Medical Imaging Positioning Guidance for Consistent Follow-Up Alignment
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Solution Overview
Problem
Existing medical imaging techniques face challenges in maintaining consistent alignment between initial and follow-up images, leading to misinterpretations and incorrect diagnoses due to varying alignments during image acquisition.
Innovation Solution
A computer-implemented method for positioning a subject in medical imaging that determines first positioning data from a first image, derives guidance data for aligning the region of interest to a second image acquisition unit, and provides guidance for achieving a target alignment similar to the first image, using techniques like image analysis and 3D modeling to ensure consistent image acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual positioning methods are used for follow-up imaging, then operational flexibility is maintained, but alignment consistency between initial and follow-up images deteriorates
Solution Approach 1:
The system performs preliminary analysis of the initial image to extract positioning data and generate guidance information before the follow-up imaging procedure. This preliminary action establishes the target alignment parameters that guide the subsequent imaging process, ensuring consistency without requiring complex manual positioning operations.
Solution Approach 2:
The system introduces an intermediary computational layer that processes initial image data and generates guidance information. This intermediary translates the complex task of maintaining alignment consistency into simplified guidance that assists operators while preserving operational flexibility.
2Reliability
If alignment guidance systems are implemented, then image comparability is improved, but system complexity increases
Solution Approach 1:
The system is designed to be universally applicable across different imaging scenarios and facilities. By extracting general positioning data from initial images and generating universal guidance information, the system achieves high image comparability without requiring facility-specific complex infrastructure.
Solution Approach 2:
The system performs self-positioning analysis by automatically extracting alignment parameters from the initial image itself. This self-service capability eliminates the need for external complex positioning equipment, reducing system complexity while maintaining reliability.
3Duration of action of moving object
If multiple imaging sessions are conducted without alignment guidance, then patient monitoring over time is achieved, but diagnostic accuracy deteriorates due to alignment variations
Solution Approach 1:
The system establishes a feedback loop where initial image positioning data informs follow-up imaging alignment. The guidance information derived from the initial image provides feedback to operators during subsequent imaging sessions, ensuring that alignment is maintained across the entire patient monitoring period, thereby preserving diagnostic accuracy.
Data Source
AI summary
A computer-implemented method for positioning a subject in medical imaging, comprising: receiving a first image (20) of a region of interest (14, 16) of the subject (S10); determining first positioning data based on the first image (20), wherein the first positioning data indicates an alignment of the region of interest (14, 16) relative to a first image acquisition unit used to acquire the first image (20) (S20); determining guidance data based on the first positioning data, wherein the guidance data comprises a guidance for an alignment of the region of interest (14, 16) relative to a second image acquisition unit used to acquire a second image (60) from a current alignment to a target alignment, wherein the target alignment is to correspond to that derived from the first positioning data (S30); providing the guidance data for acquiring the second image (60) (S40).