Medical Image Correction Using Quality Feedback for Motion Artifacts
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Solution Overview
Problem
Medical imaging technologies such as MRI, MRA, and CT often suffer from poor image quality due to factors like uneven magnetic fields, coil sensitivity, contrast agent display, and patient motion, leading to motion artifacts that complicate diagnosis.
Innovation Solution
A system and method for image correction that includes obtaining raw data, determining image quality using a preset evaluation tool, and applying a correction algorithm to generate corrected images, reducing human intervention and improving diagnostic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image quality evaluation and correction is implemented, then diagnostic efficiency is improved, but device complexity increases
Solution Approach 1:
The system performs self-evaluation of image quality using automated evaluation tools and self-correction through algorithms that automatically process and rectify artifacts without requiring manual intervention, thereby improving diagnostic efficiency while managing complexity through autonomous operation
Solution Approach 2:
The system incorporates feedback mechanisms where image quality is continuously evaluated and correction algorithms are applied based on evaluation results, creating a closed-loop process that automatically improves image quality and streamlines the diagnostic workflow
2Reliability
If manual image quality assessment is used, then device complexity is reduced, but diagnostic accuracy deteriorates due to human error and fatigue
Solution Approach 1:
The system replaces manual human assessment with automated electronic evaluation tools and algorithms that objectively measure image quality parameters, eliminating human error and fatigue while maintaining consistency and accuracy in diagnostic assessments
3Manufacturing precision
If image correction algorithms are applied to all images, then image quality is improved, but processing time increases
Solution Approach 1:
The system performs preliminary evaluation of image quality before correction is applied, using automated tools to assess whether correction is needed and to determine the appropriate correction algorithm, thereby avoiding unnecessary processing time on images that already meet quality standards
Solution Approach 2:
The system dynamically adjusts the correction process based on real-time image quality assessment, applying correction algorithms selectively and adaptively to only those images that require it, thereby optimizing processing time while maintaining high image quality standards
Data Source
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AI summary
Systems and methods for image correction are provided. The systems and methods may obtain raw data of a target object. The systems and methods may determine, based on the raw data of the target object, a target phase. The systems and methods may generate a first image corresponding to the target phase. The systems and methods may determine, using a preset evaluation tool, an image quality evaluation result of the first image. In response to determining that the image quality evaluation result of the first image does not satisfy a preset condition, the systems and methods may generate a set of corrected raw sub-data of the target object corresponding to the target phase by correcting a set of raw sub-data of the target object corresponding to the target phase. The systems and methods may generate, based on the set of corrected raw sub-data of the target object, a corrected image corresponding to the first image.