Benjamin L. Odry, Xiao Chen, Mariappan S. Nadar
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Patent number: 10324155Abstract: A computer-implemented method for sparse recovery of fiber orientations using a multidimensional Prony method for use in tractography applications includes performing magnetic resonance imaging to acquire a plurality of sparse signal measurements using a q-space sampling scheme which enforces a lattice structure with a predetermined number of collinear samples. Next, for each voxel included in the plurality of sparse signal measurements, a computer system is used to perform a parameter estimation process. This process includes translating a portion of the sparse signal measurements corresponding to the voxel into a plurality of Sparse Approximate Prony Method (SAPM) input parameters, and applying a SAPM process to the SAPM input parameters to recover a number of fiber populations, a plurality of orientation vectors, and a plurality of amplitude scalars.Type: GrantFiled: September 22, 2016Date of Patent: June 18, 2019Assignee: Siemens Healthcare GmbHInventors: Evan Schwab, Hasan Ertan Cetingul, Boris Mailhe, Mariappan S. Nadar
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Publication number: 20190178968Abstract: Resolution is enhanced for diffusion MR imaging. The tensors modeling the underlying water diffusion in brain tissues are used to interpolate other diffusion tensors, providing higher resolution diffusion biomarker images. Each diffusion tensor is represented by a pair of elements, the one in an ‘orientation space’ and another in a ‘shape space.’ The tensors are iteratively interpolated by averaging the aforementioned elements in separate mathematical spaces. The weighted average of the shape components of the diffusion tensors is computed in closed form, which decreases the runtime.Type: ApplicationFiled: February 4, 2019Publication date: June 13, 2019Inventor: Hasan Ertan Cetingul
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Publication number: 20190172207Abstract: A method of reviewing neural scans includes receiving at least one landmark corresponding to an anatomical region. A plurality of images of tissue including the anatomical region is received and a neural network configured to differentiate between healthy tissue and unhealthy tissue within the anatomical region is generated. The neural network is generated by a machine learning process configured to receive the plurality of images of tissue and generate a plurality of weighting factors configured to differentiate between healthy tissue and unhealthy tissue. At least one patient image of tissue including the anatomical region is received and a determination is made by the neural network whether the at least one patient image of tissue includes healthy or unhealthy tissue.Type: ApplicationFiled: December 5, 2017Publication date: June 6, 2019Inventors: Benjamin L. Odry, Hasan Ertan Cetingul, Mariappan S. Nadar, Puneet Sharma, Shaohua Kevin Zhou, Dorin Comaniciu
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Patent number: 10311373Abstract: A method for subject-specific assessment of neurological disorders, the method includes receiving 3D image data representative of a subject's brain and identifying subject-specific anatomical structures in the 3D image data. A subject-specific model for electrical dynamics is created based on the 3D image data and the subject-specific anatomical structures and one or more functional indicators of neurological disorder are computed using the subject-specific model for electrical dynamics.Type: GrantFiled: April 16, 2015Date of Patent: June 4, 2019Assignee: Siemens Healthcare GmbHInventors: Saikiran Rapaka, Hasan Ertan Cetingul, Francisco Pereira, Dorin Comaniciu, Alma Gregory Sorensen
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Patent number: 10241181Abstract: Resolution is enhanced for diffusion MR imaging. The tensors modeling the underlying water diffusion in brain tissues are used to interpolate other diffusion tensors, providing higher resolution diffusion biomarker images. Each diffusion tensor is represented by a pair of elements, the one in an ‘orientation space’ and another in a ‘shape space.’ The tensors are iteratively interpolated by averaging the aforementioned elements in separate mathematical spaces. The weighted average of the shape components of the diffusion tensors is computed in closed form, which decreases the runtime.Type: GrantFiled: December 17, 2014Date of Patent: March 26, 2019Assignee: Siemens Healthcare GmbHInventor: Hasan Ertan Cetingul
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Patent number: 10088544Abstract: In white matter tractography from magnetic resonance imaging, a mathematical representation of diffusion (e.g., fiber orientation distributions) is first estimated from the diffusion MR data. Fiber tracing is performed via deterministic or probabilistic tractography where the tract maps and brain regions from multiple atlases and/or templates can be used for seeding and/or as spatial constraints. Field map correction and/or denoising may improve the diffusion weighted imaging data used in tractography.Type: GrantFiled: July 28, 2016Date of Patent: October 2, 2018Assignee: Siemens Healthcare GmbHInventors: Hasan Ertan Cetingul, Benjamin L. Odry
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Patent number: 10043088Abstract: For image quality scoring of an image from a medical scanner, a generative model of an expected good quality image may be created using deep machine-learning. The deviation of an input image from the generative model is used as an input feature vector for a discriminative model. The discriminative model may also operate on another input feature vector derived from the input image. Based on these input feature vectors, the discriminative model outputs an image quality score.Type: GrantFiled: May 26, 2017Date of Patent: August 7, 2018Assignee: Siemens Healthcare GmbHInventors: Benjamin L. Odry, Boris Mailhe, Hasan Ertan Cetingul, Xiao Chen, Mariappan S. Nadar
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Publication number: 20180081018Abstract: A computer-implemented method for sparse recovery of fiber orientations using a multidimensional Prony method for use in tractography applications includes performing magnetic resonance imaging to acquire a plurality of sparse signal measurements using a q-space sampling scheme which enforces a lattice structure with a predetermined number of collinear samples. Next, for each voxel included in the plurality of sparse signal measurements, a computer system is used to perform a parameter estimation process. This process includes translating a portion of the sparse signal measurements corresponding to the voxel into a plurality of Sparse Approximate Prony Method (SAPM) input parameters, and applying a SAPM process to the SAPM input parameters to recover a number of fiber populations, a plurality of orientation vectors, and a plurality of amplitude scalars.Type: ApplicationFiled: September 22, 2016Publication date: March 22, 2018Inventors: Evan Schwab, Hasan Ertan Cetingul, Boris Mailhe, Mariappan S. Nadar
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Patent number: 9918639Abstract: A method for acquiring cine images using a magnetic resonance imaging (MRI) system includes selecting an asymmetric radial sampling scheme providing an asymmetric view of k-space corresponding to a desired image resolution. Radial k-space data is acquired using the asymmetric radial sampling scheme, wherein slice-orientation of the radial k-space data is continuously modified while acquiring the radial k-space data. A plurality of cine images are reconstructed from the radial k-space data using a compressed-sensing method.Type: GrantFiled: November 3, 2014Date of Patent: March 20, 2018Assignee: Siemens Healthcard GmbHInventors: Hasan Ertan Cetingul, Mariappan S. Nadar, Peter Speier, Michaela Schmidt
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Publication number: 20170372193Abstract: For correction of an image from an imaging system, a deep-learnt generative model is used as a regularlizer in an inverse solution with a physics model of the degradation behavior of the imaging system. The prior model is based on the generative model, allowing for correction of an image without application specific balancing. The generative model is trained from good images, so difficulty gathering problem-specific training data may be avoided or reduced.Type: ApplicationFiled: May 16, 2017Publication date: December 28, 2017Inventors: Boris Mailhe, Hasan Ertan Cetingul, Benjamin L. Odry, Xiao Chen, Mariappan S. Nadar
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Publication number: 20170371017Abstract: A system and method including receiving magnetic resonance (MR) imaging data from a first MR scanner device, the MR imaging data including data for a plurality of MR scans of different structural or anatomical regions; generating, based on the MR imaging data, normalized reference data including statistical information for each MR scan; learning a transformation, based on the normalized reference data, to correlate a set of input MR imaging data to the normalized reference data; and storing a record of the transformed imaging data.Type: ApplicationFiled: June 22, 2017Publication date: December 28, 2017Inventors: Benjamin L. Odry, Hasan Ertan Cetingul, Boris Mailhe, Mariappan S. Nadar, Xiao Chen
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Publication number: 20170372155Abstract: For image quality scoring of an image from a medical scanner, a generative model of an expected good quality image may be created using deep machine-learning. The deviation of an input image from the generative model is used as an input feature vector for a discriminative model. The discriminative model may also operate on another input feature vector derived from the input image. Based on these input feature vectors, the discriminative model outputs an image quality score.Type: ApplicationFiled: May 26, 2017Publication date: December 28, 2017Inventors: Benjamin L. Odry, Boris Mailhe, Hasan Ertan Cetingul, Xiao Chen, Mariappan S. Nadar
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Patent number: 9684979Abstract: A method of magnetic resonance (MR) imaging of a volume undergoing repetitive motion includes obtaining source slice data indicative of a plurality of source slices during the repetitive motion, and obtaining anchor slice data indicative of an anchor slice during the repetitive motion. The anchor slice intersects the plurality of source slices. The source slice data and the anchor slice data are reconstructed. A three-dimensional image assembly procedure is implemented to generate, for each phase of the repetitive motion, volume data based on a respective subset of the reconstructed source slice data. For each phase of the repetitive motion, the respective subset of slices is selected based on a correlation of the source slice data and the anchor slice data along an intersection between each source slice and the anchor slice. The source slice data of the selected subset is corrected for misalignment with the anchor slice data.Type: GrantFiled: June 9, 2014Date of Patent: June 20, 2017Assignee: Siemens Healthcare GmbHInventors: Xiaoguang Lu, Peter Speier, Hasan Ertan Cetingul, Marie-Pierre Jolly, Michaela Schmidt, Christoph Guetter, Carmel Hayes, Arne Littmann, Hui Xue, Mariappan S. Nadar, Frank Sauer, Edgar Müller
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Publication number: 20170052241Abstract: In white matter tractography from magnetic resonance imaging, a mathematical representation of diffusion (e.g., fiber orientation distributions) is first estimated from the diffusion MR data. Fiber tracing is performed via deterministic or probabilistic tractography where the tract maps and brain regions from multiple atlases and/or templates can be used for seeding and/or as spatial constraints. Field map correction and/or denoising may improve the diffusion weighted imaging data used in tractography.Type: ApplicationFiled: July 28, 2016Publication date: February 23, 2017Inventors: Hasan Ertan Cetingul, Benjamin L. Odry
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Publication number: 20160367209Abstract: A computer-implemented method for generating an assessment of traumatic brain injury (TBI) includes a TBI assessment computer receiving structural imaging data acquired by performing a structural imaging scan on an individual and generating a structural imaging score based on the structural imaging data. The TBI assessment computer receives functional imaging data acquired by performing a functional imaging scan on the individual and generates a functional imaging score based on the functional imaging data. The TBI assessment computer also receives diffusion imaging data acquired by performing a diffusion imaging scan on the individual and generates a diffusion imaging score based on the diffusion imaging data. Based on the structural imaging score, the functional imaging score, and the diffusion imaging score, the TBI assessment computer generates a TBI assessment score.Type: ApplicationFiled: June 17, 2015Publication date: December 22, 2016Inventors: Benjamin L. Odry, Hasan Ertan Cetingul