Patents by Inventor Tiziano Passerini

Tiziano Passerini 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).

  • Publication number: 20190223819
    Abstract: For non-invasive EP mapping, a sparse number of electrodes (e.g., 10 in a typical 12-lead ECG exam setting) are used to generate an EP map without requiring preoperative 3D image data (e.g. MR or CT). An imager (e.g., a depth camera) captures the surface of the patient and may be used to localize electrodes in any positioning on the patient. Two-dimensional (2D) x-rays, which are commonly available, and the surface of the patient are used to segment the heart of the patient. The EP map is then generated from the surface, heart segmentation, and measurements from the electrodes.
    Type: Application
    Filed: January 24, 2018
    Publication date: July 25, 2019
    Inventors: Tommaso Mansi, Tiziano Passerini, Puneet Sharma, Terrence Chen, Ahmet Tuysuzoglu, Shun Miao, Alexander Brost
  • 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
  • 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
  • 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
  • Publication number: 20190150869
    Abstract: A method and a corresponding system for assessing a haemodynamic parameter for a vascular region of interest of a patient based on angiographic images are provided. After acquiring multiple angiographic images, a three dimensional (3D) representation of at least a first portion of the respective region of interest is performed, and geometric features are extracted from complete or partial views. Additional geometric features are extracted from partial incomplete views. A complete set of 3D geometric features for an anatomical structure, such as a vessel tree, is then generated by combining the extracted geometric features and estimating any missing geometric features. Using the complete set of 3D geometric features, a feature-based assessment of the haemodynamic parameter, such as a fractional flow reserve, is then performed.
    Type: Application
    Filed: November 21, 2018
    Publication date: May 23, 2019
    Inventors: Tiziano Passerini, Lucian Mihai Itu, Thomas Redel, Puneet Sharma
  • 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: 20190130067
    Abstract: A computer-implemented method for executing patient management workflows includes acquiring a pre-test dataset of clinically relevant information related to a patient and using a first intelligent agent to identify a diagnostic test for the patient based on the pre-test dataset. Following performance of the diagnostic test, a second intelligent agent is used to select a processing technique to be applied to data collected from the diagnostic test to obtain a diagnostic marker. Following application of the processing technique to the data collected from the diagnostic test, a third intelligent agent is used to generate an optimal patient management plan based on the pre-test dataset, the data collected from the diagnostic test, and the diagnostic marker.
    Type: Application
    Filed: October 27, 2017
    Publication date: May 2, 2019
    Inventors: Tiziano Passerini, Puneet Sharma, Dorin Comaniciu
  • Publication number: 20190130074
    Abstract: The uncertainty, sensitivity, and/or standard deviation for a patient-specific hemodynamic quantification is determined. The contribution of different information, such as the fit of the geometry at different locations, to the uncertainty or sensitivity is determined. Alternatively or additionally, the amount of contribution of information at one location (e.g., geometric fit at the one location) to uncertainty or sensitivity at other locations is determined. Rather than relying on time consuming statistical analysis for each patient, a machine-learnt classifier is trained to determine the uncertainty, sensitivity, and/or standard deviation for the patient.
    Type: Application
    Filed: October 30, 2017
    Publication date: May 2, 2019
    Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma
  • Publication number: 20190130578
    Abstract: Systems and methods are provided for automatic segmentation of a vessel. A sequence of image slices containing a vessel is acquired. Features maps are generated for each of the image slices using a trained fully convolutional neural network. A trained bi-directional recurrent neural network generates a segmented image based on the feature maps.
    Type: Application
    Filed: October 27, 2017
    Publication date: May 2, 2019
    Inventors: Mehmet Akif Gulsun, Yefeng Zheng, Puneet Sharma, Vivek Kumar Singh, Tiziano Passerini
  • Publication number: 20190095738
    Abstract: A computer-implemented method for editing image processing results includes performing one or more image processing tasks on an input image using an iterative editing process. The iterative editing process is executed until receiving a user exit request. Each iteration of the iterative editing process comprises using a first machine learning model to generate a plurality of processed images. Each processed image corresponds to a distinct set of processing parameters. The iterative editing process further comprises presenting the plurality of processed images to a user on a display and receiving a user response comprising (i) an indication of acceptance of one or more of the processed images, (ii) an indication of rejection of all of the processed images, or (iii) the user exit request. Following the iterative editing process clinical tasks are performed using at least one of the processed images generated immediately prior to receiving the user exit request.
    Type: Application
    Filed: September 26, 2017
    Publication date: March 28, 2019
    Inventors: Puneet Sharma, Tiziano Passerini, Mehmet Akif Gulsun
  • Patent number: 10241968
    Abstract: A method and system for simulating patient-specific cardiac electrophysiology including the effect of the electrical conduction system of the heart is disclosed. A patient-specific anatomical heart model is generated from cardiac image data of a patient. The electrical conduction system of the heart of the patient is modeled by determining electrical diffusivity values of cardiac tissue based on a distance of the cardiac tissue from the endocardium. A distance field from the endocardium surface is calculated with sub-grid accuracy using a nested-level set approach. Cardiac electrophysiology for the patient is simulated using a cardiac electrophysiology model with the electrical diffusivity values determined to model the Purkinje network of the patient.
    Type: Grant
    Filed: February 17, 2015
    Date of Patent: March 26, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Tiziano Passerini, Tommaso Mansi, Ali Kamen, Bogdan Georgescu, Saikiran Rapaka, Dorin Comaniciu
  • Publication number: 20190051419
    Abstract: Methods and systems for estimating patient-specific cardiac electrical properties from medical image data and non-invasive electrocardiography measurements of a patient are disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. Patient-specific cardiac electrical properties are estimated by simulating cardiac electrophysiology over time in the patient-specific anatomical heart model using a computational cardiac electrophysiology model and adjusting cardiac electrical parameters based on the simulation results and the non-invasive electrocardiography measurements. A patient-specific cardiac electrophysiology model with the patient-specific cardiac electrical parameters can then be used to perform virtual cardiac electrophysiology interventions for planning and guidance of cardiac electrophysiology interventions.
    Type: Application
    Filed: October 17, 2018
    Publication date: February 14, 2019
    Inventors: Philipp Seegerer, Tommaso Mansi, Marie-Pierre Jolly, Bogdan Georgescu, Ali Kamen, Dorin Comaniciu, Roch Mollero, Tiziano Passerini
  • Publication number: 20190038249
    Abstract: 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: Application
    Filed: September 28, 2018
    Publication date: February 7, 2019
    Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Chris Schwemmer, Max Schoebinger, Thomas Redel, Dorin Comaniciu
  • Publication number: 20190029519
    Abstract: A method for providing a personalized evaluation of CAD for a patient includes acquiring one or more non-invasive images depicting a patient's coronary arteries and extracting a first set of features of interest from the one or more non-invasive images. A machine learning model is applied to the first set of features of interest to yield a prediction of one or more coronary measures of interest. One or more invasive images depicting the patient's coronary arteries are acquired and a second set of features of interest are extracted from the one or more invasive images. The first set of features of interest and the second set of features of interest are combined to yield a combined set of features of interest. Then, the machine learning model may be applied to the combined set of features of interest to yield an enhanced prediction of the coronary measures of interest.
    Type: Application
    Filed: April 28, 2017
    Publication date: January 31, 2019
    Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma
  • Patent number: 10192640
    Abstract: A computed tomography (CT)-based clinical decision support system provides fractional flow reserve (FFR) decision support. The available data, such as the coronary CT data, is used to determine whether to dedicate resources to CT-FFR for a specific patient. A machine-learnt predictor or other model, with access to determinative patient information, is used to assist in a clinical decision regarding CT-FFR. This determination may be made prior to review by a radiologist and/or treating physician to assist decision making.
    Type: Grant
    Filed: August 31, 2017
    Date of Patent: January 29, 2019
    Assignee: Siemens Healthcare GmbH
    Inventors: Lucian Mihai Itu, Dorin Comaniciu, Thomas Flohr, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma
  • Publication number: 20190019286
    Abstract: A method and system for non-invasive medical image based assessment of coronary artery disease (CAD) for clinical decision support using on-site and off-site processing is disclosed. Medical image data of a patient is received. A processing strategy for assessing CAD of the patient using one of on-site processing, off-site processing, or joint on-site and off-site processing is automatically selected based on clinical requirements for a current clinical scenario. Non-invasive assessment of CAD of the patient is performed based on the medical image data of the patient using one of on-site processing, off-site-processing, or joint on-site and off-site processing according to the selected processing strategy. A final assessment of CAD of the patient is output based on the non-invasive assessment of CAD.
    Type: Application
    Filed: July 12, 2017
    Publication date: January 17, 2019
    Inventors: Tiziano Passerini, Lucian Mihai Itu, Dorin Comaniciu, Puneet Sharma
  • Patent number: 10141077
    Abstract: Methods and systems for estimating patient-specific cardiac electrical properties from medical image data and non-invasive electrocardiography measurements of a patient are disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. Patient-specific cardiac electrical properties are estimated by simulating cardiac electrophysiology over time in the patient-specific anatomical heart model using a computational cardiac electrophysiology model and adjusting cardiac electrical parameters based on the simulation results and the non-invasive electrocardiography measurements. A patient-specific cardiac electrophysiology model with the patient-specific cardiac electrical parameters can then be used to perform virtual cardiac electrophysiology interventions for planning and guidance of cardiac electrophysiology interventions.
    Type: Grant
    Filed: April 2, 2015
    Date of Patent: November 27, 2018
    Assignee: Siemens Healthcare GmbH
    Inventors: Philipp Seegerer, Tommaso Mansi, Marie-Pierre Jolly, Bogdan Georgescu, Ali Kamen, Dorin Comaniciu, Roch Mollero, Tiziano Passerini
  • Publication number: 20180336319
    Abstract: A computer-implemented method for providing a personalized evaluation of assessment of atherosclerotic plaques for a patient acquiring patient data comprising non-invasive patient data, medical images of the patient, and blood biomarkers. Features of interest are extracted from the patient data and one or more machine learning models are applied to the features of interest to predict one or more measures of interest related to atherosclerotic plaque.
    Type: Application
    Filed: May 19, 2017
    Publication date: November 22, 2018
    Inventors: Lucian Mihai Itu, Tiziano Passerini, Saikiran Rapaka, Puneet Sharma, Dorin Comaniciu
  • Publication number: 20180315182
    Abstract: Machine learning is used to assess data for a patient in an emergency, providing rapid diagnosis based on a large amount of information. Assistance in triage may be provided. Given the large variety of patients and conditions that may occur, the machine learning may rely on synthetically generated images for more accurate prediction. The machine learning may accurately predict even with missing information and may be used to determine what missing information for a given patient is more or less important to obtain.
    Type: Application
    Filed: April 28, 2017
    Publication date: November 1, 2018
    Inventors: Saikiran Rapaka, Lucian Mihai Itu, Tiziano Passerini, Puneet Sharma, Dorin Comaniciu
  • Publication number: 20180310888
    Abstract: A computer-implemented method for personalized assessment of patients with acute coronary syndrome (ACS) includes extracting (i) patient-specific coronary geometry data from one or more medical images of a patient; (ii) a plurality of features of a patient-specific coronary arterial tree based on the patient-specific coronary geometry data; and (iii) a plurality of ACS-related features from additional patient measurement data. A surrogate model is used to predict patient-specific hemodynamic measures of interest related to ACS based on the plurality of features of the patient-specific coronary arterial tree and the plurality of ACS-related features from the additional patient measurement data.
    Type: Application
    Filed: November 30, 2016
    Publication date: November 1, 2018
    Inventors: Lucian Mihai Itu, Tiziano Passerini, Puneet Sharma