Adaptive C-arm CT Segmentation for TAVI Workflow
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Solution Overview
Problem
C-arm CT systems face challenges in adapting to varying requirements for treatment planning and interventional guidance, where high image quality is needed for accurate device selection in treatment planning but workflow efficiency is crucial during interventions, and existing solutions do not effectively optimize segmentation parameters for different angular ranges.
Innovation Solution
A medical imaging system with a configurator that determines an adapted angular range and segmentation parameters based on the operation mode, allowing for dynamic adjustment of segmentation techniques and image processing functionality, including a segmentator that generates a segmentation model for optimal image segmentation and landmark identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full angular range scan is used for treatment planning, then segmentation accuracy is improved, but acquisition time and workflow efficiency deteriorate
Solution Approach 1:
The system dynamically adjusts the angular range of the C-arm based on the selected operation mode. For treatment planning, a larger angular range is used to capture sufficient anatomical structures for accurate segmentation, while for intra-procedural guidance, a smaller angular range is sufficient, reducing acquisition time. This dynamic adaptation resolves the contradiction by matching the scan parameters to the specific clinical needs.
Solution Approach 2:
The configurator changes key parameters including angular range, segmentation algorithm complexity, and reconstruction settings based on the operation mode. By adjusting these parameters dynamically, the system optimizes both segmentation accuracy and acquisition time for different clinical scenarios, resolving the trade-off between precision and speed.
2Productivity
If a reduced angular range is used for intra-procedural guidance, then workflow efficiency is improved, but segmentation accuracy deteriorates
Solution Approach 1:
The system applies different quality levels of segmentation appropriate to each operation mode. For intra-procedural guidance, a streamlined segmentation process with reduced accuracy requirements is used, while for treatment planning, high-precision segmentation is applied. This local differentiation of quality requirements resolves the contradiction by matching precision to need.
Solution Approach 2:
For intra-procedural guidance, the system performs partial segmentation focusing only on the critical structures needed for immediate guidance, rather than complete high-precision segmentation of all anatomical features. This partial action approach maintains workflow efficiency while providing sufficient accuracy for the specific clinical task.
3Manufacturing precision
If high-resolution segmentation is applied universally, then image quality is improved, but processing time and computational resources worsen
Solution Approach 1:
The configurator dynamically adjusts segmentation parameters such as algorithm complexity, resolution levels, and processing intensity based on the operation mode. For treatment planning, high-resolution segmentation with complex algorithms is applied, while for intra-procedural guidance, lower-resolution faster processing is used. This parameter adaptation resolves the contradiction between image quality and processing time.
Solution Approach 2:
The segmentation processing dynamically adapts its computational intensity and resolution based on real-time identification of the operation mode. This dynamic adjustment ensures high image quality when needed for planning while enabling rapid processing during interventions, resolving the trade-off between quality and speed.
4Device complexity
If a fixed segmentation approach is used, then system complexity is reduced, but adaptability to different operation modes deteriorates
Solution Approach 1:
The configurator implements a universal control mechanism that automatically identifies the operation mode and selects appropriate segmentation parameters and algorithms. This single multi-functional configurator handles both treatment planning and intra-procedural guidance scenarios, providing adaptability without requiring separate dedicated systems for each mode, thus resolving the contradiction between simplicity and versatility.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables adaptive segmentation that optimizes image quality and workflow efficiency by dynamically adjusting segmentation parameters and functionality based on the operation mode, improving measurement accuracy and reducing artifacts, especially in TAVI procedures.
Implementation Method 1
U.S. Pat. No. 8,111,894 B2 describes a system and a method for acquiring image data, which can be used in order to perform a scanning of an object under examination. The C-arm CT system as therein described comprises an X-ray tube adapted for generating X-rays and an X-ray detection unit to acquire a set of C-arm CT slices.
Data Source
AI summary
The present invention relates to a system (1) for adaptive segmentation. The system (1) comprises a configurator (10), which is configured to determine an adapted angular range (AR) with respect to an operation mode of the system (1) and which is configured to determine a segmentation parameter (SP) based on the adapted angular range (AR). Further, the system comprises an imaging sensor (20), which is configured to acquire images (I1, . . . , IN) within the adapted angular range (AR). Still further, the system comprises a segmentator (30), which is configured to generate a segmentation model based on the acquired images (I1, . . . , IN) using the determined segmentation parameter (SP).