Patents by Inventor Michal Cachovan

Michal Cachovan 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: 11361478
    Abstract: For partial volume correction, the partial volume effect is simulated using patient-specific segmentation. An organ or other object of the patient is segmented using anatomical imaging. For simulation, the locations of the patient-specific object or objects are sub-divided, creating artificial boundaries in the object. A test activity is assigned to each sub-division and forward projected. The difference of the forward projected activity to the test activity provides a location-by-location partial volume correction map. This correction map is used in reconstruction from the measured emissions, resulting in more accurate activity estimation with less partial volume effect.
    Type: Grant
    Filed: May 14, 2020
    Date of Patent: June 14, 2022
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Patent number: 11250545
    Abstract: For denoising in SPECT, such as qSPECT, machine learning is used to relate settings to noise structure. Given the SPECT imaging arrangement for a patient, the machine-learned model estimates the structure of the noise. This noise structure may be used to denoise the reconstructed representation.
    Type: Grant
    Filed: September 9, 2019
    Date of Patent: February 15, 2022
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Publication number: 20220015726
    Abstract: A detector used for tomography imaging is mobile, allowing the detector to move about an object (e.g., patient to be imaged). A swarm of such detectors, such as a swarm of drones with detectors, may be used for tomography imaging. The trajectory or trajectories of the mobile detectors may account for the pose and/or movement of the object being imaged. The trajectory or trajectories may be based, in part, on the sampling for desired tomography. An image of an internal region of the object is reconstructed from detected signals of the mobile detectors using tomography.
    Type: Application
    Filed: September 30, 2021
    Publication date: January 20, 2022
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Patent number: 11160520
    Abstract: A detector used for tomography imaging is mobile, allowing the detector to move about an object (e.g., patient to be imaged). A swarm of such detectors, such as a swarm of drones with detectors, may be used for tomography imaging. The trajectory or trajectories of the mobile detectors may account for the pose and/or movement of the object being imaged. The trajectory or trajectories may be based, in part, on the sampling for desired tomography. An image of an internal region of the object is reconstructed from detected signals of the mobile detectors using tomography.
    Type: Grant
    Filed: February 26, 2020
    Date of Patent: November 2, 2021
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Patent number: 11151759
    Abstract: An emission image is generated from poor quality emission data. A machine-learned model may be used to recover information. Emission imaging may be provided due to the recovery in a way that at least some diagnostically useful information is made available despite corruption that would otherwise result in less diagnostically useful information or no image at all.
    Type: Grant
    Filed: September 9, 2019
    Date of Patent: October 19, 2021
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Publication number: 20210259652
    Abstract: A detector used for tomography imaging is mobile, allowing the detector to move about an object (e.g., patient to be imaged). A swarm of such detectors, such as a swarm of drones with detectors, may be used for tomography imaging. The trajectory or trajectories of the mobile detectors may account for the pose and/or movement of the object being imaged. The trajectory or trajectories may be based, in part, on the sampling for desired tomography. An image of an internal region of the object is reconstructed from detected signals of the mobile detectors using tomography.
    Type: Application
    Filed: February 26, 2020
    Publication date: August 26, 2021
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Patent number: 11065475
    Abstract: A system and method include acquisition of a set of tomographic images of a patient volume associated with each of a plurality of timepoints of a first radiopharmaceutical therapy cycle, determination, for each of the plurality of timepoints, of a systematic uncertainty for each of a plurality of regions within the patient volume based on the set of tomographic images associated with the timepoint, determination, for each of the plurality of timepoints, of a quantitative statistical uncertainty based on the set of tomographic images associated with the timepoint, determination of a dose and a dose uncertainty for each of the plurality of regions based on the set of tomographic images, the systematic uncertainty and the quantitative statistical uncertainty for each of the plurality of timepoints, and display of a cumulative dose and cumulative dose uncertainty for each of the plurality of regions based on the dose and the dose uncertainty determined for each of the plurality of regions.
    Type: Grant
    Filed: December 3, 2018
    Date of Patent: July 20, 2021
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Michal Cachovan, Alexander Hans Vija
  • Publication number: 20210106302
    Abstract: For calibration of internal dose in nuclear imaging, the dose model used for estimating internal dose in a patient is calibrated. One or more values of the dose model (e.g., a physics simulation, dose kernels, or a transport model) are set based on measured dose. The dose may be measured relative to specific tissues and/or isotopes, providing for tracer and tissue specific calibration. For example, dose from the tracer to be injected into the patient is estimated from emissions as well as measured by a dosimeter in a tissue mimicking tissue mimicking object. These doses are used to calibrate the dose model, which calibrated dose model is then used to determine internal dose for the patient.
    Type: Application
    Filed: September 10, 2020
    Publication date: April 15, 2021
    Inventors: Alexander Hans Vija, Michal Cachovan, Miesher Rodrigues
  • Publication number: 20210106848
    Abstract: Parameterized model reconstruction is used for internal dose tomography. The parameterized model, solved for within the reconstruction, models the dose level and may account for diffusion, isotope half-life, and/or biological half-life. Using the detected emissions from different scans (e.g., from different scan sessions in a given cycle) as input for the one reconstruction, the parameterized model reconstruction determines the biodistribution of dose at any time.
    Type: Application
    Filed: April 10, 2020
    Publication date: April 15, 2021
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Publication number: 20210081778
    Abstract: A system and method include training of an artificial neural network to generate an output data set, the training based on the plurality of sets of emission data acquired using a first imaging modality and respective ones of data sets acquired using a second imaging modality.
    Type: Application
    Filed: September 13, 2019
    Publication date: March 18, 2021
    Inventors: Michal Cachovan, Alexander Hans Vija
  • Publication number: 20210074033
    Abstract: An emission image is generated from poor quality emission data. A machine-learned model may be used to recover information. Emission imaging may be provided due to the recovery in a way that at least some diagnostically useful information is made available despite corruption that would otherwise result in less diagnostically useful information or no image at all.
    Type: Application
    Filed: September 9, 2019
    Publication date: March 11, 2021
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Publication number: 20210073950
    Abstract: For denoising in SPECT, such as qSPECT, machine learning is used to relate settings to noise structure. Given the SPECT imaging arrangement for a patient, the machine-learned model estimates the structure of the noise. This noise structure may be used to denoise the reconstructed representation.
    Type: Application
    Filed: September 9, 2019
    Publication date: March 11, 2021
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Publication number: 20200279409
    Abstract: For partial volume correction, the partial volume effect is simulated using patient-specific segmentation. An organ or other object of the patient is segmented using anatomical imaging. For simulation, the locations of the patient-specific object or objects are sub-divided, creating artificial boundaries in the object. A test activity is assigned to each sub-division and forward projected. The difference of the forward projected activity to the test activity provides a location-by-location partial volume correction map. This correction map is used in reconstruction from the measured emissions, resulting in more accurate activity estimation with less partial volume effect.
    Type: Application
    Filed: May 14, 2020
    Publication date: September 3, 2020
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Patent number: 10699445
    Abstract: For partial volume correction, the partial volume effect is simulated using patient-specific segmentation. An organ or other object of the patient is segmented using anatomical imaging. For simulation, the locations of the patient-specific object or objects are sub-divided, creating artificial boundaries in the object. A test activity is assigned to each sub-division and forward projected. The difference of the forward projected activity to the test activity provides a location-by-location partial volume correction map. This correction map is used in reconstruction from the measured emissions, resulting in more accurate activity estimation with less partial volume effect.
    Type: Grant
    Filed: March 6, 2018
    Date of Patent: June 30, 2020
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Publication number: 20190168029
    Abstract: A system and method include acquisition of a set of tomographic images of a patient volume associated with each of a plurality of timepoints of a first radiopharmaceutical therapy cycle, determination, for each of the plurality of timepoints, of a systematic uncertainty for each of a plurality of regions within the patient volume based on the set of tomographic images associated with the timepoint, determination, for each of the plurality of timepoints, of a quantitative statistical uncertainty based on the set of tomographic images associated with the timepoint, determination of a dose and a dose uncertainty for each of the plurality of regions based on the set of tomographic images, the systematic uncertainty and the quantitative statistical uncertainty for each of the plurality of timepoints, and display of a cumulative dose and cumulative dose uncertainty for each of the plurality of regions based on the dose and the dose uncertainty determined for each of the plurality of regions.
    Type: Application
    Filed: December 3, 2018
    Publication date: June 6, 2019
    Inventors: Michal Cachovan, Alexander Hans Vija
  • Patent number: 10303849
    Abstract: A set of set of first modality data is received including at least one view comprising a plurality of gates. The set of first modality data is received from a first imaging modality of an imaging system. A set of second modality data is received from a second imaging modality of the imaging system. A motion corrected model of the set of first modality data is generated by forward projecting the set of first modality data including a motion estimate. An update factor for each of the plurality of views is generated by comparing at least one of the plurality of gates to the motion corrected model. The motion corrected model is updated by the update factor to generate a motion corrected image.
    Type: Grant
    Filed: June 12, 2015
    Date of Patent: May 28, 2019
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Michal Cachovan, Alexander Hans Vija
  • Patent number: 10304219
    Abstract: A set of first modality data is provided to an intra-reconstruction motion correction method. The set of first modality data includes a plurality of views. A set of second modality data is provided to the method. A motion estimate is generated for each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data. A motion corrected model of the set of first modality data is generated by a forward projection including the motion estimate.
    Type: Grant
    Filed: June 12, 2015
    Date of Patent: May 28, 2019
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Michal Cachovan, Alexander Hans Vija
  • Publication number: 20180315223
    Abstract: For partial volume correction, the partial volume effect is simulated using patient-specific segmentation. An organ or other object of the patient is segmented using anatomical imaging. For simulation, the locations of the patient-specific object or objects are sub-divided, creating artificial boundaries in the object. A test activity is assigned to each sub-division and forward projected. The difference of the forward projected activity to the test activity provides a location-by-location partial volume correction map. This correction map is used in reconstruction from the measured emissions, resulting in more accurate activity estimation with less partial volume effect.
    Type: Application
    Filed: March 6, 2018
    Publication date: November 1, 2018
    Inventors: Alexander Hans Vija, Michal Cachovan
  • Publication number: 20180033166
    Abstract: A set of first modality data is provided to an intra-reconstruction motion correction method. The set of first modality data includes a plurality of views. A set of second modality data is provided to the method. A motion estimate is generated for each of the plurality of views in the set of first modality data by registering the set of first modality data with the set of second modality data. A motion corrected model of the set of first modality data is generated by a forward projection including the motion estimate.
    Type: Application
    Filed: June 12, 2015
    Publication date: February 1, 2018
    Applicants: Siemens Healthcare GmbH, Siemens Medical Solutions
    Inventors: Michal Cachovan, Alexander Hans Vija
  • Publication number: 20170193159
    Abstract: A set of set of first modality data is received including at least one view comprising a plurality of gates. The set of first modality data is received from a first imaging modality of an imaging system. A set of second modality data is received from a second imaging modality of the imaging system. A motion corrected model of the set of first modality data is generated by forward projecting the set of first modality data including a motion estimate. An update factor for each of the plurality of views is generated by comparing at least one of the plurality of gates to the motion corrected model. The motion corrected model is updated by the update factor to generate a motion corrected image.
    Type: Application
    Filed: June 12, 2015
    Publication date: July 6, 2017
    Inventors: Michal Cachovan, Alexander Hans Vija