METHOD FOR COMPUTER-ASSISTED CONFIGURATION OF A MEDICAL IMAGING DEVICE

• US 20120190962A1
• Filed: 01/18/2012
• Published: 07/26/2012
• Est. Priority Date: 01/20/2011
• Status: Abandoned Application

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Glaser-Seidnitzer, Karlheinz, Hauptmann, Werner, Otte, Clemens
• Application Number:
  US13/352,760
• Publication Number:
  US 20120190962A1
• Time in Patent Office: Days
• US Class Current: 600/407
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/545 involving automatic set-up of acquisition parameters
• CPC Class Codes: G01R 33/543 Control of the operation of the MR system, e.g. setting of acquisition parameters prior to or during MR data acquisition, dynamic shimming, use of one or more scout images for scan plane prescription G01R33/546 takes precedence
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 70/20 relating to practices or guidelines

Abstract

In a method for computer-assisted configuration of a medical imaging device for examination of a patient, training data are provided that include multiple variants of protocols in the form of protocol parameter sets for operation of the imaging device. The training data also include patient-specific parameter sets with one or more features of a patient, the patient-specific parameter sets being associated with the respective protocol parameter sets. Based on the training data, relations between the protocol parameter sets and the patient-specific parameter sets are learned with a data-driven leaning method and stored as patterns in a knowledge base. In an application phase, a protocol parameter set suitable for the examination of the patient can be determined with the use of the patterns in the knowledge base, depending on features of a patient that are provided to the imaging device.

Claims

1. A method for computer-assisted configuration of a medical imaging device for examination of a patient, comprising:

providing training data to a computerized processor that comprise multiple variants of protocols as protocol parameter sets for operation of an imaging device, and patient-specific parameter sets each representing at least one feature of respectively different patients and, in said training data, said protocol parameter sets being respectively associated with said patient-specific parameter sets by means of each protocol parameter set being provided for operating the imaging device for examination of a patient having said at least one feature of the patient-specific parameter set associated therewith;

in said computerized processor, automatically using said training data to generate relations between said multiple protocol parameter sets and said patient-specific parameter sets by implementing a data-driven learning method that produces a plurality of patterns that are stored in a knowledge base; and

via the imaging device, providing a feature of a current patient to be examined with the imaging device and accessing the patterns stored in said knowledge base to determine a protocol, as a selected protocol, from among said multiple variants of protocols, that is appropriate for operating the imaging device to examine said current patient, and making the selected protocol available at an output of the processor in a form for configuring the imaging device to implement the examination of the current patient.

2. A method as claimed in claim 1 comprising, in said patient-specific parameter sets, including, in said training data, at least one patient feature selected from the group consisting of weight, gender, girth, height, age, physical condition, prior illnesses, and previous examinations.

3. A method as claimed in claim 1 comprising:

also including diagnosis-specific parameter sets in said training data respectively associated with different diagnoses that can be implemented using said imaging device, each diagnosis-specific parameter set representing at least one diagnosis feature relevant to the diagnosis associated therewith and, in said training data set, said diagnosis-specific parameter sets being respectively associated with the respective protocol parameter sets by means of a respective protocol parameter set being provided to implement the diagnosis with which the respective diagnosis-specific parameter set is associated, and wherein said computerized processor generates said patterns in said knowledge base also using said diagnosis-specific parameter sets; and

also via said imaging device, providing said computerized processor with a current diagnosis to be implemented using said examination of said current patient and determining said selected protocol using both said feature of said current patient and said current diagnosis.

4. A method as claimed in claim 3 comprising also providing data representing expert knowledge to said computerized processor, said expert knowledge comprising at least one limitation on at least one of said protocol parameter sets, said patient-specific parameter sets, and said diagnosis-specific parameter sets, and, in said computerized processor using said at least one limitation to generate said patterns that are stored in said knowledge base.

5. A method as claimed in claim 1 comprising employing, as said data-driven learning method, a method selected from the group consisting of a statistical learning method, a learning method based on a probabilistic network, a learning network based on a semantic network, and a learning network based on a neural network.

6. A method as claimed in claim 5 comprising employing a statistical learning method as said data-driven learning method and selecting said statistical learning method from the group consisting of clustering vector machines and support vector machines.

7. A method as claimed in claim 5 comprising employing a method based on a probabilistic network as said data-driven learning method, said method based on a probabilistic network being a method based on a Bayesian network.

8. A method as claimed in claim 1 comprising learning rules, as said patterns, with said data-driven learning method in said computerized processor.

9. A method as claimed in claim 1 comprising providing data to said computerized processor representing expert knowledge, said expert knowledge comprising at least one limitation on at least one of said protocol parameter sets and said patient-specific parameter sets and, in said computerized processor, using said at least one limitation to generate said patterns that are stored in said knowledge base.

10. A method for controlling a medical imaging device for examination of a patient, said medical imaging device comprising a computerized control unit, said method comprising:

providing training data to a computerized processor that comprise multiple variants of protocols as protocol parameter sets for operation of an imaging device, and patient-specific parameter sets each representing at least one feature of respectively different patients and, in said training data, said protocol parameter sets being respectively associated with said patient-specific parameter sets by means of each protocol parameter set being provided for operating the imaging device for examination of a patient having said at least one feature of the patient-specific parameter set associated therewith;

in said computerized processor, automatically using said training data to generate relations between said multiple protocol parameter sets and said patient-specific parameter sets by implementing a data-driven learning method that produces a plurality of patterns that are stored in a knowledge base;

via the imaging device, providing a feature of a current patient to be examined with the imaging device and accessing the patterns stored in said knowledge base to determine a protocol, as a selected protocol, from among said multiple variants of protocols, that is appropriate for operating the imaging device to examine said current patient, and making the selected protocol available at an output of the processor in a form for configuring the imaging device to implement the examination of the current patient; and

operating the imaging device according to the selected protocol.

11. A method as claimed in claim 10 comprising:

also including diagnosis-specific parameter sets in said training data respectively associated with different diagnoses that can be implemented using said imaging device, each diagnosis-specific parameter set representing at least one diagnosis feature relevant to the diagnosis associated therewith and, in said training data set, said diagnosis-specific parameter sets being respectively associated with the respective protocol parameter sets by means of a respective protocol parameter set being provided to implement the diagnosis with which the respective diagnosis-specific parameter set is associated, and wherein said computerized processor generates said patterns in said knowledge base also using said diagnosis-specific parameter sets; and

also via said imaging device, providing said computerized processor with a current diagnosis to be implemented using said examination of said current patient and determining said selected protocol using both said feature of said current patient and said current diagnosis.

12. A method as claimed in claim 10 comprising allowing modification of said selected protocol parameter set.

13. A method as claimed in claim 12 comprising modifying said selected protocol parameter set by establishing parameter values of dynamic protocol parameters contained in the selected protocol parameter set.

14. A method as claimed in claim 12 comprising allowing modification of said selected protocol parameter set via a user interface of said computerized control unit.

15. A method as claimed in claim 12 comprising allowing modification of the selected protocol parameter set using said patterns stored in said knowledge base.

16. A method as claimed in claim 10 comprising determining said selected protocol parameter set using a data-driven learning method selected from the group consisting of case-based reasoning and locally weighted regressions.

17. A method as claimed in claim 10 comprising updating said selected protocol parameter set by, after selecting said selected protocol parameter set, providing said selected protocol parameter set to said computerized processor as part of said training data, and re-implementing said data-driving learning method to generate an updated, selected protocol parameter set/

18. A method as claimed in claim 10 comprising embodying said computerized processor in said computerized control unit.

19. A medical imaging device for examining a patient, comprising:

an imaging apparatus configured to operate according to a protocol provided thereto to acquire medical image data from a patient;

a computerized processor provided with training data that comprise multiple variants of protocols as protocol parameter sets for operation of the imaging apparatus, and patient-specific parameter sets each representing at least one feature of respectively different patients and, in said training data, said protocol parameter sets being respectively associated with said patient-specific parameter sets by means of each protocol parameter set being provided for operating the imaging apparatus for examination of a patient having said at least one feature of the patient-specific parameter set associated therewith;

said computerized processor being configured to automatically use said training data to generate relations between said multiple protocol parameter sets and said patient-specific parameter sets by implementing a data-driven learning method that produces a plurality of patterns that are stored in a knowledge base; and

said imaging apparatus comprising a computerized control unit provided with a feature of a current patient to be examined with the imaging apparatus, said control unit being configured to access the patterns stored in said knowledge base to determine a protocol, as a selected protocol, from among said multiple variants of protocols, that is appropriate for operating the imaging apparatus to examine said current patient, and to use the selected protocol to configure the imaging apparatus to implement the examination of the current patient.

20. A medical imaging device as claimed in claim 19 wherein said computerized processor is embodied in said computerized control unit.

21. A non-transitory, computer-readable storage medium encoded with programming instructions, said storage medium being loaded into a computerized processor, and said programming instructions causing said computerized processor to configure a medical imaging device for examination of a patient, by:

receiving training data that comprise multiple variants of protocols as protocol parameter sets for operation of an imaging device, and patient-specific parameter sets each representing at least one feature of respectively different patients and, in said training data, said protocol parameter sets being respectively associated with said patient-specific parameter sets by means of each protocol parameter set being provided for operating the imaging device for examination of a patient having said at least one feature of the patient-specific parameter set associated therewith;

using said training data to generate relations between said multiple protocol parameter sets and said patient-specific parameter sets by implementing a data-driven learning method that produces a plurality of patterns that are stored in a knowledge base; and

from the imaging device, receiving a feature of a current patient to be examined with the imaging device and accessing the patterns stored in said knowledge base to determine a protocol, as a selected protocol, from among said multiple variants of protocols, that is appropriate for operating the imaging device to examine said current patient, and making the selected protocol available at an output of the processor in a form for configuring the imaging device to implement the examination of the current patient.