Patents by Inventor Bogdan Georgescu

Bogdan Georgescu 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).

  • Patent number: 10373700
    Abstract: A method and system for non-invasive assessment of coronary artery stenosis is disclosed. Patient-specific anatomical measurements of the coronary arteries are extracted from medical image data of a patient acquired during rest state. Patient-specific rest state boundary conditions of a model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements and non-invasive clinical measurements of the patient at rest. Patient-specific rest state boundary conditions of the model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements and non-invasive clinical measurements of the patient at rest. Hyperemic blood flow and pressure across at least one stenosis region of the coronary arteries are simulated using the model of coronary circulation and the patient-specific hyperemic boundary conditions.
    Type: Grant
    Filed: March 11, 2013
    Date of Patent: August 6, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Lucian Mihai Itu, Ali Kamen, Bogdan Georgescu, Xudong Zheng, Huseyin Tek, Dorin Comaniciu, Dominik Bernhardt, Fernando Vega-Higuera, Michael Scheuering
  • Patent number: 10354744
    Abstract: A method and system for non-invasive assessment of coronary artery stenosis is disclosed. Patient-specific anatomical measurements of the coronary arteries are extracted from medical image data of a patient acquired during rest state. Patient-specific rest state boundary conditions of a model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements and non-invasive clinical measurements of the patient at rest. Patient-specific rest state boundary conditions of the model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements and non-invasive clinical measurements of the patient at rest. Hyperemic blood flow and pressure across at least one stenosis region of the coronary arteries are simulated using the model of coronary circulation and the patient-specific hyperemic boundary conditions.
    Type: Grant
    Filed: November 4, 2013
    Date of Patent: July 16, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Michael Scheuering, Lucian Mihai Itu, Ali Kamen, Bogdan Georgescu, Xudong Zheng, Huseyin Tek, Dorin Comaniciu, Dominik Bernhardt, Fernando Vega-Higuera
  • Patent number: 10354758
    Abstract: A method and system for simulating patient-specific atrial electrophysiology is disclosed. A patient-specific anatomical atria model is generated from medical image data of a patient. A patient-specific atria electrophysiology model is generated based on the patient-specific anatomical atria model and electrophysiology measurements of the patient. One or more virtual electrophysiological therapies are performed by performing atrial electrophysiology simulations using the patient-specific atria electrophysiology model. Atrial electrophysiology simulation results resulting from the one or more virtual electrophysiological therapies are displayed.
    Type: Grant
    Filed: August 28, 2015
    Date of Patent: July 16, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Huanhuan Yang, Tiziano Passerini, Bogdan Georgescu, Tommaso Mansi, Dorin Comaniciu
  • Publication number: 20190205606
    Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.
    Type: Application
    Filed: July 19, 2017
    Publication date: July 4, 2019
    Inventors: Shaohua Kevin Zhou, Mingqing Chen, Hui Ding, Bogdan Georgescu, Mehmet Akif Gulsun, Tae Soo Kim, Atilla Peter Kiraly, Xiaoguang Lu, Jin-hyeong Park, Puneet Sharma, Shanhui Sun, Daguang Xu, Zhoubing Xu, Yefeng Zheng
  • Publication number: 20190200880
    Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.
    Type: Application
    Filed: March 4, 2019
    Publication date: July 4, 2019
    Inventors: Puneet Sharma, Ali Kamen, Bogdan Georgescu, Frank Sauer, Dorin Comaniciu, Yefeng Zheng, Hien Nguyen, Vivek Kumar Singh
  • Patent number: 10339695
    Abstract: An artificial intelligence agent is machine trained and used to provide physically-based rendering settings. By using deep learning and/or other machine training, settings of multiple rendering parameters may be provided for consistent imaging even in physically-based rendering.
    Type: Grant
    Filed: July 7, 2017
    Date of Patent: July 2, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Kaloian Petkov, Shun Miao, Daphne Yu, Bogdan Georgescu, Klaus Engel, Tommaso Mansi, Dorin Comaniciu
  • Patent number: 10335238
    Abstract: A method and system for patient-specific simulation of cardiac electrophysiology is disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific cardiac electrophysiology model is generated based on simulated torso potentials and body surface potential measurements of the patient. Cardiac electrophysiology of the patient is simulated over time for the patient-specific anatomical heart model using the patient-specific electrophysiology model. One or more electrophysiology maps are generated based on the cardiac electrophysiology simulated using the patient-specific cardiac electrophysiology model.
    Type: Grant
    Filed: March 27, 2015
    Date of Patent: July 2, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Ali Kamen, Tommaso Mansi, Tiziano Passerini, Bogdan Georgescu, Saikiran Rapaka, Dorin Comaniciu, Gabriel Haras
  • Publication number: 20190197199
    Abstract: A method and system for patient-specific planning of cardiac therapy, such as cardiac resynchronization therapy (CRT), based on preoperative clinical data and medical images, such as ECG data, magnetic resonance imaging (MRI) data, and ultrasound data, is disclosed. A patient-specific anatomical model of the left and right ventricles is generated from medical image data of a patient. A patient-specific computational heart model, which comprises cardiac electrophysiology, biomechanics and hemodynamics, is generated based on the patient-specific anatomical model of the left and right ventricles and clinical data. Simulations of cardiac therapies, such as CRT at one or more anatomical locations are performed using the patient-specific computational heart model. Changes in clinical cardiac parameters are then computed from the patient-specific model, constituting predictors of therapy outcome useful for therapy planning and optimization.
    Type: Application
    Filed: January 30, 2013
    Publication date: June 27, 2019
    Applicants: Siemens Aktiengesellschaft, Siemens Corporation
    Inventors: Tommaso Mansi, Bogdan Georgescu, Xudong Zheng, Ali Kamen, Dorin Comaniciu
  • Patent number: 10325686
    Abstract: A system operating in a plurality of modes to provide an integrated analysis of molecular data, imaging data, and clinical data associated with a patient includes a multi-scale model, a molecular model, and a linking component. The multi-scale model is configured to generate one or more estimated multi-scale parameters based on the clinical data and the imaging data when the system operates in a first mode, and generate a model of organ functionality based on one or more inferred multi-scale parameters when the system operates in a second mode. The molecular model is configured to generate one or more first molecular findings based on a molecular network analysis of the molecular data, wherein the molecular model is constrained by the estimated parameters when the system operates in the first mode.
    Type: Grant
    Filed: June 27, 2013
    Date of Patent: June 18, 2019
    Assignees: Siemens Healthcare GmbH, SIEMENS AG OSTERREICH
    Inventors: Tommaso Mansi, Wei Keat Lim, Vanessa King, Andreas Kremer, Bogdan Georgescu, Xudong Zheng, Ali Kamen, Andreas Keller, Cord Friedrich Staehler, Emil Wirsz, Dorin Comaniciu
  • Patent number: 10321892
    Abstract: Computerized characterization of cardiac wall motion is provided. Quantities for cardiac wall motion are determined from a four-dimensional (i.e., 3D+time) sequence of ultrasound data. A processor automatically processes the volume data to locate the cardiac wall through the sequence and calculate the quantity from the cardiac wall position or motion. Various machine learning is used for locating and tracking the cardiac wall, such as using a motion prior learned from training data for initially locating the cardiac wall and the motion prior, speckle tracking, boundary detection, and mass conservation cues for tracking with another machine learned classifier. Where the sequence extends over multiple cycles, the cycles are automatically divided for independent tracking of the cardiac wall. The cardiac wall from one cycle may be used to propagate to another cycle for initializing the tracking. Independent tracking in each cycle may reduce or avoid inaccuracies due to drift.
    Type: Grant
    Filed: September 16, 2011
    Date of Patent: June 18, 2019
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Yang Wang, Bogdan Georgescu, Helene C. Houle, Dorin Comaniciu
  • Patent number: 10311978
    Abstract: A method and system for patient-specific planning of cardiac therapy, such as cardiac resynchronization therapy (CRT), based on preoperative clinical data and medical images, such as ECG data, magnetic resonance imaging (MRI) data, and ultrasound data, is disclosed. A patient-specific anatomical model of the left and right ventricles is generated from medical image data of a patient. A patient-specific computational heart model, which comprises cardiac electrophysiology, biomechanics and hemodynamics, is generated based on the patient-specific anatomical model of the left and right ventricles and clinical data. Simulations of cardiac therapies, such as CRT at one or more anatomical locations are performed using the patient-specific computational heart model. Changes in clinical cardiac parameters are then computed from the patient-specific model, constituting predictors of therapy outcome useful for therapy planning and optimization.
    Type: Grant
    Filed: January 30, 2013
    Date of Patent: June 4, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Tommaso Mansi, Bogdan Georgescu, Xudong Zheng, Ali Kamen, Dorin Comaniciu
  • Patent number: 10299862
    Abstract: A medical system is provided for three-dimensional hemodynamic quantification. Comprehensive three-dimensional (3D) plus time (3D+t) assessment of flow patterns inside the heart are provided by a combination of lumped-parameter modeling and computational flow dynamic modeling. Using medical scanning, the lumped parameter model is personalized to a given patient. The personalized lumped-parameter model provides pressure curves (i.e., pressure as a function of time) for one or more locations. Using geometry of the patients heart segmented from the medical scanning and the pressure curves as boundary conditions, the computational flow dynamics model calculates the absolute pressure for any location (e.g., for a three-dimensional field of locations) in the patient heart at any one or more phases of the cardiac cycle. More accurate absolute pressure may be provided without invasive measurement.
    Type: Grant
    Filed: February 5, 2016
    Date of Patent: May 28, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Bogdan Georgescu, Lucian Mihai Itu, Ali Kamen, Tommaso Mansi, Viorel Mihalef, Tiziano Passerini, Rapaka Saikiran, Puneet Sharma
  • Patent number: 10296809
    Abstract: A method and system for patient-specific cardiac electrophysiology is disclosed. Particularly, a patient-specific anatomical model of a heart is generated from medical image data of a patient, a level-set representation of the patient-specific anatomical model is generated of the heart on a Cartesian grid; and a transmembrane action potential at each node of the level-set representation of the of the patient-specific anatomical model of the heart is computed on a Cartesian grid.
    Type: Grant
    Filed: February 28, 2013
    Date of Patent: May 21, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Saikiran Rapaka, Tommaso Mansi, Bogdan Georgescu, Ali Kamen, Dorin Comaniciu
  • Patent number: 10297027
    Abstract: Anatomy, such as papillary muscle, is automatically detected (34) and/or detected in real-time. For automatic detection (34) of small anatomy, machine-learnt classification with spatial (32) and temporal (e.g., Markov) (34) constraints is used. For real-time detection, sparse machine-learnt detection (34) interleaved with optical flow tracking (38) is used.
    Type: Grant
    Filed: June 8, 2015
    Date of Patent: May 21, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Mihai Scutaru, Ingmar Voigt, Tommaso Mansi, Razvan Ionasec, Helene C. Houle, Anand Vinod Tatpati, Dorin Comaniciu, Bogdan Georgescu, Noha Youssry El-Zehiry
  • Patent number: 10296707
    Abstract: A method and system for image-based patient-specific guidance of cardiac arrhythmia therapies is disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific cardiac electrophysiology model is generated based on the patient-specific anatomical heart model and electrophysiology measurements of the patient. One or more virtual electrophysiological interventions are performed using the patient-specific cardiac electrophysiology model. One or more pacing targets or ablation targets based on the one or more virtual electrophysiological interventions are displayed.
    Type: Grant
    Filed: April 10, 2015
    Date of Patent: May 21, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Tiziano Passerini, Tommaso Mansi, Ali Kamen, Bogdan Georgescu, Dorin Comaniciu
  • Publication number: 20190139216
    Abstract: For object detection, deep learning is applied with an architecture designed for low contrast objects, such as lymph nodes. The architecture uses a combination of dense deep learning or features, which employs feed-forward connections between convolutions layers, and a pyramidal arrangement of the dense deep learning using different resolutions.
    Type: Application
    Filed: November 3, 2017
    Publication date: May 9, 2019
    Inventors: Bogdan Georgescu, Eric Wengrowski, Siqi Liu, Daguang Xu, Dorin Comaniciu, Shaohua Kevin Zhou
  • Publication number: 20190125295
    Abstract: For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.
    Type: Application
    Filed: October 30, 2017
    Publication date: May 2, 2019
    Inventors: Huseyin Tek, Bogdan Georgescu, Tommaso Mansi, Frank Sauer, Dorin Comaniciu, Helene C. Houle, Ingmar Voigt
  • Patent number: 10271817
    Abstract: A regurgitant orifice of a valve is detected. The valve is detected from ultrasound data. An anatomical model of the valve is fit to the ultrasound data. This anatomical model may be used in various ways to assist in valvular assessment. The model may define anatomical locations about which data is sampled for quantification. The model may assist in detection of the regurgitant orifice using both B-mode and color Doppler flow data with visualization without the jet. Segmentation of a regurgitant jet for the orifice may be constrained by the model. Dynamic information may be determined based on the modeling of the valve over time.
    Type: Grant
    Filed: June 10, 2015
    Date of Patent: April 30, 2019
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Ingmar Voigt, Tommaso Mansi, Bogdan Georgescu, Helene C Houle, Dorin Comaniciu, Codruta-Xenia Ene, Mihai Scutaru
  • Patent number: 10258244
    Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.
    Type: Grant
    Filed: April 20, 2018
    Date of Patent: April 16, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Puneet Sharma, Ali Kamen, Bogdan Georgescu, Frank Sauer, Dorin Comaniciu, Yefeng Zheng, Hien Nguyen, Vivek Kumar Singh
  • Patent number: 10262747
    Abstract: A non-volatile memory that includes a shared source line configuration and methods of operating the same to reduce disturbs is provided. In one embodiment, the method includes coupling a first positive high voltage to a first global wordline in a first row of an array of memory cells, and coupling a second negative high voltage (VNEG) to a first bitline in a first column of the array to apply a bias to a non-volatile memory transistor in a selected memory cell to program the selected memory cell. A margin voltage having a magnitude less than VNEG is coupled to a second global wordline in a second row of the array, and an inhibit voltage coupled to a second bitline in a second column of the array.
    Type: Grant
    Filed: November 8, 2017
    Date of Patent: April 16, 2019
    Assignee: Cypress Semiconductor Corporation
    Inventors: Ryan T. Hirose, Igor G. Kouznetsov, Venkatraman Prabhakar, Kaveh Shakeri, Bogdan Georgescu