ROI-Based Image Selection for Motion Correction
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Solution Overview
Problem
Existing medical imaging technologies face challenges in correcting for patient movement during dynamic imaging procedures, leading to degraded image quality and reduced clinical accuracy, particularly in MRI and tomosynthesis, as current motion correction methods fail to account for individual patient movements and may require excessive correction, affecting the diagnosis of small lesions.
Innovation Solution
A method and system for selecting a reference image based on analyzing the locations of regions of interest (ROIs) across multiple images, determining aggregate motion scores, and performing motion correction using the image with the lowest score to minimize the required correction, thereby improving image quality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion correction is performed using conventional methods, then patient movement during dynamic imaging is addressed, but image quality degrades due to excessive correction and loss of clinical accuracy
Solution Approach 1:
The patent changes the reference parameter from a fixed first acquired image to a dynamically selected reference image based on motion characteristics. By calculating motion scores for each image and selecting the one with the lowest score as reference, the system adapts to actual patient movement patterns, correcting motion artifacts while preserving diagnostic quality and avoiding excessive correction that degrades image quality.
2Reliability
If motion correction is applied to all images, then patient movement is compensated, but small lesions are affected and diagnostic accuracy reduces
Solution Approach 1:
The patent applies motion correction selectively based on local motion characteristics of different images. By calculating individual motion scores for each image and applying correction only where necessary (using the image with lowest motion score as reference), the system preserves local diagnostic quality in regions with minimal motion while still compensating for movement in other areas, thus maintaining small lesion detectability.
3Ease of operation
If a fixed reference image is used for motion correction, then the correction process is simple, but it does not account for individual patient movements varying over time
Solution Approach 1:
The patent transforms the static reference image selection into a dynamic process. Instead of fixing the first image as reference, the system calculates motion scores for all images and dynamically selects the most appropriate reference image based on actual patient movement patterns. This adaptive approach maintains operational simplicity through automated scoring while achieving versatility in accommodating individual patient movements.
Data Source
AI summary
Methods and systems for performing motion correction on imaging data. The methods include accessing a set of imaging data. The set of imaging data includes first imaging data for a first time point, second imaging data for a second time point, and third imaging data for a third time point. A location of a region of interest (ROI) is identified in the different imaging data. Differences between the locations of ROI across the imaging data are determined and aggregated to generate an aggregate motion score for the respective imaging data in the set of imaging data. One of the imaging data is then selected as reference imaging data for motion correction based on the aggregate motion score. Motion correction of the set of imaging data is performed based on the selected reference imaging data. Similar comparisons on images may be performed for peak enhancement of MRI imaging data.