Systems and methods for image processing

Patent metadata

Field	Value
Publication number	US-12266116-B2
Application number	US-202117643409-A
Country	US
Kind code	B2
Filing date	Dec 8, 2021
Priority date	Dec 9, 2020
Publication date	Apr 1, 2025
Grant date	Apr 1, 2025

Abstract

The present disclosure provides a system and method for image reconstruction. The method may include obtaining training samples, the training samples including at least one sample first image generated based on a first tracer, at least one reference first image each of which corresponds to one of the at least one sample first image and has a higher image quality than the corresponding sample first image, at least one sample second image generated based on a second tracer different from the first tracer, and at least one reference second image each of which corresponds to one of the at least one sample second images and has a higher image quality than the corresponding sample second image; and generating a trained image processing model by training a preliminary model using the training samples.

First claim

What is claimed is:

1. A system, comprising:
at least one storage device storing executable instructions for model training; and
at least one processor in communication with the at least one storage device, wherein when executing the executable instructions, the at least one processor is configured to cause the system to perform operations including:
obtaining training samples, the training samples including at least one sample first image generated based on a first tracer, at least one reference first image each of which corresponds to one of the at least one sample first image and has a higher image quality than the corresponding sample first image, at least one sample second image generated based on a second tracer different from the first tracer, and at least one reference second image each of which corresponds to one of the at least one sample second images and has a higher image quality than the corresponding sample second image; and
generating a trained image processing model by training a preliminary model using the training samples.

2. The system of claim 1 , wherein obtaining the training samples includes:
obtaining one of the at least one reference first image generated based on first imaging data acquired by an imaging device according to the first tracer;
generating the corresponding sample first image by down-sampling the first imaging data;
obtaining one of the at least one reference second image generated based on second imaging data acquired by the imaging device according to the second tracer; and
generating the corresponding sample second image by down-sampling the second imaging data.

3. The system of claim 1 , wherein a first count of the at least one sample first image or the at least one reference first image is greater than a second count of the at least one sample second image or the at least one reference second image.

4. The system of claim 1 , wherein generating the trained image processing model by training the preliminary model using the training samples includes:
for each of the at least one sample first image, obtaining a first preliminary weight;
for each of the at least one sample second image, obtaining a second preliminary weight;
determining a loss function based on the at least one first preliminary weight and the at least one second preliminary weight; and
generating the trained image processing model by training the preliminary model based on the loss function.

5. The system of claim 4 , wherein the loss function includes a first term at least related to the at least one first preliminary weight and a second term at least related to the at least one second preliminary weight.

6. The system of claim 4 , wherein the at least one second preliminary weight is greater than the at least one first preliminary weight.

7. The system of claim 4 , wherein the training process includes a plurality of iterations, at least one iteration of the plurality of iterations including:
determining at least one first prediction output by inputting the at least one sample first image into a first intermediate model, the first intermediate model being the preliminary model in the first iteration of the plurality of iterations, or an updated model generated in a previous iteration;
determining at least one second prediction output by inputting the at least one sample second image into the first intermediate model;
obtaining at least one first current weight corresponding to the at least one sample first image, and at least one second current weight corresponding to the at least one sample second image, the at least one first current weight being the at least one first preliminary weight in the first iteration of the plurality of iterations, or at least one updated weight corresponding to the at least one first preliminary weight in the previous iteration, the at least one second current weight being the at least one second preliminary weight in the first iteration of the plurality of iterations, or at least one updated weight corresponding to the at least one second preliminary weight in the previous iteration;
determining a value of the loss function based on the at least one first current weight, the at least one second current weight, the at least one first prediction output, the at least one reference first image, the at least one second prediction output, and the at least one reference second image; and
updating at least one parameter of the first intermediate model based on the value of the loss function.

8. The system of claim 7 , wherein the at least one iteration of the plurality of iterations further includes:
determining a current image difference between the at least one reference second image and the at least one second prediction output; and
updating the at least one first current weight and the at least one second current weight based on the current image difference.

9. The system of claim 8 , wherein determining the current image difference between the at least one reference second image and the at least one second prediction output includes:
determining pixel differences between a plurality of pixels of each of the at least one reference second image and a plurality of pixels of each of the at least one second prediction output; and
determining the current image difference based on the pixel differences and the at least one second current weight.

10. The system of claim 8 , wherein updating the at least one first current weight and the at least one second current weight based on the current image difference includes:
obtaining an initial factor;
determining a target factor by comparing the current image difference of the current iteration and a previous image difference that corresponds to the current image difference and is determined in the previous iteration; and
updating the at least one first current weight and the at least one second current weight based on the comparison.

11. The system of claim 10 , wherein updating the at least one first current weight and the at least one second current weight based on the comparison includes:
in response to determining that the current image difference is greater than the previous image difference,
decreasing the at least one first current weight; and
increasing the at least one second current weight.

12. The system of claim 10 , wherein updating the at least one first current weight and the at least one second current weight based on the comparison includes:
in response to determining that the current image difference is less than the previous image difference,
increasing the at least one first current weight; and
decreasing the at least one second current weight.

13. The system of claim 1 , wherein generating the trained image processing model by training the preliminary model using the training samples includes:
generating a second intermediate model by training the preliminary model using the at least one sample first image and the at least one reference first image; and
generating the trained image processing model by training the second intermediate model using the at least one sample second image and the at least one reference second image.

14. The system of claim 13 , wherein generating the trained image processing model by training the second intermediate model using the at least one sample second image and the at least one reference second image includes:
generating the trained image processing model by iteratively updating parameter values of preset layers in the second intermediate model based on the at least one sample second image and the at least one reference second image.

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Assignees

• Shanghai United Imaging Healthcare Co Ltd

Inventors

• Lyu Yang
• Xi Chen

Classifications

• A61B6/5235

  combining images from the same or different ionising radiation imaging techniques, e.g. PET and CT · CPC title

• A61B6/5205

  involving processing of raw data to produce diagnostic data · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/10104

  Positron emission tomography [PET] · CPC title