Patents by Inventor Simon Arberet

Simon Arberet 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: 20240077561
    Abstract: A computer-implemented method includes, based on scan data defining an input image, determining a reconstructed image using a reconstruction algorithm, and executing a data consistency operation for enforcing consistency between the input image and the reconstructed image. The data consistency operation includes using a norm ball projection that takes into account the available noise level information in order to automatically adjust the balance between the network prediction and the input measurements.
    Type: Application
    Filed: September 6, 2022
    Publication date: March 7, 2024
    Inventors: Simon Arberet, Boris Mailhe, Marcel Dominik Nickel, Thomas Benkert, Mahmoud Mostapha, Mariappan S. Nadar
  • Publication number: 20240070936
    Abstract: For reconstruction in sampling-based imaging, such as reconstruction in MR imaging, an iterative, multiple-mapping based hierarchal machine-learned network reconstruction may produce artifact corrected images based on under-sampled scans. Two or more mappings may be used to reduce the presence of artifacts, in some cases including localized low-noise-contribution artifacts, relative to reconstructions based on fully-sampled scans.
    Type: Application
    Filed: February 17, 2023
    Publication date: February 29, 2024
    Inventors: Antoine Edouard Adrien Cadiou, Mahmoud Mostapha, Simon Arberet, Mariappan S. Nadar
  • Publication number: 20240037817
    Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, an iterative, hierarchal network for regularization may decrease computational complexity. To further maintain computational complexity while improving robustness, auxiliary information is used in the regularization and corresponding reconstruction. The auxiliary information is in put to the machine-learned network.
    Type: Application
    Filed: July 26, 2022
    Publication date: February 1, 2024
    Inventors: Mahmoud Mostapha, Mariappan S. Nadar, Simon Arberet
  • Publication number: 20240036138
    Abstract: Systems and methods for reconstruction for a medical imaging system. An adapter is used to adapt scan data so that different quantities of repetitions or directions may be used to train and implement a single multichannel backbone network.
    Type: Application
    Filed: July 27, 2022
    Publication date: February 1, 2024
    Inventors: Simon Arberet, Marcel Dominik Nickel, Thomas Benkert, Mahmoud Mostapha, Mariappan S. Nadar
  • Publication number: 20240029323
    Abstract: Systems and methods for reconstruction for a medical imaging system. A scaling factor is used during the reconstruction process to adjust a step size of a gradient update. The adjustment of the step size of the gradient provides the ability to adjust a level of denoising by the reconstruction process.
    Type: Application
    Filed: July 19, 2022
    Publication date: January 25, 2024
    Inventors: Marcel Dominik Nickel, Thomas Benkert, Simon Arberet, Mahmoud Mostapha, Mariappan S. Nadar
  • Publication number: 20240020889
    Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, scanning is accelerated by under-sampling. In iterative reconstruction, the input to the regularizer is altered provide for correlation of non-local aliasing artifacts. Duplicates of the input image are shifted by different amounts based on the level of acceleration. The resulting shifted images are used to form the input to the regularizer. Providing an input based on shifts allows the regularization to suppress non-local as well as local aliasing artifacts.
    Type: Application
    Filed: July 12, 2022
    Publication date: January 18, 2024
    Inventors: Mahmoud Mostapha, Gregor Körzdörfer, Marcel Dominik Nickel, Esther Raithel, Simon Arberet, Mariappan S. Nadar
  • Patent number: 11835613
    Abstract: For reconstruction of an image in MRI, unsupervised training (i.e., data-driven) based on a scan of a given patient is used to reconstruct model parameters, such as estimating values of a contrast model and a motion model based on fit of images generated by the models for different readouts and times. The models and the estimated values from the scan-specific unsupervised training are then used to generate the patient image for that scan. This may avoid artifacts from binning different readouts together while allowing for scan sequences using multiple readouts.
    Type: Grant
    Filed: January 11, 2022
    Date of Patent: December 5, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Boris Mailhe, Dorin Comaniciu, Simon Arberet, Nirmal Janardhanan, Mariappan S. Nadar, Hongki Lim, Mahmoud Mostapha
  • Publication number: 20230298162
    Abstract: For reconstruction in medical imaging using phase correction, a machine learning model is trained for reconstruction of an image. The reconstruction may be for a sequence without repetitions or may be for a sequence with repetitions. Where repetitions are used, rather than using just a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions may used to train the machine learning model. In either approach, a phase correction is applied in machine training. A phase map is extracted from output of the model in training or extracted from the ground truth of the training data. The phase correction, based on the phase map, is applied to the ground truth and/or the output of the model in training. The resulting machine-learned model may better reconstruct an image as a result of having been trained using phase correction.
    Type: Application
    Filed: March 17, 2022
    Publication date: September 21, 2023
    Inventors: Simon Arberet, Marcel Dominik Nickel, Thomas Benkert, Mariappan S. Nadar
  • Patent number: 11748921
    Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, the number of iterations in deep learning-based reconstruction may be reduced by including a learnable extrapolation in one or more iterations. Regularization may be provided in fewer than all of the iterations of the reconstruction. The result of either approach alone or both together is better quality reconstruction and/or less computationally expensive reconstruction.
    Type: Grant
    Filed: November 13, 2020
    Date of Patent: September 5, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Simon Arberet, Mariappan S. Nadar, Boris Mailhe, Marcel Dominik Nickel
  • Publication number: 20230274418
    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: Application
    Filed: February 25, 2022
    Publication date: August 31, 2023
    Inventors: Simon Arberet, Mariappan S. Nadar, Mahmoud Mostapha, Dorin Comaniciu
  • Publication number: 20230251338
    Abstract: Techniques are provided for determining magnetic resonance images showing different contrasts in an examination. Magnetic resonance data for all magnetic resonance images are acquired using the same acquisition technique and the magnetic resonance images are reconstructed from their magnetic resonance data sets using at least one reconstruction algorithm. The reconstruction comprises at least one de-noising step. After acquisition of the magnetic resonance data, at least one noise strength measure is determined for the magnetic resonance data sets for each contrast, and de-noising strengths for the de-noising step are chosen individually for each contrast depending on the respective at least one noise strength measure.
    Type: Application
    Filed: February 8, 2023
    Publication date: August 10, 2023
    Applicant: Siemens Healthcare GmbH
    Inventors: Thomas Benkert, Marcel Dominik Nickel, Simon Arberet
  • Publication number: 20230221392
    Abstract: For reconstruction of an image in MRI, unsupervised training (i.e., data-driven) based on a scan of a given patient is used to reconstruct model parameters, such as estimating values of a contrast model and a motion model based on fit of images generated by the models for different readouts and times. The models and the estimated values from the scan-specific unsupervised training are then used to generate the patient image for that scan. This may avoid artifacts from binning different readouts together while allowing for scan sequences using multiple readouts.
    Type: Application
    Filed: January 11, 2022
    Publication date: July 13, 2023
    Inventors: Boris Mailhe, Dorin Comaniciu, Simon Arberet, Nirmal Janardhanan, Mariappan S. Nadar, Hongki Lim, Mahmoud Mostapha
  • Publication number: 20230108663
    Abstract: For magnetic resonance (MR) reconstruction using artificial intelligence (AI), the AI-based reconstruction for MR imaging systems is offloaded to one or more servers. A remote server performs AI-based reconstruction. A library of recent, old, custom, and/or publicly available AI-based reconstruction processes may be rapidly deployed and available to the server, which has the memory and processing resources for AI-based reconstruction. Load balancing of the data and/or between servers may improve performance.
    Type: Application
    Filed: October 6, 2021
    Publication date: April 6, 2023
    Inventors: Nirmal Janardhanan, Laszlo Lazar, Boris Mailhe, Simon Arberet, Mariappan S. Nadar, Dorin Comaniciu, Kelvin Chow, Michael Bush
  • Publication number: 20230095222
    Abstract: For reconstruction in medical imaging, user control of a characteristic (e.g., noise level) of the reconstructed image is provided. A machine-learned model alters the reconstructed image to enhance or reduce the characteristic. The user selected level of characteristic is then provided by combining the reconstructed image with the altered image based on the input level of the characteristic. Personalized or more controllable impression for medical imaging reconstruction is provided without requiring different reconstructions.
    Type: Application
    Filed: September 24, 2021
    Publication date: March 30, 2023
    Inventors: Mahmoud Mostapha, Boris Mailhe, Marcel Dominik Nickel, Gregor Körzdörfer, Simon Arberet, Mariappan S. Nadar
  • Publication number: 20230093752
    Abstract: One or more tractograms of a global tractography of a tissue of interest are determined. At least one instance of diffusion magnetic resonance imaging data of the tissue of interest is obtained. A trained machine-learning algorithm generates the one or more tractograms based on the at least one instance of the diffusion magnetic resonance imaging data.
    Type: Application
    Filed: September 8, 2022
    Publication date: March 23, 2023
    Inventors: Mahmoud Mostapha, Boris Mailhe, Dorin Comaniciu, Nirmal Janardhanan, Simon Arberet, Hongki Lim, Mariappan S. Nadar
  • Publication number: 20230085254
    Abstract: For reconstruction in medical imaging using a scan protocol with repetition, a machine learning model is trained for reconstruction of an image for each repetition. Rather than using a loss for that repetition in training, the loss based on an aggregation of images reconstructed from multiple repetitions is used to train the machine learning model. This loss for reconstruction of one repetition based on aggregation of reconstructions for multiple repetitions is based on deep set-based deep learning. The resulting machine-learned model may better reconstruct an image from a given repetition and/or a combined image from multiple repetitions than a model learned from a loss per repetition.
    Type: Application
    Filed: September 13, 2021
    Publication date: March 16, 2023
    Inventors: Simon Arberet, Boris Mailhe, Thomas Benkert, Marcel Dominik Nickel, Mahmoud Mostapha, Mariappan S. Nadar
  • Publication number: 20230079353
    Abstract: For correction of an image from an imaging system, an inverse solution uses an imaging prior as a regularizer and a physics model of the imaging system. An invertible network is used as the deep-learnt generative model in the regularizer of the inverse solution with the physics model of the degradation behavior of the imaging system. The prior model based on the invertible network provides a closed-form expression of the prior probability, resulting in a more versatile or accurate probability prediction.
    Type: Application
    Filed: September 14, 2021
    Publication date: March 16, 2023
    Inventors: Boris Mailhe, Mariappan S. Nadar, Simon Arberet, Mahmoud Mostapha
  • Publication number: 20230084413
    Abstract: For reconstruction, a machine-learned model is adapted to allow for reconstruction based on the repetitions available in some scanning. The reconstruction for one or more subsets is performed during the scanning. The machine-learned model is trained to reconstruction separately or independently for each repetition or to use information from previous repetitions without requiring waiting for completion of scanning. The reconstructed image may be displayed much more rapidly after completion of the acquisition since the reconstruction begins during the reconstruction.
    Type: Application
    Filed: September 13, 2021
    Publication date: March 16, 2023
    Inventors: Thomas Benkert, Marcel Dominik Nickel, Simon Arberet, Boris Mailhe, Mahmoud Mostapha
  • Patent number: 11585876
    Abstract: A system is provided for MRI coil sensitivity estimation and reconstruction At least two cascades of regularization networks are serially connected such that the output of a cascade is used as input of a following cascade, at least two deepsets coil sensitivity map networks are serially connected such that the output of a deepsets coil sensitivity map network is used as input of a following deepsets coil sensitivity map network (CR), and wherein the outputs of the deepsets coil sensitivity map networks are also used as inputs for the cascades.
    Type: Grant
    Filed: January 17, 2022
    Date of Patent: February 21, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Mahmoud Mostapha, Boris Mailhe, Mariappan S. Nadar, Simon Arberet, Marcel Dominik Nickel
  • Patent number: 11580381
    Abstract: For machine training and application of a trained complex-valued machine learning model, an activation function of the machine learning model, such as a neural network, includes a learnable parameter that is complex or defined in a complex domain with two dimensions, such as real and imaginary or magnitude and phase dimensions. The complex learnable parameter is trained for any of various applications, such as MR fingerprinting, other medical imaging, or non-medical uses.
    Type: Grant
    Filed: April 25, 2019
    Date of Patent: February 14, 2023
    Assignee: Siemens Healthcare GmbH
    Inventors: Guillaume Daval Frerot, Xiao Chen, Simon Arberet, Boris Mailhe, Mariappan S. Nadar, Peter Speier, Mathias Nittka