Providing an indication regarding the affliction of a patient with an infectious respiratory disease based on magnetic resonance imaging data

Patent metadata

Field	Value
Publication number	US-12465238-B2
Application number	US-202117921413-A
Country	US
Kind code	B2
Filing date	Apr 8, 2021
Priority date	Apr 29, 2020
Publication date	Nov 11, 2025
Grant date	Nov 11, 2025

Abstract

A computer-implemented method for providing output data comprising an indication regarding the affliction of a patient with an infectious respiratory disease, the method comprises receiving magnetic resonance imaging data, the magnetic resonance imaging data acquired using a magnetic resonance imaging system, the magnetic resonance imaging data comprising a lung region of the patient; applying a trained function to the magnetic resonance imaging data to generate the output data, the trained function being based on an artificial neural network and the output data comprising the indication regarding the affliction of the patience with the infectious respiratory disease; and proving the output data.

First claim

The invention claimed is:

1 . A computer-implemented method for providing output data comprising an indication regarding an affliction of a patient with an infectious respiratory disease, the method comprising:
receiving magnetic resonance imaging data, the magnetic resonance imaging data acquired using a magnetic resonance imaging system, the magnetic resonance imaging data comprising a lung region of the patient; applying a trained function to the magnetic resonance imaging data to generate the output data, the trained function being based on an artificial neural network and the output data comprising the indication regarding the affliction of the patient with the infectious respiratory disease, the indication regarding the affliction of the patient with the infectious respiratory disease including an indication whether the patient is afflicted with a post-acute condition following an acute infection with the infectious respiratory disease; and providing the output data.

2 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging system has a main magnetic field strength of less than 0.7 Tesla.

3 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging system has a main magnetic field strength between 0.5 Tesla and 0.6 Tesla.

4 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging system has a system architecture for imaging a chest region of the patient.

5 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise magnetic resonance imaging data acquired using a T2-weighted Single-shot Turbo Spin Echo imaging (HASTE) pulse sequence and a T1-weighted Gradient Echo sequence.

6 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise magnetic resonance imaging data acquired using (i) a spiral or 3D radial Ultra-short echo-time pulse sequence and a T2-weighted, or (ii) proton density (PD)-weighted Turbo Spin Echo (TSE) pulse sequence.

7 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise magnetic resonance imaging data acquired using a T2-weighted BLADE pulse sequence and a radial Volumetric Interpolated Breath-hold Examination (VIBE) pulse sequence.

8 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise contrast-agent based magnetic resonance imaging data acquired using at least one technique, the at least one technique including at least one of an inhalation of an oxygen-based contrast agent by the patient, an administration of a hyperpolarized contrast agent to the patient or an administration of a Fluorine-19 contrast agent to the patient.

9 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise magnetic resonance imaging data acquired using exclusively a single T2-weighted or PD-weighted imaging sequence.

10 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise magnetic resonance imaging data acquired using exclusively a single spiral or 3D radial Ultra-short echo-time pulse sequence.

11 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise magnetic resonance imaging data acquired during free breathing of the patient.

12 . The computer-implemented method of claim 1 , wherein the artificial neural network is a convolutional neural network.

13 . The computer-implemented method of claim 1 , wherein the artificial neural network is a recurrent neural network.

14 . The computer-implemented method of claim 1 , wherein the output data comprise an indication whether the patient is afflicted with the infectious respiratory disease.

15 . The computer-implemented method of claim 1 , wherein the infectious respiratory disease is COVID-19 (coronavirus disease 2019).

16 . The computer-implemented method of claim 1 , wherein the output data comprise a classification if the patient is afflicted with COVID-19 (coronavirus disease 2019) or not.

17 . The computer-implemented method of claim 1 , wherein the output data comprise a classification if the patient is healthy or if the patient is afflicted with a disease, the disease being pneumonia, middle east respiratory syndrome (MERS) or severe acute respiratory syndrome (SARS).

18 . The computer-implemented method of claim 1 , wherein the output data comprise a classification if the patient afflicted with (coronavirus disease 2019) is in an acute COVID-19 stage or has already recovered from COVID-19.

19 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data form a part of input data which the trained function is applied to generate the output data, wherein the method further comprises:
receiving temperature mapping data of the patient acquired with the magnetic resonance imaging system, wherein the input data further comprise the temperature mapping data.

20 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data form a part of input data which the trained function is applied to generate the output data, wherein the method further comprises:
receiving at least one of demographic data of the patient or epidemiologic data of the patient, wherein the input data further comprise the at least one of the demographic data or the epidemiologic data.

21 . A non-transitory computer readable medium comprising instructions which, when executed by a computer system, cause the computer system to perform the method of claim 1 .

22 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging system has a main magnetic field strength of less than 1.0 Tesla.

23 . A non-transitory computer-readable medium comprising instructions which, when executed by a computer system, cause the computer system to perform the method of claim 22 .

24 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise morphological magnetic resonance imaging data acquired using at least one magnetic resonance imaging pulse sequence, the at least one magnetic resonance imaging pulse sequence being at least one of
a T2-weighted half-Fourier acquisition Single-shot Turbo Spin Echo imaging (HASTE) pulse sequence, a T2-weighted BLADE pulse sequence, a T2-weighted Turbo Spin Echo (TSE) pulse sequence, a T1-weighted Gradient Echo pulse sequence, a True fast imaging with steady-state free precision (FISP) pulse sequence, a pulse sequence measuring a free-induction-decay (FID), a radial Volumetric Interpolated Breath-hold Examination (VIBE) pulse sequence, a spiral VIBE pulse sequence, a Gradient and Spin Echo (GRASE) pulse sequence, or a radial TSE pulse sequence.

25 . The computer-implemented method of claim 24 , wherein the magnetic resonance imaging data comprise a combination of the morphological magnetic resonance imaging data and functional magnetic resonance imaging data, the functional magnetic resonance imaging data including at least one of a lung ventilation map or a lung perfusion map.

26 . The computer-implemented method of claim 1 , wherein the magnetic resonance imaging data comprise functional magnetic resonance imaging data, the functional magnetic resonance imaging data including at least one of a lung ventilation map or

Assignees

• Siemens Healthcare Gmbh
• Siemens Healthineers Ag

Inventors

• Zeller Mario
• Grodzki David
• Benkert Thomas

Classifications

• A61B5/704

  Tables · CPC title

• A61B5/055

  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

• G06T2207/30061

  Lung · CPC title

• G06T2207/10088

  Magnetic resonance imaging [MRI] · CPC title

• G01R33/56366

  Perfusion imaging · CPC title