Systems and methods for reducing colored noise in medical images using deep neural network

Abstract

Methods and systems are provided for de-noising medical images using deep neural networks. In one embodiment, a method comprises receiving a medical image acquired by an imaging system, the medical image comprising colored noise; mapping the medical image to a de-noised medical image using a trained convolutional neural network (CNN); and displaying the de-noised medical image via a display device. The deep neural network may thereby reduce colored noise in the acquired noisy medical image, increasing a clarity and diagnostic quality of the image.

Description

technical field [0001] Embodiments of the subject matter disclosed herein relate to processing medical images, such as magnetic resonance images (MRI), CT images, etc., and more particularly to reducing noise in medical images using deep neural networks. Background technique [0002] Medical imaging systems are commonly used to obtain anatomical and / or internal physiological information of a subject, such as a patient. For example, medical imaging systems may be used to obtain medical images of a patient's skeletal structure, brain, heart, lungs, and various other features. A medical image may be an image generated by a medical imaging system. Medical imaging systems may include magnetic resonance imaging (MRI) systems, computed tomography (CT) systems, x-ray systems, ultrasound systems, and various other imaging modalities. [0003] Medical images obtained by certain imaging modalities, such as MRI, may contain one or more types of noise, which can reduce image clarity an...

AI Technical Summary

Problems solved by technology

[0003] Medical images obtained with certain imaging modalities, such as MRI, can contain one or more types of noise, which can reduce image clarity and resolution

Presence of noise in medical images can affect diagnostic quality

Specifically, k-space sampling patterns, image reconstruction, and postprocessing can produce medical images with colored noise (e.g., noise that is not uniformly distributed in the spatial frequency domain) in magnetic resonance (MR) images, which can be difficult to Reduced by existing image processing methods

Deep learning methods have been proposed for removing colored noise from medical images, however current deep learning methods perform inconsistently in removing colored noise and often fail to produce a sufficient degree of noise reduction

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