DSA Mask Image Selection for Patient Movement Artifacts
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Solution Overview
Problem
Digital subtraction angiography (DSA) in 2D suffers from movement artifacts due to patient movement between mask and fill image recordings, which existing methods inadequately compensate, especially when complex movement patterns involve overlapping organs with different movement patterns.
Innovation Solution
A method that automatically determines the suitability of mask images for different movement states during recording, using a comparison algorithm to identify and record additional mask images that match the movement states of fill images, thereby reducing movement artifacts and optimizing image quality through an automated workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single mask image is recorded and used for all fill images, then the recording process is simple and fast, but movement artifacts appear in result images when patient movement occurs between mask and fill image recordings
Solution Approach 1:
The system dynamically adapts the number of mask images recorded based on detected movement. When movement is detected between mask and fill images, the system automatically records additional mask images to match the movement state, transforming a static single-mask approach into a dynamic multi-mask approach that responds to actual patient movement conditions
Solution Approach 2:
The system uses automatic comparison algorithms to detect movement artifacts between mask and fill images, providing feedback that triggers the recording of additional mask images. This closed-loop feedback mechanism ensures that mask images are recorded only when necessary, optimizing both recording efficiency and image quality
2Reliability
If multiple mask images are recorded to cover different movement states, then movement artifacts are reduced, but the recording time and complexity increase
Solution Approach 1:
The system performs preliminary comparison between the initially recorded mask image and fill images to detect movement artifacts before final image processing. This preliminary detection allows selective recording of additional mask images only when movement is detected, avoiding unnecessary extended recording time while ensuring image quality when needed
Solution Approach 2:
The system changes the parameter of mask image quantity from fixed (one or multiple) to variable based on movement detection. The comparison algorithm evaluates movement magnitude and automatically adjusts the number of mask images recorded, optimizing the balance between recording time and image quality
3Reliability
If manual allocation of mask images to fill images is performed, then suitable mask images can be selected for each fill image, but the process becomes very time-consuming
Solution Approach 1:
The system replaces the manual mechanical process of image allocation with an automated computer-based comparison algorithm. The algorithm automatically compares mask images with fill images using image processing techniques to detect movement artifacts and select suitable mask-fill image pairs, eliminating time-consuming manual intervention while maintaining or improving matching accuracy
Solution Approach 2:
The comparison algorithm performs self-service by automatically evaluating movement artifacts between mask and fill images, selecting suitable pairs, and generating result images without requiring manual allocation. The system serves itself by detecting when additional mask images are needed and automatically recording them, reducing overall processing time
Data Source
AI summary
A method for digital subtraction angiography of a recording region of a patient is provided herein. The method includes recording at least one mask image of a recording region without using a contrast medium; recording a plurality of ill images after administration of a contrast medium; and determining result images by subtraction of one of the at least one mask image from respective fill images. As a function of automatically determined or user-provided image quality information describing a suitability of the at least one mask image in respect of different movement states in the recording region during recording of the at least one mask image and in the case of at least some of the fill images, in the case of non-suitability of the at least one mask image for at least one fill image of a non-suitability group, at least one further mask image is recorded and the suitability of the further mask image for the at least one fill image of the non-suitability group is checked automatically by a comparison algorithm in respect of the movement state. Additionally, following the existence of suitable mask images for each fill image of the non-suitability group, the recording of further mask images is terminated.