公开(公告)号：US12109061B2

公开(公告)日：2024-10-08

申请号：US17195694

申请日：2021-03-09

Applicant: SIEMENS HEALTHINEERS AG

Inventor： Lucian Mihai Itu ,  Tiziano Passerini ,  Saikiran Rapaka ,  Puneet Sharma ,  Chris Schwemmer ,  Max Schoebinger ,  Thomas Redel ,  Dorin Comaniciu

IPC: G06T7/11 ,  A61B5/00 ,  A61B5/026 ,  A61B6/00 ,  A61B6/03 ,  A61B6/50 ,  A61B8/06 ,  A61B8/08 ,  G06F18/21 ,  G06F18/22 ,  G06F18/2413 ,  G06T7/00 ,  G06V10/42 ,  G06V10/776 ,  G16H20/00 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  A61B5/02 ,  A61B6/46 ,  A61B8/00 ,  G16H30/20

CPC classification number: A61B6/5217 ,  A61B5/026 ,  A61B5/7267 ,  A61B6/032 ,  A61B6/504 ,  A61B6/507 ,  A61B8/06 ,  A61B8/065 ,  A61B8/5223 ,  G06F18/217 ,  G06F18/22 ,  G06F18/2413 ,  G06T7/0012 ,  G06T7/11 ,  G06V10/42 ,  G06V10/776 ,  G16H20/00 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  A61B5/02007 ,  A61B5/02028 ,  A61B5/0263 ,  A61B5/743 ,  A61B6/469 ,  A61B8/469 ,  A61B2576/00 ,  G06T2200/04 ,  G06T2207/10072 ,  G06T2207/10076 ,  G06T2207/20081 ,  G06T2207/30101 ,  G06T2207/30104 ,  G16H30/20

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. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

公开(公告)号：US12105174B2

公开(公告)日：2024-10-01

申请号：US17446223

申请日：2021-08-27

Applicant: SIEMENS HEALTHINEERS AG

Inventor： Puneet Sharma ,  Lucian Mihai Itu

IPC: G01R33/563 ,  G01R33/567 ,  G06N3/08

CPC classification number: G01R33/56366 ,  G01R33/567 ,  G06N3/08

Abstract: A technique for determining a cardiac metric from rest and stress perfusion cardiac magnetic resonance (CMR) images is provided. A neural network system for determining at least one cardiac metric from CMR images comprises an input layer configured to receive at least one CMR image representative of a rest perfusion state and at least one CMR image representative of a stress perfusion state. The neural network system further comprises an output layer configured to output at least one cardiac metric based on the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state. The neural network system with interconnections between the input layer and the output layer is trained by a plurality of datasets. Each of the datasets comprises an instance of the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state for the input layer and the at least one cardiac metric for the output layer.

公开(公告)号：US12004860B2

公开(公告)日：2024-06-11

申请号：US17305391

申请日：2021-07-07

Applicant: SIEMENS HEALTHINEERS AG

Inventor： Paul Klein ,  Ingo Schmuecking ,  Costin Florian Ciusdel ,  Lucian Mihai Itu ,  Tiziano Passerini ,  Puneet Sharma

IPC: A61B5/308 ,  A61B5/00 ,  A61B5/026

CPC classification number: A61B5/308 ,  A61B5/026 ,  A61B5/7267

Abstract: A method includes processing at least one input dataset (using a multi-level processing algorithm, one or more of the at least one input dataset comprising imaging data of an echocardiography of a cardiovascular system of a patient. The multi-level processing algorithm comprises a multi-task level and a consolidation-task level. An input of the consolidation-task level is coupled to an output of the multi-task level. The multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset. The consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient.

公开(公告)号：US11931195B2

公开(公告)日：2024-03-19

申请号：US17602820

申请日：2019-07-22

Applicant: Siemens Healthineers AG

Inventor： Lucian Mihai Itu ,  Diana Ioana Stoian ,  Tiziano Passerini ,  Puneet Sharma

IPC: G16H50/20 ,  A61B6/00 ,  G06T7/00

CPC classification number: A61B6/504 ,  A61B6/487 ,  G06T7/0012 ,  G16H50/20 ,  G06T2207/10081 ,  G06T2207/10121 ,  G06T2207/30101

Abstract: Systems and methods are provided for training an artificial intelligence model for detecting calcified portions of a vessel in an input medical image. One or more first medical images of a vessel in a first modality and one or more second medical image of the vessel in a second modality are received. Calcified portions of the vessel are detected in the one or more first medical images, The artificial intelligence model is trained for detecting calcified portions of the vessel in the input medical image in the second modality based on the one or more second medical images and the detected calcified portions of the vessel detected in the one or more first medical images.

公开(公告)号：US20240423575A1

公开(公告)日：2024-12-26

申请号：US18825275

申请日：2024-09-05

Applicant: Siemens Healthineers AG

Inventor： Lucian Mihai Itu ,  Tiziano Passerini ,  Saikiran Rapaka ,  Puneet Sharma ,  Chris Schwemmer ,  Max Schoebinger ,  Thomas Redel ,  Dorin Comaniciu

IPC: A61B6/00 ,  A61B5/00 ,  A61B5/02 ,  A61B5/026 ,  A61B6/03 ,  A61B6/46 ,  A61B6/50 ,  A61B8/00 ,  A61B8/06 ,  A61B8/08 ,  G06F18/21 ,  G06F18/22 ,  G06F18/2413 ,  G06T7/00 ,  G06T7/11 ,  G06V10/42 ,  G06V10/776 ,  G16H20/00 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50

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. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

公开(公告)号：US12089918B2

公开(公告)日：2024-09-17

申请号：US17070993

申请日：2020-10-15

Applicant: SIEMENS HEALTHINEERS AG

Inventor： Puneet Sharma ,  Lucian Mihai Itu ,  Saikiran Rapaka ,  Frank Sauer

IPC: A61B5/02 ,  A61B5/00 ,  A61B5/021 ,  A61B5/029 ,  A61B5/11 ,  A61B6/03 ,  G06F18/214 ,  G06F18/2413 ,  G06T7/00 ,  G16H50/20

CPC classification number: A61B5/02007 ,  A61B5/02028 ,  A61B5/021 ,  A61B5/029 ,  A61B5/1128 ,  A61B5/7275 ,  G06F18/214 ,  G06F18/2413 ,  G06T7/0012 ,  G16H50/20 ,  A61B6/032 ,  A61B2576/023

Abstract: Systems and methods for determining a quantity of interest of a patient comprise receiving patient data of the patient at a first physiological state. A value of a quantity of interest of the patient at the first physiological state is determined based on the patient data. The quantity of interest represents a medical characteristic of the patient. Features are extracted from the patient data, wherein the features which are extracted are based on the quantity of interest to be determined for the patient at a second physiological state. The value of the quantity of interest of the patient at the first physiological state is mapped to a value of the quantity of interest of the patient at the second physiological state based on the extracted features.

公开(公告)号：US20240161285A1

公开(公告)日：2024-05-16

申请号：US18465447

申请日：2023-09-12

Applicant: Siemens Healthineers AG

Inventor： Dominik Neumann ,  Alexandru Turcea ,  Lucian Mihai Itu ,  Tiziano Passerini ,  Mehmet Akif Gulsun ,  Martin Berger

IPC: G06T7/00 ,  G06T3/40 ,  G06T7/60

CPC classification number: G06T7/0012 ,  G06T3/40 ,  G06T7/60 ,  G06T2207/10116 ,  G06T2207/30048 ,  G06T2207/30101 ,  G06T2207/30168

Abstract: Various aspects of the disclosure generally pertain to determining estimates of hemodynamic properties based on angiographic x-ray examinations of a coronary system. Various aspects of the disclosure specifically pertain to determining such estimates based on single frame metrics operating on two-dimensional images. For example, the fractional flow reserve (FFR) can be computed.

公开(公告)号：US11995823B2

公开(公告)日：2024-05-28

申请号：US17445204

申请日：2021-08-17

Applicant: SIEMENS HEALTHINEERS AG

Inventor： Lucian Mihai Itu ,  Andrei Bogdan Gheorghita ,  Puneet Sharma ,  Teodora Chitiboi

IPC: G06T7/00

CPC classification number: G06T7/0012 ,  G06T2207/10088 ,  G06T2207/30048

Abstract: A value indicative of an ejection fraction, EF, of a cardiac chamber of a heart is based on a temporal sequence of cardiac magnetic resonance, CMR, images of the cardiac chamber. A neural network system has an input layer configured to receive the temporal sequence of a stack of slices of the CMR images along an axis of the heart. The temporal sequence is one or multiple consecutive cardiac cycles of the heart. The neural network system has an output layer configured to output the value indicative of the EF based on the temporal sequence. The neural network system has interconnections between the input layer and the output layer and is trained with a plurality of datasets. Each of the datasets comprises an instance temporal sequence of the stack of slices of the CMR images along the axis over one or multiple consecutive cardiac cycles for the input layer and an associated instance value indicative of the EF for the output layer.

公开(公告)号：US12021967B2

公开(公告)日：2024-06-25

申请号：US17449463

申请日：2021-09-30

Applicant: SIEMENS HEALTHINEERS AG

Inventor： Andreea Bianca Popescu ,  Cosmin Ioan Nita ,  Ioana Taca ,  Anamaria Vizitiu ,  Lucian Mihai Itu ,  Puneet Sharma

IPC: H04L29/06 ,  G16H50/20 ,  H04L9/00

CPC classification number: H04L9/008 ,  G16H50/20

Abstract: Data privacy is a major concern when accessing and processing sensitive medical data. Homomorphic Encryption (HE) is one technique that preserves privacy while allowing computations to be performed on encrypted data. An encoding method enables typical HE schemes to operate on real-valued numbers of arbitrary precision and size by representing the numbers as a series of polynomial terms.