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Systems and methods of using self-attention deep learning for image enhancement

A deep learning network and medical image technology, applied in the field of image enhancement systems and methods using self-focused deep learning, can solve problems such as small lesions that are difficult to analyze, reduce imaging artifacts, eliminate noise, and speed up PET scanning time Effect

Active Publication Date: 2021-05-07
长沙微妙医疗科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, small lesions are difficult to resolve when FLAIR sequences are accelerated at shorter scan times (similar to the faster scans of PET)

Method used

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  • Systems and methods of using self-attention deep learning for image enhancement
  • Systems and methods of using self-attention deep learning for image enhancement
  • Systems and methods of using self-attention deep learning for image enhancement

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Embodiment Construction

[0028] While various embodiments of the invention have been shown and described herein, it will be readily understood by those skilled in the art that these embodiments are provided by way of example only. Numerous variations, changes and substitutions will occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[0029] The present disclosure provides systems and methods capable of improving medical image quality. Specifically, the provided systems and methods can employ a self-attention mechanism and an adaptive deep learning framework that can significantly improve image quality.

[0030] The provided systems and methods can improve image quality in various aspects. Examples of low quality in medical imaging may include noise (e.g., low signal-to-noise ratio), blurring (e.g., motion artifacts), shadowing (e.g., sensed blockage or interfer...

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Abstract

A computer-implemented method is provided for improving image quality. The method comprises: acquiring, using a medical imaging apparatus, a medical image of a subject, wherein the medical image is acquired with shortened scanning time or reduced amount of tracer dose; applying a deep learning network model to the medical image to generate one or more feature attention maps a medical image of the subject with improved image quality for analysis by a physician.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Application No. 62 / 908,814, filed October 1, 2019, the contents of which are hereby incorporated in their entirety. Background technique [0003] Medical imaging plays a vital role in healthcare. For example, multiple imaging modalities such as positron emission tomography (PET), magnetic resonance imaging (MRI), ultrasound imaging, X-ray imaging, computed tomography (CT), or a combination of these can help with prevention, early detection, early Diagnose and treat diseases and syndromes. Due to various factors such as physical limitations of electronics, dynamic range limitations, noise from the environment, and motion artifacts due to patient motion during imaging, image quality may degrade and images may be contaminated by noise. [0004] Efforts are ongoing to improve image quality and reduce various types of noise, such as aliasing noise and various artifacts, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B01J31/22
CPCG06V10/44G06V10/774G06V10/25G06T7/11G06V10/82G06N3/0464G06T2207/20104G06T2207/20081G06N3/088G06N3/048G06N3/044G06N3/045G06T2207/10072G06T2207/20084G06T2207/20092G06N3/042G06T5/00G06V10/771G06T3/4053G06T7/0012G06T2207/10088G06T2207/10104G06T2207/30168
Inventor 项磊王泷张涛宫恩浩
Owner 长沙微妙医疗科技有限公司