System and method for determining optimal operating parameters for medical imaging

Patent metadata

Field	Value
Publication number	US-2017351937-A1
Application number	US-201615172949-A
Country	US
Kind code	A1
Filing date	Jun 3, 2016
Priority date	Jun 3, 2016
Publication date	Dec 7, 2017
Grant date	—

Abstract

Systems and methods for determining optimized imaging parameters for imaging a patient include learning a model of a relationship between known imaging parameters and a quality measure, the known imaging parameters and the quality measure being determined from training data. Optimized imaging parameters are determined by optimizing the quality measure using the learned model. Images of the patient are acquired using the optimized imaging parameters.

First claim

1 . A method for determining optimized imaging parameters for imaging a patient, comprising:
learning a model of a relationship between known imaging parameters and a quality measure, the known imaging parameters and the quality measure being determined from training data; determining optimized imaging parameters by optimizing the quality measure using the learned model; and acquiring images of the patient using the optimized imaging parameters.

2 . The method as recited in claim 1 , wherein determining optimized imaging parameters by optimizing the quality measure using the learned model comprises maximizing the quality measure.

3 . The method as recited in claim 1 , wherein the quality measure comprises a signal-to-noise ratio (SNR) determined as a ratio between an average intensity value of pixels in a first region of interest in a target structure in an image and an average intensity value of pixels in a second region of interest outside of the target structure in the image.

4 . The method as recited in claim 1 , wherein the quality measure comprises as at least one of a signal-to-noise ratio, a contrast-to-noise ratio, a resolution, a measure of motion artifacts, and a measure of ghosting.

5 . The method as recited in claim 4 , wherein the quality measure is selected based on a procedure being performed.

6 . The method as recited in claim 1 , wherein the known imaging parameters comprise parameters for operating an image acquisition device.

7 . The method as recited in claim 1 , wherein the optimized imaging parameters are constrained to be within a range of the known imaging parameters.

8 . The method as recited in claim 1 , wherein optimizing the quality measure comprises optimizing the quality measure using an interior point algorithm.

9 . The method as recited in claim 1 , wherein learning the model comprises learning the model using a regression analysis.

10 . An apparatus for determining optimized imaging parameters for imaging a patient, comprising:
means for learning a model of a relationship between known imaging parameters and a quality measure, the known imaging parameters and the quality measure being determined from training data; means for determining optimized imaging parameters by optimizing the quality measure using the learned model; and means for acquiring images of the patient using the optimized imaging parameters.

11 . The apparatus as recited in claim 10 , wherein the means for determining optimized imaging parameters by optimizing the quality measure using the learned model comprises means for maximizing the quality measure.

12 . The apparatus as recited in claim 10 , wherein the quality measure comprises a signal-to-noise ratio (SNR) determined as a ratio between an average intensity value of pixels in a first region of interest in a target structure in an image and an average intensity value of pixels in a second region of interest outside of the target structure in the image.

13 . The apparatus as recited in claim 10 , wherein the quality measure comprises as at least one of a signal-to-noise ratio, a contrast-to-noise ratio, a resolution, a measure of motion artifacts, and a measure of ghosting.

14 . The apparatus as recited in claim 13 , wherein the quality measure is selected based on a procedure being performed.

15 . The apparatus as recited in claim 10 , wherein the known imaging parameters comprise parameters for operating an image acquisition device.

16 . The apparatus as recited in claim 10 , wherein the optimized imaging parameters are constrained to be within a range of the known imaging parameters.

17 . The apparatus as recited in claim 10 , wherein the means for optimizing the quality measure comprises means for optimizing the quality measure using an interior point algorithm.

18 . The apparatus as recited in claim 10 , wherein the means for learning the model comprises means for learning the model using a regression analysis.

19 . A non-transitory computer readable medium storing computer program instructions for determining optimized imaging parameters for imaging a patient, the computer program instructions when executed by a processor cause the processor to perform operations comprising:
learning a model of a relationship between known imaging parameters and a quality measure, the known imaging parameters and the quality measure being determined from training data; determining optimized imaging parameters by optimizing the quality measure using the learned model; and acquiring images of the patient using the optimized imaging parameters.

20 . The non-transitory computer readable medium as recited in claim 19 , wherein determining optimized imaging parameters by optimizing the quality measure using the learned model comprises maximizing the quality measure.

21 . The non-transitory computer readable medium as recited in claim 19 , wherein the quality measure comprises a signal-to-noise ratio (SNR) determined as a ratio between an average intensity value of pixels in a first region of interest in a target structure in an image and an average intensity value of pixels in a second region of interest outside of the target structure in the image.

22 . The non-transitory computer readable medium as recited in claim 19 , wherein the quality measure comprises as at least one of a signal-to-noise ratio, a contrast-to-noise ratio, a resolution, a measure of motion artifacts, and a measure of ghosting.

23 . The non-transitory computer readable medium as recited in claim 22 , wherein the quality measure is selected based on a procedure being performed.

24 . The non-transitory computer readable medium as recited in claim 19 , wherein the known imaging parameters comprise parameters for operating an image acquisition device.

25 . The non-transitory computer readable medium as recited in claim 19 , wherein the optimized imaging parameters are constrained to be within a range of the known imaging parameters.

26 . The non-transitory computer readable medium as recited in claim 19 , wherein optimizing the quality measure comprises optimizing the quality measure using an interior point algorithm.

27 . The non-transitory computer readable medium as recited in claim 19 , wherein learning the model comprises learning the model using a regression analysis.

Assignees

• Siemens Healthcare Gmbh

Inventors

• Lu Xiaoguang
• Deshpande Vibhas
• Kollasch Peter
• Wang Dingxin
• Sharma Puneet

Classifications

• G06F18/2193

  based on specific statistical tests · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06T2207/30048

  Heart; Cardiac · CPC title

• G06K9/6265 Primary

  Physics · mapped topic

• G06T7/0014

  using an image reference approach · CPC title