Kidney Motion Tracking via Edge-Based Consistency Metric
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Solution Overview
Problem
The registration of dynamic renal perfusion MR images is challenging due to motion induced by breathing, which complicates the correspondence of anatomical structures across time frames, especially with rapidly changing image contrast and non-uniform enhancement of renal tissue types.
Innovation Solution
An integrated image registration algorithm that performs rough registration by detecting large-scale motion using an edge-based consistency metric invariant to contrast changes, followed by fine registration integrating region and edge information with auxiliary image segmentation results, allowing for accurate tracking of kidney motion across the image sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If block matching and intensity comparison are used for registration, then the method is simple to implement, but the registration accuracy deteriorates due to rapidly changing image contrast and non-uniform renal tissue enhancement
Solution Approach 1:
The patent introduces an edge-based consistency metric as an intermediary that is invariant to contrast changes. This metric serves as a mediator between the registration process and the rapidly changing image intensities, allowing accurate registration without being affected by the varying contrast of renal tissues during contrast enhancement
Solution Approach 2:
The patent changes the registration parameter from intensity-based comparison to edge-based consistency measurement. By transforming the registration criterion from one that depends on absolute intensities (which change rapidly) to one that depends on edge relationships (which remain consistent), the method achieves both accuracy and robustness against contrast variations
2Measurement precision
If manual contour drawing is used to initialize registration, then the initial segmentation is accurate, but the process is time-consuming and requires user intervention
Solution Approach 1:
The patent implements self-service by enabling the system to automatically initialize registration using algorithmic contour detection instead of requiring manual user input. The edge-based consistency metric automatically identifies kidney boundaries through the image sequence, making the system self-sufficient and eliminating the need for time-consuming manual contour drawing
Solution Approach 2:
The patent performs preliminary action by automatically detecting and establishing initial contours through edge-based analysis before the main registration process begins. This preliminary automatic segmentation provides accurate initial conditions for registration without requiring subsequent manual adjustment or user intervention
3Productivity
If coarse registration is performed first, then the initial alignment is achieved quickly, but fine registration requires additional processing steps
Solution Approach 1:
The patent applies dynamics by implementing a two-stage registration process that adapts to the specific characteristics of the image data. The method dynamically transitions from coarse registration using overall edge consistency to fine registration using localized edge information, optimizing both speed and accuracy based on the registration progress
Data Source
AI summary
A system and method are provided for integrated registration of images, the system including a processor, a first registration portion for performing rough registration of an image, a first segmentation portion for performing segmentation of an object of interest in the image, a second registration portion for performing fine registration of the image, and a second segmentation portion for performing segmentation of structures of the object of interest in the image; and the method including receiving a sequence of images, selecting an image from the sequence, cropping a region of interest (ROI) from the selected image, performing rough registration of the cropped ROI, performing segmentation of an object of interest from the rough registered ROI, performing fine registration of the ROI, and performing segmentation of structures of the object of interest from the fine registered ROI.