Patents by Inventor Dorin Comaniciu

Dorin Comaniciu 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: 20250248616
    Abstract: Systems and methods that leverage the power of artificial intelligence (AI) to enhance the process of whole-body MRI scanning. AI models optimize the acquisition protocol, resulting in shorter and more patient-friendly scan durations. Furthermore, AI models aid in the automatic interpretation of the imaging data, highlighting potential areas of concern and streamlining the diagnostic process.
    Type: Application
    Filed: February 5, 2024
    Publication date: August 7, 2025
    Inventors: Sasa Grbic, Dorin Comaniciu
  • Publication number: 20250232445
    Abstract: Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.
    Type: Application
    Filed: February 20, 2025
    Publication date: July 17, 2025
    Inventors: Ali Kamen, Bin Lou, Bibo Shi, Nicolas Von Roden, Berthold Kiefer, Robert Grimm, Heinrich von Busch, Mamadou Diallo, Tongbai Meng, Dorin Comaniciu, David Jean Winkel, Xin Yu
  • Patent number: 12347552
    Abstract: A scheduling system includes: a plurality of input devices configured to output medical data, a workforce storage, configured to store working characteristics of a plurality of doctors, and a scheduler configured to receive as input data related to the medical data and the working characteristics, and configured to provide as output a plurality of schedules for the plurality of doctors for analysing the medical data.
    Type: Grant
    Filed: September 1, 2021
    Date of Patent: July 1, 2025
    Assignee: Siemens Healthineers AG
    Inventors: Ahmet Tuysuzoglu, Eli Gibson, Dorin Comaniciu
  • Patent number: 12329565
    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: Grant
    Filed: November 22, 2021
    Date of Patent: June 17, 2025
    Assignee: Siemens Medical Solutions USA, Inc.
    Inventors: Huseyin Tek, Bogdan Georgescu, Tommaso Mansi, Frank Sauer, Dorin Comaniciu, Helene C. Houle, Ingmar Voigt
  • Patent number: 12295774
    Abstract: Systems and methods for occlusion detection in medical images are provided. An input medical image of one or more vessels in an anatomical object of a patient is received. One or more anatomical landmarks are identified in the input medical image. A first patch and one or more additional patches are extracted from the input medical image based on the identified one or more anatomical landmarks. The first patch and the one or more additional patches depict different portions of the anatomical object. Features are extracted from the first patch and the one or more additional patches using a machine learning based feature extractor network. An occlusion in the one or more vessels is detected in the first patch based on the extracted features with or without modeling features on a probability distribution function. Results of the detecting are output.
    Type: Grant
    Filed: June 20, 2022
    Date of Patent: May 13, 2025
    Assignee: Siemens Healthineers AG
    Inventors: Bogdan Georgescu, Eli Gibson, Thomas Re, Dorin Comaniciu
  • Publication number: 20250149187
    Abstract: Systems and methods for generating radiology passages are provided. An input common data element and one or more associated input values are received. A radiology passage is generated based on the input common data element and the one or more associated input values using a trained language model. The generated radiology passage is output.
    Type: Application
    Filed: September 26, 2024
    Publication date: May 8, 2025
    Inventors: Rikhiya Ghosh, Sanjeev Kumar Karn, Poikavila Ullaskrishnan, Oladimeji Farri, Dorin Comaniciu
  • Publication number: 20250111656
    Abstract: Systems and methods for performing a medical analysis task using a trained machine learning based task network are provided. Input medical data is received. A medical analysis task is performed using a trained machine learning based task network based on the input medical data. Results of the medical analysis task are output. The trained machine learning based task network is trained by: receiving unannotated training medical data; generating weakly-supervised labels for the unannotated training medical data using one or more trained machine learning based supervised learning networks; training the machine learning based task network for performing the medical analysis task based on 1) the unannotated training medical data, 2) self-supervised labels for the unannotated training medical data learned via self-supervised learning, and 3) the generated weakly-supervised labels for the unannotated training medical data; and outputting the trained machine learning based task network.
    Type: Application
    Filed: September 28, 2023
    Publication date: April 3, 2025
    Inventors: Venkatesh Narasimha Murthy, Bogdan Georgescu, Florin-Cristian Ghesu, Mehmet Akif Gulsun, Dominik Neumann, Alexandru Constantin Serban, Dorin Comaniciu
  • Publication number: 20250107756
    Abstract: Rather than the whole-body scout scan, multiple scout scans of different regions are used to locate the target for magnetic resonance scanning. A diagnostic scan may be planned in as little time as possible based on localization using multiple scout scans of different regions of the patient. The planning of the different regions may be optimized to minimize the number and/or time for localizing the target.
    Type: Application
    Filed: September 28, 2023
    Publication date: April 3, 2025
    Inventors: Boris Mailhe, Stefan Popescu, Dorin Comaniciu
  • Publication number: 20250104276
    Abstract: Systems and methods for performing a medical imaging analysis task based on pixelwise positionally encoded features are provided. One or more input medical images are received. One or more pixelwise positional embedding images are generated for the one or more input medical images using a spatially varying function. Patches are extracted from the one or more input medical images and the one or more pixelwise positional embedding images. The patches extracted from the one or more input medical images are encoded with corresponding ones of the patches extracted from the one or more pixelwise positional embedding images into pixelwise positionally encoded features. A medical imaging analysis task is performed using a machine learning based network based on the pixelwise positionally encoded features. Results of the medical imaging analysis task are output.
    Type: Application
    Filed: September 26, 2023
    Publication date: March 27, 2025
    Inventors: Gengyan Zhao, Badhan Kumar Das, Eli Gibson, Dorin Comaniciu
  • Publication number: 20250093448
    Abstract: For artificial Intelligence-based optimization of a MR sequence, an agent machine trained with reinforcement learning generates the MR pulse sequence for a patient. The agent may generate values for multiple or all the parameters defining the MR pulse sequence. The agent was trained using the end goal or task (e.g., MR map or segmentation) as the reward function, so the MR pulse sequence generated by the agent provides good quality MR imaging. The agent generates the MR pulse sequence quickly and without requiring multiple sequences to be used on the patient.
    Type: Application
    Filed: September 18, 2023
    Publication date: March 20, 2025
    Inventors: Simon Arberet, Dorin Comaniciu
  • Publication number: 20250095849
    Abstract: Systems and methods for managing patient diagnostic and therapy workflows in a hospital and/or radiology centers. A radiology recommendation agent is trained using reinforcement learning and a simulation environment in which the agent takes actions and receives feedback from the simulation environment based on how its action affect the simulation environment over time.
    Type: Application
    Filed: February 27, 2024
    Publication date: March 20, 2025
    Inventors: Vivek Singh, Ankur Kapoor, Ingo Schmuecking, Scott Steingall, David Scholl, Dorin Comaniciu
  • Publication number: 20250079011
    Abstract: Systems and methods for performing one or more medical analysis tasks are provided. Patient data of a patient is received for a set of biomarkers acquired at one or more time points within a period of time. The patient data is encoded using an encoder network to generate patient data embeddings. One or more medical analysis tasks are performed based on the patient data embeddings using one or more decoder networks. The one or more medical analysis tasks comprise generating recommendations for a clinical course of action. Results of the one or more medical analysis tasks are output.
    Type: Application
    Filed: September 1, 2023
    Publication date: March 6, 2025
    Inventors: Vivek Singh, Ali Kamen, Raj Gopalan, Jamie Gramz, Dorin Comaniciu
  • Publication number: 20250078471
    Abstract: Systems and methods for assigning labels to medical images are provided. One or more prompts comprising instructions for assigning labels to medical images are received. The labels are assigned to the medical images using a large language model based on 1) the instructions and 2) patient data stored in a plurality of patient databases. The assignment of the labels to the medical images is output.
    Type: Application
    Filed: August 28, 2023
    Publication date: March 6, 2025
    Inventors: Sasa Grbic, Dorin Comaniciu
  • Publication number: 20250068668
    Abstract: Systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instruction using a large language model. The response is output.
    Type: Application
    Filed: August 22, 2023
    Publication date: February 27, 2025
    Inventors: Sasa Grbic, Eli Gibson, Oladimeji Farri, Bogdan Georgescu, Gianluca Paladini, Puneet Sharma, Daphne Yu, Dorin Comaniciu
  • Publication number: 20250054603
    Abstract: Systems and methods for performing one or more medical imaging analysis tasks are provided. A sequence of medical images is received. One or more patches are extracted from each image of the sequence of medical images. Spatio-temporal features are extracted from the one or more extracted patches using a machine learning based encoder network. One or more medical imaging analysis tasks are performed based on the extracted spatio-temporal features. Results of the one or more medical imaging analysis tasks are output. The machine learning based encoder network is trained by receiving a sequence of training medical images. Patches of a first set of images of the sequence of training medical images are masked according to a first masking strategy. Patches of a second set of images of the sequence of training medical images are masked according to a second masking strategy.
    Type: Application
    Filed: January 23, 2024
    Publication date: February 13, 2025
    Inventors: Dominik Neumann, Saahil Islam, Venkatesh Narasimha Murthy, Serkan Cimen, Florin-Cristian Ghesu, Puneet Sharma, Dorin Comaniciu
  • Publication number: 20250045912
    Abstract: A system for synthesizing medical images including synthesizing medical abnormalities has multiple diffusion model based denoising stages. At a first denoising stage, a machine-learned network denoises a first noise input to obtain an abnormality spatial mask detailing positional and structural characteristics of the synthesized medical abnormality. At a second denoising stage, a machine-learned network denoises a second noise input based on the abnormality spatial mask and a pre-abnormality image to obtain a synthesized medical image that corresponds to the pre-abnormality image with the synthesized medical abnormality inserted consistent with the abnormality spatial mask.
    Type: Application
    Filed: August 4, 2023
    Publication date: February 6, 2025
    Inventors: Gengyan Zhao, Eli Gibson, Boris Mailhe, Youngjin Yoo, Bogdan Georgescu, Dorin Comaniciu
  • Publication number: 20250044390
    Abstract: Systems and methods for AI-powered histological fingerprinting in magnetic resonance imaging. MR signal data of an object is acquired using a high sensitivity scanner. Ground truth tissue microstructure data is acquired for the object. A forward model is learned using machine learning. The forward model is used to generate a dictionary or to train a model to map the signals to the histological parameters including the tissue microstructure of a scanner object. A signal-to-signal translation model is also provided to provide signals with improved sensitivity.
    Type: Application
    Filed: August 3, 2023
    Publication date: February 6, 2025
    Inventors: Mahmoud Mostapha, Dorin Comaniciu, Mariappan S. Nadar
  • Publication number: 20250046434
    Abstract: Systems and methods for determining an uncertainty measure associated with results of a medical task performed by an LLM (large language model) are provided. One or more prompts associated with a medical task are received. At least one of the one or more prompts are encoded into a set of features using a feature encoder network of an LLM. The medical task is performed based on the set of features using a decoder network of the LLM. An uncertainty measure associated with results of the medical task is determined based on the set of features using an uncertainty quantification module of the LLM. The results of the medical task and the uncertainty measure are output.
    Type: Application
    Filed: August 1, 2023
    Publication date: February 6, 2025
    Inventors: Bogdan Georgescu, Oladimeji Farri, Dorin Comaniciu
  • Patent number: 12182998
    Abstract: For reconstruction in medical imaging, self-consistency using data augmentation is improved by including data consistency. Artificial intelligence is trained based on self-consistency and data consistency, allowing training without supervision. Fully sampled data and/or ground truth is not needed but may be used. The machine-trained model is less likely to reconstruct images with motion artifacts, and/or the training data may be more easily gathered by not requiring full sampling.
    Type: Grant
    Filed: February 25, 2022
    Date of Patent: December 31, 2024
    Assignee: Siemens Healthineers AG
    Inventors: Simon Arberet, Mariappan S. Nadar, Mahmoud Mostapha, Dorin Comaniciu
  • Publication number: 20240428401
    Abstract: Systems and methods for performing an assessment of one or more tumors are provided. A plurality of input medical images of a patient acquired at a plurality of points in time is received. One or more tumors are identified in each of the plurality of input medical images. A tumor burden of the patient is determined for each of the plurality of points in time based on the one or more identified tumors using one or more machine learning based networks. An assessment of the one or more tumors is performed based on the tumor burden of the patient determined for each of the plurality of points in time. Results of the assessment of the one or more tumors are output.
    Type: Application
    Filed: June 22, 2023
    Publication date: December 26, 2024
    Inventors: Bin Lou, Julian Rosenman, Patrick Kupelian, Zhoubing Xu, Sasa Grbic, Ali Kamen, Dorin Comaniciu