Methods and systems for parametric noise modulation in x-ray imaging

Patent metadata

Field	Value
Publication number	US-2021158486-A1
Application number	US-202016941081-A
Country	US
Kind code	A1
Filing date	Jul 28, 2020
Priority date	Nov 27, 2019
Publication date	May 27, 2021
Grant date	—

Abstract

Various methods and systems are provided for x-ray imaging. In one embodiment, a method comprises acquiring an image of a subject, generating, based on the image and a plurality of parameters, a noise modulation map comprising an estimated amount of noise in each pixel of the image, selectively reducing noise in the image based on the noise modulation map to generate a final image, and displaying the final image. In this way, the radiation dose during imaging may be reduced while maintaining or even improving image quality.

First claim

1 . A method, comprising:
acquiring an image of a subject; generating, based on the image and a plurality of parameters, a noise modulation map comprising an estimated amount of noise in each pixel of the image; selectively reducing noise in the image based on the noise modulation map to generate a final image; and displaying the final image.

2 . The method of claim 1 , wherein generating the noise modulation map comprises inputting the image and the plurality of parameters to a noise modulation model that outputs the noise modulation map.

3 . The method of claim 2 , wherein the noise modulation model comprises a trained deep learning model.

4 . The method of claim 2 , wherein the noise modulation model comprises a linear or non-linear regression model.

5 . The method of claim 2 , wherein the plurality of parameters includes at least two parameters selected from acquisition parameters relating to the acquisition of the image, display parameters relating to the display of the final image, and subject parameters relating to the subject being imaged.

6 . The method of claim 5 , wherein the acquisition parameters include one or more of a tube voltage applied to an x-ray source during image acquisition, a tube current applied to the x-ray source during image acquisition, a focal spot size, a distance between the x-ray source and an x-ray detector, an amount of spectral filtration, a size of an anti-scatter grid disposed in front of the x-ray detector, and a detector pixel of the x-ray detector.

7 . The method of claim 5 , wherein the display parameters include one or more of a type of display device for displaying the final image, an image processing technique, and a field-of-view.

8 . The method of claim 5 , wherein the subject parameters include a thickness of the subject.

9 . The method of claim 1 , further comprising applying noise reduction to the image to generate a smoothed image, and subtracting the smoothed image from the image to generate a noise image, wherein selectively reducing noise in the image based on the noise modulation map to generate the final image comprises blending the noise image with the smoothed image according to the noise modulation map.

10 . A method, comprising:
controlling an x-ray source and an x-ray detector to acquire an image of a subject; measuring, based on the image and a plurality of parameters, an estimated amount of noise in each pixel of the image; applying noise reduction throughout the image to generate a smoothed image; subtracting the smoothed image from the image to generate a noise image; multiplying the estimated amount of noise in each pixel of the image with the noise image to generate a modulated noise image; adding the modulated noise image to the smoothed image to generate a final image; and displaying the final image.

11 . The method of claim 10 , wherein measuring the estimated amount of noise in each pixel of the image comprises inputting the image and the plurality of parameters to a noise modulation model that outputs the estimated amount of noise in each pixel of the image.

12 . The method of claim 11 , wherein the estimated amount of noise in each pixel of the image comprises an estimate of signal-to-noise ratio determined for each pixel based on the image and the plurality of parameters.

13 . The method of claim 11 , wherein the noise modulation model comprises one of a trained deep learning model, a linear regression model, or a non-linear regression model.

14 . The method of claim 10 , wherein the plurality of parameters includes a peak tube kilovoltage applied to the x-ray source during acquisition of the image, and a size of detector pixels of the x-ray detector.

15 . The method of claim 14 , wherein the plurality of parameters further includes one or more of a tube current applied to the x-ray source during image acquisition, a focal spot size, a distance between the x-ray source and the x-ray detector, an amount of spectral filtration, a size of an anti-scatter grid disposed in front of the x-ray detector, a type of display device for displaying the final image, an image processing technique, a field-of-view for displaying the final image, and a thickness of the subject.

16 . A system, comprising:
an x-ray source for generating x-rays; an x-ray detector configured to detect the x-rays; a display device; and a processor configured with instructions in non-transitory memory that when executed cause the processor to:
acquire an image of a subject;
generate, based on the image and a plurality of parameters, a noise modulation map comprising an estimated amount of noise in each pixel of the image;
selectively reduce noise in the image based on the noise modulation map to generate a final image; and
display, via the display device, the final image.

17 . The system of claim 16 , wherein, to generate the noise modulation map, the processor is configured with instructions in the non-transitory memory that when executed cause the processor to input the image and the plurality of parameters to a noise modulation model that outputs the noise modulation map.

18 . The system of claim 17 , wherein the noise modulation model comprises one or more of a trained deep learning model, a linear regression model, and a non-linear regression model.

19 . The system of claim 16 , wherein the plurality of parameters includes two or more of a peak tube kilovoltage applied to the x-ray source during image acquisition, a tube current applied to the x-ray source during image acquisition, a focal spot size, a distance between the x-ray source and the x-ray detector, an amount of spectral filtration, a size of an anti-scatter grid disposed in front of the x-ray detector, a size of detector pixels in the x-ray detector, a type of the display device for displaying the final image, an image processing technique for processing the image, a field-of-view for displaying the final image, and a thickness of the subject.

20 . The system of claim 16 , wherein the processor is further configured with instructions in the non-transitory memory that when executed cause the processor to:
pre-process the image to generate a processed image, the pre-processing including at least a normalization of photon counts; apply noise reduction throughout the processed image to generate a smoothed image; subtract the smoothed image from the processed image to generate a noise image; multiply the noise modulation map with the noise image to generate a modulated noise image; and add the modulated noise image to the smoothed image to generate a final image.

Assignees

• Ge Prec Healthcare Llc

Inventors

• Tohme Michel S
• Vera Gonzalez German Guillermo
• Xue Ping
• Wanek Justin M

Classifications

• A61B6/5217

  extracting a diagnostic or physiological parameter from medical diagnostic data · CPC title

• A61B6/5258 Primary

  involving detection or reduction of artifacts or noise · CPC title

• G06T2207/10081

  Computed x-ray tomography [CT] · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title

• G06T2207/10116

  X-ray image · CPC title