Automated MRI Phantom Analysis System
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Solution Overview
Problem
Current MRI system quality control processes are time-consuming and prone to human error due to the manual calculation and analysis of image quality metrics, particularly in low contrast detectability tests, which require subjective human interpretation.
Innovation Solution
An automated system using fuzzy logic algorithms to analyze MRI images of quality-control phantoms, identifying seed pixels and ranking combinations to generate an indication of imaging quality characteristics, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculation and analysis of image quality metrics is used, then subjective human interpretation can be applied, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical process of visual inspection and calculation with an automated computer-based image analysis system. The system uses algorithms to automatically detect, segment, and measure image quality metrics such as low-contrast detectability, replacing human observers and eliminating subjective interpretation while significantly reducing analysis time.
Solution Approach 2:
The image analysis system performs self-assessment of quality control metrics without requiring human intervention. The automated algorithms independently evaluate image quality parameters, generate reports, and determine compliance status, enabling the system to serve itself in the quality control process.
2Adaptability or versatility
If multiple phantoms with different structures and materials are used to test many characteristics, then comprehensive device evaluation is achieved, but the complexity of the QC protocol increases
Solution Approach 1:
The patent creates a universal image analysis system that can handle multiple phantom types, structures, and materials through a single automated platform. The system uses configurable algorithms that adapt to different QC protocol requirements, eliminating the need for separate manual analysis procedures for each phantom type and reducing overall protocol complexity.
Solution Approach 2:
The system changes analytical parameters and algorithms based on the specific phantom being tested. By automatically detecting phantom type and adjusting analysis parameters accordingly, the system maintains comprehensive evaluation capability while simplifying the user interface and reducing the complexity of managing multiple specialized procedures.
3Productivity
If automated image analysis is implemented, then time consumption and human error are reduced, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between the imaging system and the analysis algorithms. This includes standardized data structures, automated phantom detection routines, and configurable parameter sets that bridge the gap between raw images and quality metrics. This intermediary layer manages system complexity internally while presenting a simple interface to users.
Solution Approach 2:
The image analysis system is segmented into modular functional components: image preprocessing module, phantom detection module, feature extraction module, metric calculation module, and reporting module. Each module handles a specific aspect of the analysis independently, making the overall complex system manageable through clear separation of concerns and enabling independent optimization of each component.
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 automated system significantly reduces the burden of performing low contrast resolution detection tests by providing accurate and efficient analysis of MRI system performance, minimizing human error and enabling regular quality control assessments.
Implementation Method 1
when a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field
Implementation Method 2
a plurality of gradient coils configured to apply a gradient field to the polarizing magnetic field
Implementation Method 3
the individual magnetic moments of the excited nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency
Implementation Method 4
If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and operating near the Larmor frequency, the net aligned moment, Mz, may be rotated, or 'tipped' into the x-y plane
Implementation Method 5
A signal is emitted by the excited nuclei or 'spins' after the excitation signal B1 is terminated, and this signal may be received and processed to form an image
Data Source
AI summary
A system and method for assessing the operation of a imaging system, such as magnetic resonance imaging (MRI) system, is disclosed including a that computer is programmed to access an image of a phantom from image data, identify a plurality of seed point in the image of the phantom using a shape recognition algorithm, and rank combinations of the seed points using a pattern recognition algorithm using a priori information about the predefined pattern. The computer is programmed to rank the combinations of the seed points to generate an indication of an imaging quality characteristic of the imaging system.