AI-Driven Breast Compression Adjustment in Mammography
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Solution Overview
Problem
Conventional mammography techniques often result in sub-optimum breast compression, leading to sub-optimal image quality and discomfort for patients due to the lack of consideration for individual breast tissue density and area, which are not adequately accounted for in existing methods.
Innovation Solution
A method using machine learning algorithms to determine a patient-adjusted breast compression point by analyzing individual patient data, including tissue density and breast area, to achieve both minimum image quality and patient comfort standards, implemented through a breast compression determining device and training system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional compression parameters (compression force and breast thickness) are used, then the examination process is simple, but the image quality is sub-optimal and patient comfort is compromised
Solution Approach 1:
The invention changes the parameters used for compression determination from simple mechanical parameters (compression force, breast thickness) to include tissue density and breast area parameters. This allows the system to determine optimal compression values by considering multiple parameters simultaneously, thereby improving image quality while maintaining an acceptable level of system complexity through automated calculation.
Solution Approach 2:
The invention introduces a compression determination device as an intermediary between the mammography system and the operator. This device automatically calculates optimal compression parameters based on input data (tissue density, breast area) and provides recommendations, reducing the complexity burden on the operator while improving image quality through precise parameter optimization.
2Adaptability or versatility
If individual patient parameters (tissue density, breast area) are considered, then patient-adjusted compression is achieved, but the system becomes under-determined without additional parameters
Solution Approach 1:
The invention adds tissue density and breast area as new parameters to the compression determination system. These parameters provide the additional information needed to make the system determined, allowing reliable calculation of optimal compression values that are adapted to individual patient characteristics while maintaining mathematical solvability.
3Productivity
If sub-optimum compression is applied, then the examination process is faster and simpler, but image quality deteriorates and patient discomfort increases
Solution Approach 1:
The invention performs preliminary determination of optimal compression parameters before the actual compression is applied. By calculating the optimal compression values in advance based on patient-specific parameters (tissue density, breast area), the system ensures that the correct compression is applied from the start, avoiding the need for iterative adjustments and ensuring both patient comfort and image quality without compromising examination efficiency.
Data Source
AI summary
A method is for determining a patient-adjusted breast compression in mammography. In an embodiment of the method, input data including individual, person-related data of a female patient, is determined. Furthermore, an adjusted individual compression point is determined by applying a function, trained by an algorithm based on machine learning, to the input data. The adjusted individual compression point is generated as the output data. Other embodiments include a method for providing a trained function; a breast compression determining device; a training device; and a mammography system.