US 12,564,366 B2 · Systems and methods for image denoising via adversarial learning

Abstract

Various examples are provided related to reconstructing images such as, e.g., medical images from low-dose image scans. Adversarial learning such as, e.g., a Cyclic Simulation and Denoising (CSD) framework can be used to address challenges of complicated mixed noise in real low-dose scans. The CSD framework can include a simulator model that can extract low-dose noise and features (e.g., tissue features) from separate image spaces into a unified feature space and a denoiser model that can learn how to remove noise and restore features, simultaneously. Both the simulator model and the denoiser model can regularize each other in a cyclic manner to optimize network learning effectively. The CSD framework in combination with phantom scans can embrace the realistic low-dose noise and features into a unified learning environment to address the challenge of real low-dose image restoration.

Claims11

Dependent claims

An adversarial machine learning and denoising system, comprising:

a memory device storing a simulator model and a denoiser model;
and
a processor configured to perform the simulator model and the denoiser model, wherein during performance of a simulation-to-denoising (S2D) training cycle of the simulator model and the denoiser model, the simulator model receives as input a low-dose noisy phantom image scan and a high-dose patient image scan and uses the low-dose noisy phantom image scan and the high-dose patient image scan to generate a simulated low-dose noisy patient image scan, and the denoiser model receives the simulated low-dose noisy patient image scan output from the simulator model and uses the simulated low-dose noisy patient image scan to train the denoiser model to remove noise from a real low-dose noisy patient image scan, where low-dose image scans are performed at about 95 mAs or less and high-dose image scans are performed above the low-dose image scans.

An adversarial learning and denoising method, comprising:

during performance of a simulation-to-denoising (S2D) training cycle, receiving as input in a simulator model a low-dose noisy phantom image scan and a high-dose patient image scan and outputting a simulated low-dose noisy patient image scan, where low-dose image scans are performed at about 95 mAs or less and high-dose image scans are performed above the low-dose image scans;
and
during performance of the S2D training cycle, receiving the simulated low-dose noisy patient image scan output from the simulator model in a denoiser model and using the simulated low-dose noisy patient image scan in the denoiser model to train the denoiser model to remove noise from a real low-dose noisy patient image scan.

Dependencies are inferred from the claim text.

Systems and methods for image denoising via adversarial learning

Life of this patent

Systems and methods for image denoising via adversarial learning

Life of this patent

Abstract

Claims11