Neural Network Metastasis Detection Using ADC Maps
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Solution Overview
Problem
Current clinical practices for metastatic characterization in cancer staging, particularly for lymph node and distant metastasis, face challenges such as inaccurate manual annotation, the 'flare' phenomenon, and high variability in lesion characteristics, leading to suboptimal accuracy in N-stage determination and metastasis detection.
Innovation Solution
A computer-implemented method using a deep convolutional neural network with attention gates, trained on apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI measurements, to automate the detection and characterization of metastases, providing spatial context and improving prediction reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for metastasis detection, then flexibility and adaptability are maintained, but accuracy and reliability deteriorate due to human error and variability
Solution Approach 1:
The system enables automated self-annotation through neural networks that automatically detect and characterize metastases in MRI images, eliminating the need for manual annotation by radiologists while maintaining or improving accuracy through consistent algorithmic application
Solution Approach 2:
The patent replaces the manual mechanical annotation process with an automated neural network system that processes MRI images and generates metastasis detections algorithmically, substituting human manual work with computational automation
2Difficulty of detecting and measuring
If contrast-enhanced MRI with diffusion weighted imaging is used, then detection capability is improved, but the 'flare' phenomenon and lesion variability increase measurement precision challenges
Solution Approach 1:
The neural network analyzes multiple MRI parameters including contrast enhancement patterns and diffusion weighted imaging metrics simultaneously, transforming multiple imaging parameters into a unified metastasis characterization that overcomes the limitations of individual parameter variability
Solution Approach 2:
The patent introduces radiomics features as intermediary variables that bridge the gap between raw MRI imaging data and metastasis characterization, extracting quantitative features that serve as reliable intermediaries for accurate detection despite imaging variability
3Reliability
If deep neural networks are trained on multiple MRI parameters including ADC maps, then measurement precision and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The neural network is structured with multiple specialized layers including convolutional layers for feature extraction, attention gates for spatial prioritization, and separate processing streams for different MRI parameters, segmenting the complex task into manageable functional components
Solution Approach 2:
The patent incorporates 3D spatial information and temporal sequence data from multiple MRI sequences into the neural network architecture, adding dimensional depth to the analysis that improves reliability while managing complexity through hierarchical processing
Data Source
AI summary
Various examples of the disclosure pertain to the characterization of one or more metastases using a neural network, as well as an apparent diffusion coefficient, ADC, map as obtained from diffusion-weighted magnetic resonance imaging, DWI MRI. A convolutional neural network can be employed. Training processes and inference processes are disclosed.